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2,402.13064 | Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for
Language Models | ['Haoran Li', 'Qingxiu Dong', 'Zhengyang Tang', 'Chaojun Wang', 'Xingxing Zhang', 'Haoyang Huang', 'Shaohan Huang', 'Xiaolong Huang', 'Zeqiang Huang', 'Dongdong Zhang', 'Yuxian Gu', 'Xin Cheng', 'Xun Wang', 'Si-Qing Chen', 'Li Dong', 'Wei Lu', 'Zhifang Sui', 'Benyou Wang', 'Wai Lam', 'Furu Wei'] | ['cs.CL'] | We introduce Generalized Instruction Tuning (called GLAN), a general and
scalable method for instruction tuning of Large Language Models (LLMs). Unlike
prior work that relies on seed examples or existing datasets to construct
instruction tuning data, GLAN exclusively utilizes a pre-curated taxonomy of
human knowledge a... | 2024-02-20T15:00:35Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,402.13126 | VGMShield: Mitigating Misuse of Video Generative Models | ['Yan Pang', 'Baicheng Chen', 'Yang Zhang', 'Tianhao Wang'] | ['cs.CR', 'cs.AI', 'cs.CV', 'cs.LG', 'eess.IV'] | With the rapid advancement in video generation, people can conveniently use
video generation models to create videos tailored to their specific desires. As
a result, there are also growing concerns about the potential misuse of video
generation for spreading illegal content and misinformation.
In this work, we introd... | 2024-02-20T16:39:23Z | 18 pages | null | null | null | null | null | null | null | null | null |
2,402.13178 | Benchmarking Retrieval-Augmented Generation for Medicine | ['Guangzhi Xiong', 'Qiao Jin', 'Zhiyong Lu', 'Aidong Zhang'] | ['cs.CL', 'cs.AI'] | While large language models (LLMs) have achieved state-of-the-art performance
on a wide range of medical question answering (QA) tasks, they still face
challenges with hallucinations and outdated knowledge. Retrieval-augmented
generation (RAG) is a promising solution and has been widely adopted. However,
a RAG system c... | 2024-02-20T17:44:06Z | Homepage: https://teddy-xionggz.github.io/benchmark-medical-rag/ | null | null | null | null | null | null | null | null | null |
2,402.13217 | VideoPrism: A Foundational Visual Encoder for Video Understanding | ['Long Zhao', 'Nitesh B. Gundavarapu', 'Liangzhe Yuan', 'Hao Zhou', 'Shen Yan', 'Jennifer J. Sun', 'Luke Friedman', 'Rui Qian', 'Tobias Weyand', 'Yue Zhao', 'Rachel Hornung', 'Florian Schroff', 'Ming-Hsuan Yang', 'David A. Ross', 'Huisheng Wang', 'Hartwig Adam', 'Mikhail Sirotenko', 'Ting Liu', 'Boqing Gong'] | ['cs.CV', 'cs.AI'] | We introduce VideoPrism, a general-purpose video encoder that tackles diverse
video understanding tasks with a single frozen model. We pretrain VideoPrism on
a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M
video clips with noisy parallel text (e.g., ASR transcripts). The pretraining
appr... | 2024-02-20T18:29:49Z | Accepted to ICML 2024. v2: added retrieval results on MSRVTT (1K-A),
more data analyses, and ablation studies; v3: released models at
https://github.com/google-deepmind/videoprism | null | null | VideoPrism: A Foundational Visual Encoder for Video Understanding | ['Long Zhao', 'N. B. Gundavarapu', 'Liangzhe Yuan', 'Hao Zhou', 'Shen Yan', 'Jennifer J. Sun', 'Luke Friedman', 'Rui Qian', 'Tobias Weyand', 'Yue Zhao', 'Rachel Hornung', 'Florian Schroff', 'Ming Yang', 'David A. Ross', 'Huisheng Wang', 'Hartwig Adam', 'Mikhail Sirotenko', 'Ting Liu', 'Boqing Gong'] | 2,024 | International Conference on Machine Learning | 36 | 144 | ['Computer Science'] |
2,402.13228 | Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive | ['Arka Pal', 'Deep Karkhanis', 'Samuel Dooley', 'Manley Roberts', 'Siddartha Naidu', 'Colin White'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Direct Preference Optimisation (DPO) is effective at significantly improving
the performance of large language models (LLMs) on downstream tasks such as
reasoning, summarisation, and alignment. Using pairs of preferred and
dispreferred data, DPO models the relative probability of picking one response
over another. In t... | 2024-02-20T18:42:34Z | null | null | null | null | null | null | null | null | null | null |
2,402.13232 | A Touch, Vision, and Language Dataset for Multimodal Alignment | ['Letian Fu', 'Gaurav Datta', 'Huang Huang', 'William Chung-Ho Panitch', 'Jaimyn Drake', 'Joseph Ortiz', 'Mustafa Mukadam', 'Mike Lambeta', 'Roberto Calandra', 'Ken Goldberg'] | ['cs.CV', 'cs.RO'] | Touch is an important sensing modality for humans, but it has not yet been
incorporated into a multimodal generative language model. This is partially due
to the difficulty of obtaining natural language labels for tactile data and the
complexity of aligning tactile readings with both visual observations and
language de... | 2024-02-20T18:47:56Z | null | null | null | null | null | null | null | null | null | null |
2,402.13253 | BiMediX: Bilingual Medical Mixture of Experts LLM | ['Sara Pieri', 'Sahal Shaji Mullappilly', 'Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Salman Khan', 'Timothy Baldwin', 'Hisham Cholakkal'] | ['cs.CL'] | In this paper, we introduce BiMediX, the first bilingual medical mixture of
experts LLM designed for seamless interaction in both English and Arabic. Our
model facilitates a wide range of medical interactions in English and Arabic,
including multi-turn chats to inquire about additional details such as patient
symptoms ... | 2024-02-20T18:59:26Z | Accepted to EMNLP 2024 (Findings) | Findings of the Association for Computational Linguistics: EMNLP
2024, pages 16984-17002 | 10.18653/v1/2024.findings-emnlp.989 | BiMediX: Bilingual Medical Mixture of Experts LLM | ['Sara Pieri', 'Sahal Shaji Mullappilly', 'F. Khan', 'R. Anwer', 'Salman H. Khan', 'Timothy Baldwin', 'Hisham Cholakkal'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 15 | 43 | ['Computer Science'] |
2,402.1335 | PIRB: A Comprehensive Benchmark of Polish Dense and Hybrid Text
Retrieval Methods | ['Sławomir Dadas', 'Michał Perełkiewicz', 'Rafał Poświata'] | ['cs.CL'] | We present Polish Information Retrieval Benchmark (PIRB), a comprehensive
evaluation framework encompassing 41 text information retrieval tasks for
Polish. The benchmark incorporates existing datasets as well as 10 new,
previously unpublished datasets covering diverse topics such as medicine, law,
business, physics, an... | 2024-02-20T19:53:36Z | null | null | null | null | null | null | null | null | null | null |
2,402.13516 | ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity
within Large Language Models | ['Chenyang Song', 'Xu Han', 'Zhengyan Zhang', 'Shengding Hu', 'Xiyu Shi', 'Kuai Li', 'Chen Chen', 'Zhiyuan Liu', 'Guangli Li', 'Tao Yang', 'Maosong Sun'] | ['cs.LG', 'cs.AI', 'cs.CL', 'I.2.7'] | Activation sparsity refers to the existence of considerable
weakly-contributed elements among activation outputs. As a prevalent property
of the models using the ReLU activation function, activation sparsity has been
proven a promising paradigm to boost model inference efficiency. Nevertheless,
most large language mode... | 2024-02-21T03:58:49Z | 19 pages, 4 figures, 9 tables | null | null | ProSparse: Introducing and Enhancing Intrinsic Activation Sparsity within Large Language Models | ['Chenyang Song', 'Xu Han', 'Zhengyan Zhang', 'Shengding Hu', 'Xiyu Shi', 'Kuai Li', 'Chen Chen', 'Zhiyuan Liu', 'Guanglin Li', 'Tao Yang', 'Maosong Sun'] | 2,024 | International Conference on Computational Linguistics | 32 | 98 | ['Computer Science'] |
2,402.13583 | LongWanjuan: Towards Systematic Measurement for Long Text Quality | ['Kai Lv', 'Xiaoran Liu', 'Qipeng Guo', 'Hang Yan', 'Conghui He', 'Xipeng Qiu', 'Dahua Lin'] | ['cs.CL'] | The quality of training data are crucial for enhancing the long-text
capabilities of foundation models. Despite existing efforts to refine data
quality through heuristic rules and evaluations based on data diversity and
difficulty, there's a lack of systematic approaches specifically tailored for
assessing long texts. ... | 2024-02-21T07:27:18Z | Update Figures | null | null | LongWanjuan: Towards Systematic Measurement for Long Text Quality | ['Kai Lv', 'Xiaoran Liu', 'Qipeng Guo', 'Hang Yan', 'Conghui He', 'Xipeng Qiu', 'Dahua Lin'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 4 | 67 | ['Computer Science'] |
2,402.13604 | Breaking the HISCO Barrier: Automatic Occupational Standardization with
OccCANINE | ['Christian Møller Dahl', 'Torben Johansen', 'Christian Vedel'] | ['cs.CL', 'econ.EM', 'I.2.7; I.7.0'] | This paper introduces a new tool, OccCANINE, to automatically transform
occupational descriptions into the HISCO classification system. The manual work
involved in processing and classifying occupational descriptions is
error-prone, tedious, and time-consuming. We finetune a preexisting language
model (CANINE) to do th... | 2024-02-21T08:10:43Z | All code and guides on how to use OccCANINE is available on GitHub
https://github.com/christianvedels/OccCANINE | null | null | null | null | null | null | null | null | null |
2,402.13616 | YOLOv9: Learning What You Want to Learn Using Programmable Gradient
Information | ['Chien-Yao Wang', 'I-Hau Yeh', 'Hong-Yuan Mark Liao'] | ['cs.CV'] | Today's deep learning methods focus on how to design the most appropriate
objective functions so that the prediction results of the model can be closest
to the ground truth. Meanwhile, an appropriate architecture that can facilitate
acquisition of enough information for prediction has to be designed. Existing
methods i... | 2024-02-21T08:42:53Z | null | null | null | null | null | null | null | null | null | null |
2,402.13643 | Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition | ['Mingkun Yang', 'Biao Yang', 'Minghui Liao', 'Yingying Zhu', 'Xiang Bai'] | ['cs.CV'] | Scene text recognition is a rapidly developing field that faces numerous
challenges due to the complexity and diversity of scene text, including complex
backgrounds, diverse fonts, flexible arrangements, and accidental occlusions.
In this paper, we propose a novel approach called Class-Aware Mask-guided
feature refinem... | 2024-02-21T09:22:45Z | Accepted by Pattern Recognition | null | null | null | null | null | null | null | null | null |
2,402.13718 | $\infty$Bench: Extending Long Context Evaluation Beyond 100K Tokens | ['Xinrong Zhang', 'Yingfa Chen', 'Shengding Hu', 'Zihang Xu', 'Junhao Chen', 'Moo Khai Hao', 'Xu Han', 'Zhen Leng Thai', 'Shuo Wang', 'Zhiyuan Liu', 'Maosong Sun'] | ['cs.CL'] | Processing and reasoning over long contexts is crucial for many practical
applications of Large Language Models (LLMs), such as document comprehension
and agent construction. Despite recent strides in making LLMs process contexts
with more than 100K tokens, there is currently a lack of a standardized
benchmark to evalu... | 2024-02-21T11:30:29Z | null | 2023.12.15ARR | null | ∞Bench: Extending Long Context Evaluation Beyond 100K Tokens | ['Xinrong Zhang', 'Yingfa Chen', 'Shengding Hu', 'Zihang Xu', 'Junhao Chen', 'Moo Khai Hao', 'Xu Han', 'Z. Thai', 'Shuo Wang', 'Zhiyuan Liu', 'Maosong Sun'] | 2,024 | Volume 1 | 195 | 53 | ['Computer Science'] |
2,402.13753 | LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens | ['Yiran Ding', 'Li Lyna Zhang', 'Chengruidong Zhang', 'Yuanyuan Xu', 'Ning Shang', 'Jiahang Xu', 'Fan Yang', 'Mao Yang'] | ['cs.CL'] | Large context window is a desirable feature in large language models (LLMs).
However, due to high fine-tuning costs, scarcity of long texts, and
catastrophic values introduced by new token positions, current extended context
windows are limited to around 128k tokens. This paper introduces LongRoPE that,
for the first t... | 2024-02-21T12:30:33Z | null | null | null | null | null | null | null | null | null | null |
2,402.13852 | Neural Control System for Continuous Glucose Monitoring and Maintenance | ['Azmine Toushik Wasi'] | ['cs.LG', 'cs.AI', 'cs.NE', 'cs.SY', 'eess.SY', 'stat.ML'] | Precise glucose level monitoring is critical for people with diabetes to
avoid serious complications. While there are several methods for continuous
glucose level monitoring, research on maintenance devices is limited. To
mitigate the gap, we provide a novel neural control system for continuous
glucose monitoring and m... | 2024-02-21T14:56:36Z | 9 Pages, 4 figures, ICLR 2024 Tiny Papers Track
https://openreview.net/forum?id=Te4P3Cn54g | The Second Tiny Papers Track at ICLR 2024 | null | null | null | null | null | null | null | null |
2,402.13929 | SDXL-Lightning: Progressive Adversarial Diffusion Distillation | ['Shanchuan Lin', 'Anran Wang', 'Xiao Yang'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We propose a diffusion distillation method that achieves new state-of-the-art
in one-step/few-step 1024px text-to-image generation based on SDXL. Our method
combines progressive and adversarial distillation to achieve a balance between
quality and mode coverage. In this paper, we discuss the theoretical analysis,
discr... | 2024-02-21T16:51:05Z | null | null | null | SDXL-Lightning: Progressive Adversarial Diffusion Distillation | ['Shanchuan Lin', 'Anran Wang', 'Xiao Yang'] | 2,024 | arXiv.org | 134 | 79 | ['Computer Science'] |
2,402.13963 | Towards Building Multilingual Language Model for Medicine | ['Pengcheng Qiu', 'Chaoyi Wu', 'Xiaoman Zhang', 'Weixiong Lin', 'Haicheng Wang', 'Ya Zhang', 'Yanfeng Wang', 'Weidi Xie'] | ['cs.CL'] | The development of open-source, multilingual medical language models can
benefit a wide, linguistically diverse audience from different regions. To
promote this domain, we present contributions from the following: First, we
construct a multilingual medical corpus, containing approximately 25.5B tokens
encompassing 6 ma... | 2024-02-21T17:47:20Z | null | null | null | null | null | null | null | null | null | null |
2,402.13991 | Analysing The Impact of Sequence Composition on Language Model
Pre-Training | ['Yu Zhao', 'Yuanbin Qu', 'Konrad Staniszewski', 'Szymon Tworkowski', 'Wei Liu', 'Piotr Miłoś', 'Yuxiang Wu', 'Pasquale Minervini'] | ['cs.CL'] | Most language model pre-training frameworks concatenate multiple documents
into fixed-length sequences and use causal masking to compute the likelihood of
each token given its context; this strategy is widely adopted due to its
simplicity and efficiency. However, to this day, the influence of the
pre-training sequence ... | 2024-02-21T18:23:16Z | null | Analysing The Impact of Sequence Composition on Language Model
Pre-Training (Zhao et al., ACL 2024) | 10.18653/v1/2024.acl-long.427 | Analysing The Impact of Sequence Composition on Language Model Pre-Training | ['Yu Zhao', 'Yuanbin Qu', 'Konrad Staniszewski', 'Szymon Tworkowski', 'Wei Liu', "Piotr Milo's", 'Yuxiang Wu', 'Pasquale Minervini'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 15 | 43 | ['Computer Science'] |
2,402.14285 | Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion | ['Yujia Huang', 'Adishree Ghatare', 'Yuanzhe Liu', 'Ziniu Hu', 'Qinsheng Zhang', 'Chandramouli S Sastry', 'Siddharth Gururani', 'Sageev Oore', 'Yisong Yue'] | ['cs.SD', 'cs.LG', 'eess.AS'] | We study the problem of symbolic music generation (e.g., generating piano
rolls), with a technical focus on non-differentiable rule guidance. Musical
rules are often expressed in symbolic form on note characteristics, such as
note density or chord progression, many of which are non-differentiable which
pose a challenge... | 2024-02-22T04:55:58Z | ICML 2024 (Oral) | null | null | null | null | null | null | null | null | null |
2,402.14289 | TinyLLaVA: A Framework of Small-scale Large Multimodal Models | ['Baichuan Zhou', 'Ying Hu', 'Xi Weng', 'Junlong Jia', 'Jie Luo', 'Xien Liu', 'Ji Wu', 'Lei Huang'] | ['cs.LG', 'cs.CL'] | We present the TinyLLaVA framework that provides a unified perspective in
designing and analyzing the small-scale Large Multimodal Models (LMMs). We
empirically study the effects of different vision encoders, connection modules,
language models, training data and training recipes. Our extensive experiments
showed that ... | 2024-02-22T05:05:30Z | Our model weights and codes will be made public at
https://github.com/DLCV-BUAA/TinyLLaVABench | null | null | null | null | null | null | null | null | null |
2,402.1431 | Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize
Encoded Knowledge | ['Jinlan Fu', 'Shenzhen Huangfu', 'Hang Yan', 'See-Kiong Ng', 'Xipeng Qiu'] | ['cs.CL'] | Large Language Models (LLMs) have recently showcased remarkable
generalizability in various domains. Despite their extensive knowledge, LLMs
still face challenges in efficiently utilizing encoded knowledge to develop
accurate and logical reasoning processes. To mitigate this problem, we
introduced Hint-before-Solving P... | 2024-02-22T05:58:03Z | 18 pages | null | null | Hint-before-Solving Prompting: Guiding LLMs to Effectively Utilize Encoded Knowledge | ['Jinlan Fu', 'Shenzhen Huangfu', 'Hang Yan', 'See-Kiong Ng', 'Xipeng Qiu'] | 2,024 | arXiv.org | 8 | 35 | ['Computer Science'] |
2,402.14318 | Assessing generalization capability of text ranking models in Polish | ['Sławomir Dadas', 'Małgorzata Grębowiec'] | ['cs.CL'] | Retrieval-augmented generation (RAG) is becoming an increasingly popular
technique for integrating internal knowledge bases with large language models.
In a typical RAG pipeline, three models are used, responsible for the
retrieval, reranking, and generation stages. In this article, we focus on the
reranking problem fo... | 2024-02-22T06:21:41Z | null | null | null | null | null | null | null | null | null | null |
2,402.14327 | Subobject-level Image Tokenization | ['Delong Chen', 'Samuel Cahyawijaya', 'Jianfeng Liu', 'Baoyuan Wang', 'Pascale Fung'] | ['cs.CV', 'cs.CL'] | Patch-based image tokenization ignores the morphology of the visual world,
limiting effective and efficient learning of image understanding. Inspired by
subword tokenization, we introduce subobject-level adaptive token segmentation
and explore several approaches, including superpixel, SAM, and a proposed
Efficient and ... | 2024-02-22T06:47:44Z | null | null | null | Subobject-level Image Tokenization | ['Delong Chen', 'Samuel Cahyawijaya', 'Jianfeng Liu', 'Baoyuan Wang', 'Pascale Fung'] | 2,024 | arXiv.org | 9 | 91 | ['Computer Science'] |
2,402.14379 | Novi jezički modeli za srpski jezik | ['Mihailo Škorić'] | ['cs.CL'] | The paper will briefly present the development history of transformer-based
language models for the Serbian language. Several new models for text
generation and vectorization, trained on the resources of the Society for
Language Resources and Technologies, will also be presented. Ten selected
vectorization models for S... | 2024-02-22T08:48:21Z | in Serbian language | null | null | null | null | null | null | null | null | null |
2,402.14407 | Learning an Actionable Discrete Diffusion Policy via Large-Scale
Actionless Video Pre-Training | ['Haoran He', 'Chenjia Bai', 'Ling Pan', 'Weinan Zhang', 'Bin Zhao', 'Xuelong Li'] | ['cs.LG', 'cs.CV', 'cs.RO'] | Learning a generalist embodied agent capable of completing multiple tasks
poses challenges, primarily stemming from the scarcity of action-labeled
robotic datasets. In contrast, a vast amount of human videos exist, capturing
intricate tasks and interactions with the physical world. Promising prospects
arise for utilizi... | 2024-02-22T09:48:47Z | Accepted by NeurIPS 2024. 24 pages | null | null | null | null | null | null | null | null | null |
2,402.14499 | "My Answer is C": First-Token Probabilities Do Not Match Text Answers in
Instruction-Tuned Language Models | ['Xinpeng Wang', 'Bolei Ma', 'Chengzhi Hu', 'Leon Weber-Genzel', 'Paul Röttger', 'Frauke Kreuter', 'Dirk Hovy', 'Barbara Plank'] | ['cs.CL'] | The open-ended nature of language generation makes the evaluation of
autoregressive large language models (LLMs) challenging. One common evaluation
approach uses multiple-choice questions (MCQ) to limit the response space. The
model is then evaluated by ranking the candidate answers by the log probability
of the first ... | 2024-02-22T12:47:33Z | ACL 2024 Findings | null | null | null | null | null | null | null | null | null |
2,402.14526 | Balanced Data Sampling for Language Model Training with Clustering | ['Yunfan Shao', 'Linyang Li', 'Zhaoye Fei', 'Hang Yan', 'Dahua Lin', 'Xipeng Qiu'] | ['cs.CL', 'cs.AI'] | Data plays a fundamental role in the training of Large Language Models
(LLMs). While attention has been paid to the collection and composition of
datasets, determining the data sampling strategy in training remains an open
question. Most LLMs are trained with a simple strategy, random sampling.
However, this sampling s... | 2024-02-22T13:20:53Z | ACL 2024 (findings), Code is released at
https://github.com/choosewhatulike/cluster-clip | null | null | null | null | null | null | null | null | null |
2,402.14545 | Less is More: Mitigating Multimodal Hallucination from an EOS Decision
Perspective | ['Zihao Yue', 'Liang Zhang', 'Qin Jin'] | ['cs.CL', 'cs.CV'] | Large Multimodal Models (LMMs) often suffer from multimodal hallucinations,
wherein they may create content that is not present in the visual inputs. In
this paper, we explore a new angle of this issue: overly detailed training data
hinders the model's ability to timely terminate generation, leading to
continued output... | 2024-02-22T13:33:13Z | Accepted to ACL 2024 | null | null | null | null | null | null | null | null | null |
2,402.14654 | Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot | ['Fabien Baradel', 'Matthieu Armando', 'Salma Galaaoui', 'Romain Brégier', 'Philippe Weinzaepfel', 'Grégory Rogez', 'Thomas Lucas'] | ['cs.CV'] | We present Multi-HMR, a strong sigle-shot model for multi-person 3D human
mesh recovery from a single RGB image. Predictions encompass the whole body,
i.e., including hands and facial expressions, using the SMPL-X parametric model
and 3D location in the camera coordinate system. Our model detects people by
predicting c... | 2024-02-22T16:05:13Z | Accepted at ECCV'24 - Code: https://github.com/naver/multi-hmr | null | null | null | null | null | null | null | null | null |
2,402.14658 | OpenCodeInterpreter: Integrating Code Generation with Execution and
Refinement | ['Tianyu Zheng', 'Ge Zhang', 'Tianhao Shen', 'Xueling Liu', 'Bill Yuchen Lin', 'Jie Fu', 'Wenhu Chen', 'Xiang Yue'] | ['cs.SE', 'cs.AI', 'cs.CL'] | The introduction of large language models has significantly advanced code
generation. However, open-source models often lack the execution capabilities
and iterative refinement of advanced systems like the GPT-4 Code Interpreter.
To address this, we introduce OpenCodeInterpreter, a family of open-source code
systems de... | 2024-02-22T16:06:23Z | null | null | null | OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement | ['Tianyu Zheng', 'Ge Zhang', 'Tianhao Shen', 'Xueling Liu', 'Bill Yuchen Lin', 'Jie Fu', 'Wenhu Chen', 'Xiang Yue'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 131 | 45 | ['Computer Science'] |
2,402.1471 | IEPile: Unearthing Large-Scale Schema-Based Information Extraction
Corpus | ['Honghao Gui', 'Lin Yuan', 'Hongbin Ye', 'Ningyu Zhang', 'Mengshu Sun', 'Lei Liang', 'Huajun Chen'] | ['cs.CL', 'cs.AI', 'cs.DB', 'cs.IR', 'cs.LG'] | Large Language Models (LLMs) demonstrate remarkable potential across various
domains; however, they exhibit a significant performance gap in Information
Extraction (IE). Note that high-quality instruction data is the vital key for
enhancing the specific capabilities of LLMs, while current IE datasets tend to
be small i... | 2024-02-22T17:11:38Z | ACL 2024 (short); 21 pages; Github: https://github.com/zjunlp/IEPile | null | null | null | null | null | null | null | null | null |
2,402.14714 | Efficient and Effective Vocabulary Expansion Towards Multilingual Large
Language Models | ['Seungduk Kim', 'Seungtaek Choi', 'Myeongho Jeong'] | ['cs.CL', 'cs.AI'] | This report introduces \texttt{EEVE-Korean-v1.0}, a Korean adaptation of
large language models that exhibit remarkable capabilities across English and
Korean text understanding. Building on recent highly capable but
English-centric LLMs, such as SOLAR-10.7B and Phi-2, where non-English texts
are inefficiently processed... | 2024-02-22T17:12:39Z | null | null | null | Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models | ['Seungduk Kim', 'Seungtaek Choi', 'Myeongho Jeong'] | 2,024 | arXiv.org | 7 | 31 | ['Computer Science'] |
2,402.1474 | Back to Basics: Revisiting REINFORCE Style Optimization for Learning
from Human Feedback in LLMs | ['Arash Ahmadian', 'Chris Cremer', 'Matthias Gallé', 'Marzieh Fadaee', 'Julia Kreutzer', 'Olivier Pietquin', 'Ahmet Üstün', 'Sara Hooker'] | ['cs.LG', 'I.2.7'] | AI alignment in the shape of Reinforcement Learning from Human Feedback
(RLHF) is increasingly treated as a crucial ingredient for high performance
large language models. Proximal Policy Optimization (PPO) has been positioned
by recent literature as the canonical method for the RL part of RLHF. However,
it involves bot... | 2024-02-22T17:52:34Z | 27 pages, 7 figures, 2 tables | null | null | null | null | null | null | null | null | null |
2,402.14776 | 2D Matryoshka Sentence Embeddings | ['Xianming Li', 'Zongxi Li', 'Jing Li', 'Haoran Xie', 'Qing Li'] | ['cs.CL', 'cs.LG'] | Common approaches rely on fixed-length embedding vectors from language models
as sentence embeddings for downstream tasks such as semantic textual similarity
(STS). Such methods are limited in their flexibility due to unknown
computational constraints and budgets across various applications. Matryoshka
Representation L... | 2024-02-22T18:35:05Z | Decoupled with ESE | null | null | 2D Matryoshka Sentence Embeddings | ['Xianming Li', 'Zongxi Li', 'Jing Li', 'Haoran Xie', 'Qing Li'] | 2,024 | arXiv.org | 4 | 34 | ['Computer Science'] |
2,402.14811 | Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity
Tracking | ['Nikhil Prakash', 'Tamar Rott Shaham', 'Tal Haklay', 'Yonatan Belinkov', 'David Bau'] | ['cs.CL', 'cs.LG'] | Fine-tuning on generalized tasks such as instruction following, code
generation, and mathematics has been shown to enhance language models'
performance on a range of tasks. Nevertheless, explanations of how such
fine-tuning influences the internal computations in these models remain
elusive. We study how fine-tuning af... | 2024-02-22T18:59:24Z | ICLR 2024. 26 pages, 13 figures. Code and data at
https://finetuning.baulab.info/ | null | null | Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking | ['Nikhil Prakash', 'Tamar Rott Shaham', 'Tal Haklay', 'Yonatan Belinkov', 'David Bau'] | 2,024 | International Conference on Learning Representations | 67 | 51 | ['Computer Science'] |
2,402.1483 | Orca-Math: Unlocking the potential of SLMs in Grade School Math | ['Arindam Mitra', 'Hamed Khanpour', 'Corby Rosset', 'Ahmed Awadallah'] | ['cs.CL', 'cs.AI'] | Mathematical word problem-solving has long been recognized as a complex task
for small language models (SLMs). A recent study hypothesized that the smallest
model size, needed to achieve over 80% accuracy on the GSM8K benchmark, is 34
billion parameters. To reach this level of performance with smaller models,
researche... | 2024-02-16T23:44:38Z | null | null | null | null | null | null | null | null | null | null |
2,402.14905 | MobileLLM: Optimizing Sub-billion Parameter Language Models for
On-Device Use Cases | ['Zechun Liu', 'Changsheng Zhao', 'Forrest Iandola', 'Chen Lai', 'Yuandong Tian', 'Igor Fedorov', 'Yunyang Xiong', 'Ernie Chang', 'Yangyang Shi', 'Raghuraman Krishnamoorthi', 'Liangzhen Lai', 'Vikas Chandra'] | ['cs.LG', 'cs.AI', 'cs.CL'] | This paper addresses the growing need for efficient large language models
(LLMs) on mobile devices, driven by increasing cloud costs and latency
concerns. We focus on designing top-quality LLMs with fewer than a billion
parameters, a practical choice for mobile deployment. Contrary to prevailing
belief emphasizing the ... | 2024-02-22T18:58:55Z | ICML 2024. Code is available at
https://github.com/facebookresearch/MobileLLM | null | null | MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases | ['Zechun Liu', 'Changsheng Zhao', 'Forrest N. Iandola', 'Chen Lai', 'Yuandong Tian', 'Igor Fedorov', 'Yunyang Xiong', 'Ernie Chang', 'Yangyang Shi', 'Raghuraman Krishnamoorthi', 'Liangzhen Lai', 'Vikas Chandra'] | 2,024 | International Conference on Machine Learning | 103 | 65 | ['Computer Science'] |
2,402.14992 | tinyBenchmarks: evaluating LLMs with fewer examples | ['Felipe Maia Polo', 'Lucas Weber', 'Leshem Choshen', 'Yuekai Sun', 'Gongjun Xu', 'Mikhail Yurochkin'] | ['cs.CL', 'cs.AI', 'cs.LG', 'stat.ML'] | The versatility of large language models (LLMs) led to the creation of
diverse benchmarks that thoroughly test a variety of language models'
abilities. These benchmarks consist of tens of thousands of examples making
evaluation of LLMs very expensive. In this paper, we investigate strategies to
reduce the number of eva... | 2024-02-22T22:05:23Z | Proceedings of the 41st International Conference on Machine Learning
(ICML) | null | null | null | null | null | null | null | null | null |
2,402.15043 | KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large
Language Models | ['Zhuohao Yu', 'Chang Gao', 'Wenjin Yao', 'Yidong Wang', 'Wei Ye', 'Jindong Wang', 'Xing Xie', 'Yue Zhang', 'Shikun Zhang'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Automatic evaluation methods for large language models (LLMs) are hindered by
data contamination, leading to inflated assessments of their effectiveness.
Existing strategies, which aim to detect contaminated texts, focus on
quantifying contamination status instead of accurately gauging model
performance. In this paper,... | 2024-02-23T01:30:39Z | Accepted to ACL 2024 (main conference); 19 pages, 5 figures, 19
tables, code is available at: https://github.com/zhuohaoyu/KIEval | null | null | null | null | null | null | null | null | null |
2,402.15059 | ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot
Multilingual Information Retrieval | ['Antoine Louis', 'Vageesh Saxena', 'Gijs van Dijck', 'Gerasimos Spanakis'] | ['cs.CL', 'cs.IR'] | State-of-the-art neural retrievers predominantly focus on high-resource
languages like English, which impedes their adoption in retrieval scenarios
involving other languages. Current approaches circumvent the lack of
high-quality labeled data in non-English languages by leveraging multilingual
pretrained language model... | 2024-02-23T02:21:24Z | Under review. Code is available at
https://github.com/ant-louis/xm-retrievers | null | null | null | null | null | null | null | null | null |
2,402.15343 | NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data | ['Sergei Bogdanov', 'Alexandre Constantin', 'Timothée Bernard', 'Benoit Crabbé', 'Etienne Bernard'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) have shown impressive abilities in data
annotation, opening the way for new approaches to solve classic NLP problems.
In this paper, we show how to use LLMs to create NuNER, a compact language
representation model specialized in the Named Entity Recognition (NER) task.
NuNER can be fine-tun... | 2024-02-23T14:23:51Z | null | null | null | null | null | null | null | null | null | null |
2,402.15391 | Genie: Generative Interactive Environments | ['Jake Bruce', 'Michael Dennis', 'Ashley Edwards', 'Jack Parker-Holder', 'Yuge Shi', 'Edward Hughes', 'Matthew Lai', 'Aditi Mavalankar', 'Richie Steigerwald', 'Chris Apps', 'Yusuf Aytar', 'Sarah Bechtle', 'Feryal Behbahani', 'Stephanie Chan', 'Nicolas Heess', 'Lucy Gonzalez', 'Simon Osindero', 'Sherjil Ozair', 'Scott R... | ['cs.LG', 'cs.AI', 'cs.CV'] | We introduce Genie, the first generative interactive environment trained in
an unsupervised manner from unlabelled Internet videos. The model can be
prompted to generate an endless variety of action-controllable virtual worlds
described through text, synthetic images, photographs, and even sketches. At
11B parameters, ... | 2024-02-23T15:47:26Z | https://sites.google.com/corp/view/genie-2024/ | null | null | Genie: Generative Interactive Environments | ['Jake Bruce', 'Michael Dennis', 'Ashley Edwards', 'Jack Parker-Holder', 'Yuge Shi', 'Edward Hughes', 'Matthew Lai', 'Aditi Mavalankar', 'Richie Steigerwald', 'Chris Apps', 'Y. Aytar', 'Sarah Bechtle', 'Feryal M. P. Behbahani', 'Stephanie Chan', 'N. Heess', 'Lucy Gonzalez', 'Simon Osindero', 'Sherjil Ozair', 'Scott Ree... | 2,024 | International Conference on Machine Learning | 188 | 80 | ['Computer Science'] |
2,402.15449 | Repetition Improves Language Model Embeddings | ['Jacob Mitchell Springer', 'Suhas Kotha', 'Daniel Fried', 'Graham Neubig', 'Aditi Raghunathan'] | ['cs.CL', 'cs.LG'] | Recent approaches to improving the extraction of text embeddings from
autoregressive large language models (LLMs) have largely focused on
improvements to data, backbone pretrained language models, or improving
task-differentiation via instructions. In this work, we address an
architectural limitation of autoregressive ... | 2024-02-23T17:25:10Z | 36 pages, 11 figures, 16 tables | null | null | null | null | null | null | null | null | null |
2,402.15506 | AgentOhana: Design Unified Data and Training Pipeline for Effective
Agent Learning | ['Jianguo Zhang', 'Tian Lan', 'Rithesh Murthy', 'Zhiwei Liu', 'Weiran Yao', 'Ming Zhu', 'Juntao Tan', 'Thai Hoang', 'Zuxin Liu', 'Liangwei Yang', 'Yihao Feng', 'Shirley Kokane', 'Tulika Awalgaonkar', 'Juan Carlos Niebles', 'Silvio Savarese', 'Shelby Heinecke', 'Huan Wang', 'Caiming Xiong'] | ['cs.AI', 'cs.CL', 'cs.LG'] | Autonomous agents powered by large language models (LLMs) have garnered
significant research attention. However, fully harnessing the potential of LLMs
for agent-based tasks presents inherent challenges due to the heterogeneous
nature of diverse data sources featuring multi-turn trajectories. In this
paper, we introduc... | 2024-02-23T18:56:26Z | Add GitHub repo link at
\url{https://github.com/SalesforceAIResearch/xLAM} and HuggingFace model link
at \url{https://huggingface.co/Salesforce/xLAM-v0.1-r} | null | null | null | null | null | null | null | null | null |
2,402.15648 | MambaIR: A Simple Baseline for Image Restoration with State-Space Model | ['Hang Guo', 'Jinmin Li', 'Tao Dai', 'Zhihao Ouyang', 'Xudong Ren', 'Shu-Tao Xia'] | ['cs.CV'] | Recent years have seen significant advancements in image restoration, largely
attributed to the development of modern deep neural networks, such as CNNs and
Transformers. However, existing restoration backbones often face the dilemma
between global receptive fields and efficient computation, hindering their
application... | 2024-02-23T23:15:54Z | Accepted by ECCV2024 | null | null | MambaIR: A Simple Baseline for Image Restoration with State-Space Model | ['Hang Guo', 'Jinmin Li', 'Tao Dai', 'Zhihao Ouyang', 'Xudong Ren', 'Shu-Tao Xia'] | 2,024 | European Conference on Computer Vision | 249 | 96 | ['Computer Science'] |
2,402.15729 | How Do Humans Write Code? Large Models Do It the Same Way Too | ['Long Li', 'Xuzheng He', 'Haozhe Wang', 'Linlin Wang', 'Liang He'] | ['cs.AI', 'cs.CL', 'cs.PL'] | Program-of-Thought (PoT) replaces natural language-based Chain-of-Thought
(CoT) as the most popular method in Large Language Models (LLMs) mathematical
reasoning tasks by utilizing external tool calls to circumvent computational
errors. However, our evaluation of the GPT-4 and Llama series reveals that
using PoT introd... | 2024-02-24T05:40:01Z | null | null | null | null | null | null | null | null | null | null |
2,402.15761 | Res-VMamba: Fine-Grained Food Category Visual Classification Using
Selective State Space Models with Deep Residual Learning | ['Chi-Sheng Chen', 'Guan-Ying Chen', 'Dong Zhou', 'Di Jiang', 'Dai-Shi Chen'] | ['cs.CV', 'cs.AI'] | Food classification is the foundation for developing food vision tasks and
plays a key role in the burgeoning field of computational nutrition. Due to the
complexity of food requiring fine-grained classification, recent academic
research mainly modifies Convolutional Neural Networks (CNNs) and/or Vision
Transformers (V... | 2024-02-24T08:20:39Z | 14 pages, 3 figures | null | null | Res-VMamba: Fine-Grained Food Category Visual Classification Using Selective State Space Models with Deep Residual Learning | ['Chi-Sheng Chen', 'Guan-Ying Chen', 'Dong Zhou', 'Di Jiang', 'Daishi Chen'] | 2,024 | arXiv.org | 24 | 67 | ['Computer Science'] |
2,402.15861 | MATHWELL: Generating Educational Math Word Problems Using Teacher
Annotations | ['Bryan R Christ', 'Jonathan Kropko', 'Thomas Hartvigsen'] | ['cs.CL'] | Math word problems are critical K-8 educational tools, but writing them is
time consuming and requires extensive expertise. To be educational, problems
must be solvable, have accurate answers, and, most importantly, be
educationally appropriate. We propose that language models have potential to
support K-8 math educati... | 2024-02-24T17:08:45Z | 24 pages, 10 figures Accepted to EMNLP 2024 (Findings) | null | null | null | null | null | null | null | null | null |
2,402.15865 | HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved
Diffusion Models | ['Li Pang', 'Xiangyu Rui', 'Long Cui', 'Hongzhong Wang', 'Deyu Meng', 'Xiangyong Cao'] | ['cs.CV', 'eess.IV'] | Hyperspectral image (HSI) restoration aims at recovering clean images from
degraded observations and plays a vital role in downstream tasks. Existing
model-based methods have limitations in accurately modeling the complex image
characteristics with handcraft priors, and deep learning-based methods suffer
from poor gene... | 2024-02-24T17:15:05Z | null | null | null | null | null | null | null | null | null | null |
2,402.16029 | GraphWiz: An Instruction-Following Language Model for Graph Problems | ['Nuo Chen', 'Yuhan Li', 'Jianheng Tang', 'Jia Li'] | ['cs.CL'] | Large language models (LLMs) have achieved impressive success across several
fields, but their proficiency in understanding and resolving complex graph
problems is less explored. To bridge this gap, we introduce GraphInstruct, a
novel and comprehensive instruction-tuning dataset designed to equip language
models with t... | 2024-02-25T08:41:32Z | 27pages, 15 tables | null | null | null | null | null | null | null | null | null |
2,402.16065 | Training a Bilingual Language Model by Mapping Tokens onto a Shared
Character Space | ['Aviad Rom', 'Kfir Bar'] | ['cs.CL', 'cs.LG'] | We train a bilingual Arabic-Hebrew language model using a transliterated
version of Arabic texts in Hebrew, to ensure both languages are represented in
the same script. Given the morphological, structural similarities, and the
extensive number of cognates shared among Arabic and Hebrew, we assess the
performance of a l... | 2024-02-25T11:26:39Z | null | null | null | null | null | null | null | null | null | null |
2,402.16107 | Knowledge Fusion of Chat LLMs: A Preliminary Technical Report | ['Fanqi Wan', 'Ziyi Yang', 'Longguang Zhong', 'Xiaojun Quan', 'Xinting Huang', 'Wei Bi'] | ['cs.CL'] | Recently, FuseLLM introduced the concept of knowledge fusion to transfer the
collective knowledge of multiple structurally varied LLMs into a target LLM
through lightweight continual training. In this report, we extend the
scalability and flexibility of the FuseLLM framework to realize the fusion of
chat LLMs, resultin... | 2024-02-25T15:11:58Z | Technical Report, work in progress | null | null | Knowledge Fusion of Chat LLMs: A Preliminary Technical Report | ['Fanqi Wan', 'Ziyi Yang', 'Longguang Zhong', 'Xiaojun Quan', 'Xinting Huang', 'Wei Bi'] | 2,024 | null | 1 | 40 | ['Computer Science'] |
2,402.16153 | ChatMusician: Understanding and Generating Music Intrinsically with LLM | ['Ruibin Yuan', 'Hanfeng Lin', 'Yi Wang', 'Zeyue Tian', 'Shangda Wu', 'Tianhao Shen', 'Ge Zhang', 'Yuhang Wu', 'Cong Liu', 'Ziya Zhou', 'Ziyang Ma', 'Liumeng Xue', 'Ziyu Wang', 'Qin Liu', 'Tianyu Zheng', 'Yizhi Li', 'Yinghao Ma', 'Yiming Liang', 'Xiaowei Chi', 'Ruibo Liu', 'Zili Wang', 'Pengfei Li', 'Jingcheng Wu', 'Ch... | ['cs.SD', 'cs.AI', 'cs.CL', 'cs.LG', 'cs.MM', 'eess.AS'] | While Large Language Models (LLMs) demonstrate impressive capabilities in
text generation, we find that their ability has yet to be generalized to music,
humanity's creative language. We introduce ChatMusician, an open-source LLM
that integrates intrinsic musical abilities. It is based on continual
pre-training and fin... | 2024-02-25T17:19:41Z | GitHub: https://shanghaicannon.github.io/ChatMusician/ | null | null | null | null | null | null | null | null | null |
2,402.16352 | MathGenie: Generating Synthetic Data with Question Back-translation for
Enhancing Mathematical Reasoning of LLMs | ['Zimu Lu', 'Aojun Zhou', 'Houxing Ren', 'Ke Wang', 'Weikang Shi', 'Junting Pan', 'Mingjie Zhan', 'Hongsheng Li'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) have exhibited great potential in mathematical
reasoning. However, there remains a performance gap in this area between
existing open-source models and closed-source models such as GPT-4. In this
paper, we introduce MathGenie, a novel method for generating diverse and
reliable math problems... | 2024-02-26T07:17:25Z | ACL 2024 camera ready | null | null | null | null | null | null | null | null | null |
2,402.16444 | ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable
Safety Detectors | ['Zhexin Zhang', 'Yida Lu', 'Jingyuan Ma', 'Di Zhang', 'Rui Li', 'Pei Ke', 'Hao Sun', 'Lei Sha', 'Zhifang Sui', 'Hongning Wang', 'Minlie Huang'] | ['cs.CL'] | The safety of Large Language Models (LLMs) has gained increasing attention in
recent years, but there still lacks a comprehensive approach for detecting
safety issues within LLMs' responses in an aligned, customizable and
explainable manner. In this paper, we propose ShieldLM, an LLM-based safety
detector, which aligns... | 2024-02-26T09:43:02Z | 19 pages. Camera ready version of EMNLP 2024 Findings | null | null | ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors | ['Zhexin Zhang', 'Yida Lu', 'Jingyuan Ma', 'Di Zhang', 'Rui Li', 'Pei Ke', 'Hao Sun', 'Lei Sha', 'Zhifang Sui', 'Hongning Wang', 'Minlie Huang'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 31 | 40 | ['Computer Science'] |
2,402.16445 | ProLLaMA: A Protein Language Model for Multi-Task Protein Language
Processing | ['Liuzhenghao Lv', 'Zongying Lin', 'Hao Li', 'Yuyang Liu', 'Jiaxi Cui', 'Calvin Yu-Chian Chen', 'Li Yuan', 'Yonghong Tian'] | ['cs.CE', 'q-bio.BM'] | Large Language Models (LLMs) have achieved remarkable performance in multiple
Natural Language Processing (NLP) tasks. Under the premise that protein
sequences constitute the protein language, Protein Language Models(PLMs) have
advanced the field of protein engineering. However, as of now, unlike LLMs in
NLP, PLMs cann... | 2024-02-26T09:43:52Z | null | null | null | null | null | null | null | null | null | null |
2,402.16472 | mEdIT: Multilingual Text Editing via Instruction Tuning | ['Vipul Raheja', 'Dimitris Alikaniotis', 'Vivek Kulkarni', 'Bashar Alhafni', 'Dhruv Kumar'] | ['cs.CL', 'cs.AI', 'I.2.7'] | We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent
state-of-the-art text editing models for writing assistance. mEdIT models are
trained by fine-tuning multi-lingual large, pre-trained language models (LLMs)
via instruction tuning. They are designed to take instructions from the user
specifying the a... | 2024-02-26T10:33:36Z | Accepted to NAACL 2024 (Main). 23 pages, 8 tables, 11 figures | null | null | null | null | null | null | null | null | null |
2,402.16602 | Rethinking Negative Instances for Generative Named Entity Recognition | ['Yuyang Ding', 'Juntao Li', 'Pinzheng Wang', 'Zecheng Tang', 'Bowen Yan', 'Min Zhang'] | ['cs.CL'] | Large Language Models (LLMs) have demonstrated impressive capabilities for
generalizing in unseen tasks. In the Named Entity Recognition (NER) task,
recent advancements have seen the remarkable improvement of LLMs in a broad
range of entity domains via instruction tuning, by adopting entity-centric
schema. In this work... | 2024-02-26T14:30:37Z | ACL 2024 Findings | null | null | Rethinking Negative Instances for Generative Named Entity Recognition | ['Yuyang Ding', 'Juntao Li', 'Pinzheng Wang', 'Zecheng Tang', 'Bowen Yan', 'Min Zhang'] | 2,024 | Annual Meeting of the Association for Computational Linguistics | 13 | 49 | ['Computer Science'] |
2,402.16641 | Towards Open-ended Visual Quality Comparison | ['Haoning Wu', 'Hanwei Zhu', 'Zicheng Zhang', 'Erli Zhang', 'Chaofeng Chen', 'Liang Liao', 'Chunyi Li', 'Annan Wang', 'Wenxiu Sun', 'Qiong Yan', 'Xiaohong Liu', 'Guangtao Zhai', 'Shiqi Wang', 'Weisi Lin'] | ['cs.CV'] | Comparative settings (e.g. pairwise choice, listwise ranking) have been
adopted by a wide range of subjective studies for image quality assessment
(IQA), as it inherently standardizes the evaluation criteria across different
observers and offer more clear-cut responses. In this work, we extend the edge
of emerging larg... | 2024-02-26T15:10:56Z | Fix typos | null | null | null | null | null | null | null | null | null |
2,402.16671 | StructLM: Towards Building Generalist Models for Structured Knowledge
Grounding | ['Alex Zhuang', 'Ge Zhang', 'Tianyu Zheng', 'Xinrun Du', 'Junjie Wang', 'Weiming Ren', 'Stephen W. Huang', 'Jie Fu', 'Xiang Yue', 'Wenhu Chen'] | ['cs.CL'] | Structured data sources, such as tables, graphs, and databases, are
ubiquitous knowledge sources. Despite the demonstrated capabilities of large
language models (LLMs) on plain text, their proficiency in interpreting and
utilizing structured data remains limited. Our investigation reveals a notable
deficiency in LLMs' ... | 2024-02-26T15:47:01Z | Technical Report | null | null | null | null | null | null | null | null | null |
2,402.16689 | Adaptation of Biomedical and Clinical Pretrained Models to French Long
Documents: A Comparative Study | ['Adrien Bazoge', 'Emmanuel Morin', 'Beatrice Daille', 'Pierre-Antoine Gourraud'] | ['cs.CL', 'cs.AI'] | Recently, pretrained language models based on BERT have been introduced for
the French biomedical domain. Although these models have achieved
state-of-the-art results on biomedical and clinical NLP tasks, they are
constrained by a limited input sequence length of 512 tokens, which poses
challenges when applied to clini... | 2024-02-26T16:05:33Z | null | null | null | null | null | null | null | null | null | null |
2,402.16775 | A Comprehensive Evaluation of Quantization Strategies for Large Language
Models | ['Renren Jin', 'Jiangcun Du', 'Wuwei Huang', 'Wei Liu', 'Jian Luan', 'Bin Wang', 'Deyi Xiong'] | ['cs.CL', 'cs.AI'] | Increasing the number of parameters in large language models (LLMs) usually
improves performance in downstream tasks but raises compute and memory costs,
making deployment difficult in resource-limited settings. Quantization
techniques, which reduce the bits needed for model weights or activations with
minimal performa... | 2024-02-26T17:45:36Z | ACL 2024 Findings | null | null | null | null | null | null | null | null | null |
2,402.16819 | Nemotron-4 15B Technical Report | ['Jupinder Parmar', 'Shrimai Prabhumoye', 'Joseph Jennings', 'Mostofa Patwary', 'Sandeep Subramanian', 'Dan Su', 'Chen Zhu', 'Deepak Narayanan', 'Aastha Jhunjhunwala', 'Ayush Dattagupta', 'Vibhu Jawa', 'Jiwei Liu', 'Ameya Mahabaleshwarkar', 'Osvald Nitski', 'Annika Brundyn', 'James Maki', 'Miguel Martinez', 'Jiaxuan Yo... | ['cs.CL', 'cs.AI', 'cs.LG'] | We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual
language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates
strong performance when assessed on English, multilingual, and coding tasks: it
outperforms all existing similarly-sized open models on 4 out of 7 downstream
evaluation ar... | 2024-02-26T18:43:45Z | null | null | null | null | null | null | null | null | null | null |
2,402.16829 | GISTEmbed: Guided In-sample Selection of Training Negatives for Text
Embedding Fine-tuning | ['Aivin V. Solatorio'] | ['cs.LG', 'cs.CL'] | Embedding models are integral to AI applications like semantic search,
personalized recommendations, and retrieval augmented generation for LLMs,
necessitating high-quality training data. However, the limited scalability of
manual data curation prompts the need for automated methods to ensure data
integrity. Traditiona... | 2024-02-26T18:55:15Z | GISTEmbed GitHub repository at
https://github.com/avsolatorio/GISTEmbed | null | null | GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning | ['Aivin V. Solatorio'] | 2,024 | arXiv.org | 24 | 31 | ['Computer Science'] |
2,402.1684 | MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT | ['Omkar Thawakar', 'Ashmal Vayani', 'Salman Khan', 'Hisham Cholakal', 'Rao M. Anwer', 'Michael Felsberg', 'Tim Baldwin', 'Eric P. Xing', 'Fahad Shahbaz Khan'] | ['cs.CL'] | "Bigger the better" has been the predominant trend in recent Large Language
Models (LLMs) development. However, LLMs do not suit well for scenarios that
require on-device processing, energy efficiency, low memory footprint, and
response efficiency. These requisites are crucial for privacy, security, and
sustainable dep... | 2024-02-26T18:59:03Z | Code available at : https://github.com/mbzuai-oryx/MobiLlama | null | null | null | null | null | null | null | null | null |
2,402.16918 | m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers | ['Ka Man Lo', 'Yiming Liang', 'Wenyu Du', 'Yuantao Fan', 'Zili Wang', 'Wenhao Huang', 'Lei Ma', 'Jie Fu'] | ['cs.LG', 'cs.CV'] | Modular neural architectures are gaining attention for their powerful
generalization and efficient adaptation to new domains. However, training these
models poses challenges due to optimization difficulties arising from intrinsic
sparse connectivity. Leveraging knowledge from monolithic models through
techniques like k... | 2024-02-26T04:47:32Z | null | null | null | m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers | ['Ka Man Lo', 'Yiming Liang', 'Wenyu Du', 'Yuantao Fan', 'Zili Wang', 'Wenhao Huang', 'Lei Ma', 'Jie Fu'] | 2,024 | arXiv.org | 2 | 45 | ['Computer Science'] |
2,402.16928 | CLAP: Learning Transferable Binary Code Representations with Natural
Language Supervision | ['Hao Wang', 'Zeyu Gao', 'Chao Zhang', 'Zihan Sha', 'Mingyang Sun', 'Yuchen Zhou', 'Wenyu Zhu', 'Wenju Sun', 'Han Qiu', 'Xi Xiao'] | ['cs.SE', 'cs.AI'] | Binary code representation learning has shown significant performance in
binary analysis tasks. But existing solutions often have poor transferability,
particularly in few-shot and zero-shot scenarios where few or no training
samples are available for the tasks. To address this problem, we present CLAP
(Contrastive Lan... | 2024-02-26T13:49:52Z | null | null | null | null | null | null | null | null | null | null |
2,402.17016 | Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings | ['Isabelle Mohr', 'Markus Krimmel', 'Saba Sturua', 'Mohammad Kalim Akram', 'Andreas Koukounas', 'Michael Günther', 'Georgios Mastrapas', 'Vinit Ravishankar', 'Joan Fontanals Martínez', 'Feng Wang', 'Qi Liu', 'Ziniu Yu', 'Jie Fu', 'Saahil Ognawala', 'Susana Guzman', 'Bo Wang', 'Maximilian Werk', 'Nan Wang', 'Han Xiao'] | ['cs.CL', 'cs.AI', 'cs.IR', '68T50', 'I.2.7'] | We introduce a novel suite of state-of-the-art bilingual text embedding
models that are designed to support English and another target language. These
models are capable of processing lengthy text inputs with up to 8192 tokens,
making them highly versatile for a range of natural language processing tasks
such as text r... | 2024-02-26T20:53:12Z | null | null | null | null | null | null | null | null | null | null |
2,402.17113 | Transparent Image Layer Diffusion using Latent Transparency | ['Lvmin Zhang', 'Maneesh Agrawala'] | ['cs.CV', 'cs.GR'] | We present LayerDiffuse, an approach enabling large-scale pretrained latent
diffusion models to generate transparent images. The method allows generation
of single transparent images or of multiple transparent layers. The method
learns a "latent transparency" that encodes alpha channel transparency into the
latent mani... | 2024-02-27T01:19:53Z | 44 pages, 37 figures, github.com/layerdiffusion/LayerDiffuse | null | null | null | null | null | null | null | null | null |
2,402.17245 | Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
Text-to-Image Generation | ['Daiqing Li', 'Aleks Kamko', 'Ehsan Akhgari', 'Ali Sabet', 'Linmiao Xu', 'Suhail Doshi'] | ['cs.CV', 'cs.AI'] | In this work, we share three insights for achieving state-of-the-art
aesthetic quality in text-to-image generative models. We focus on three
critical aspects for model improvement: enhancing color and contrast, improving
generation across multiple aspect ratios, and improving human-centric fine
details. First, we delve... | 2024-02-27T06:31:52Z | Model weights:
https://huggingface.co/playgroundai/playground-v2.5-1024px-aesthetic | null | null | null | null | null | null | null | null | null |
2,402.173 | VoCo: A Simple-yet-Effective Volume Contrastive Learning Framework for
3D Medical Image Analysis | ['Linshan Wu', 'Jiaxin Zhuang', 'Hao Chen'] | ['eess.IV'] | Self-Supervised Learning (SSL) has demonstrated promising results in 3D
medical image analysis. However, the lack of high-level semantics in
pre-training still heavily hinders the performance of downstream tasks. We
observe that 3D medical images contain relatively consistent contextual
position information, i.e., cons... | 2024-02-27T08:22:55Z | Accepted by CVPR 2024. The camera-ready version will soon be
available | null | null | null | null | null | null | null | null | null |
2,402.17497 | REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain
Question Answering | ['Yuhao Wang', 'Ruiyang Ren', 'Junyi Li', 'Wayne Xin Zhao', 'Jing Liu', 'Ji-Rong Wen'] | ['cs.CL', 'cs.IR'] | Considering the limited internal parametric knowledge, retrieval-augmented
generation (RAG) has been widely used to extend the knowledge scope of large
language models (LLMs). Despite the extensive efforts on RAG research, in
existing methods, LLMs cannot precisely assess the relevance of retrieved
documents, thus like... | 2024-02-27T13:22:51Z | Accepted to EMNLP 2024 Main Conference. Published on ACL Anthology:
https://aclanthology.org/2024.emnlp-main.321.pdf | null | null | null | null | null | null | null | null | null |
2,402.17645 | SongComposer: A Large Language Model for Lyric and Melody Generation in
Song Composition | ['Shuangrui Ding', 'Zihan Liu', 'Xiaoyi Dong', 'Pan Zhang', 'Rui Qian', 'Junhao Huang', 'Conghui He', 'Dahua Lin', 'Jiaqi Wang'] | ['cs.SD', 'cs.AI', 'cs.CL', 'eess.AS'] | Creating lyrics and melodies for the vocal track in a symbolic format, known
as song composition, demands expert musical knowledge of melody, an advanced
understanding of lyrics, and precise alignment between them. Despite
achievements in sub-tasks such as lyric generation, lyric-to-melody, and
melody-to-lyric, etc, a ... | 2024-02-27T16:15:28Z | ACL 2025 main. project page: https://pjlab-songcomposer.github.io/
code: https://github.com/pjlab-songcomposer/songcomposer | null | null | SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition | ['Shuangrui Ding', 'Zihan Liu', 'Xiao-wen Dong', 'Pan Zhang', 'Rui Qian', 'Junhao Huang', 'Conghui He', 'Dahua Lin', 'Jiaqi Wang'] | 2,024 | null | 1 | 50 | ['Computer Science', 'Engineering'] |
2,402.1766 | TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular
Simulations | ['Raul P. Pelaez', 'Guillem Simeon', 'Raimondas Galvelis', 'Antonio Mirarchi', 'Peter Eastman', 'Stefan Doerr', 'Philipp Thölke', 'Thomas E. Markland', 'Gianni De Fabritiis'] | ['cs.LG', 'physics.bio-ph', 'physics.chem-ph', 'physics.comp-ph'] | Achieving a balance between computational speed, prediction accuracy, and
universal applicability in molecular simulations has been a persistent
challenge. This paper presents substantial advancements in the TorchMD-Net
software, a pivotal step forward in the shift from conventional force fields to
neural network-based... | 2024-02-27T16:27:06Z | Version accepted in Journal of Chemical Theory and Computation | null | 10.1021/acs.jctc.4c00253 | null | null | null | null | null | null | null |
2,402.17701 | Real-time Low-latency Music Source Separation using Hybrid
Spectrogram-TasNet | ['Satvik Venkatesh', 'Arthur Benilov', 'Philip Coleman', 'Frederic Roskam'] | ['eess.AS', 'cs.LG', 'cs.SD', 'I.5.1; I.5.4'] | There have been significant advances in deep learning for music demixing in
recent years. However, there has been little attention given to how these
neural networks can be adapted for real-time low-latency applications, which
could be helpful for hearing aids, remixing audio streams and live shows. In
this paper, we i... | 2024-02-27T17:26:33Z | Accepted to ICASSP 2024 | null | null | Real-Time Low-Latency Music Source Separation Using Hybrid Spectrogram-Tasnet | ['Satvik Venkatesh', 'Arthur Benilov', 'Philip Coleman', 'Frederic Roskam'] | 2,024 | IEEE International Conference on Acoustics, Speech, and Signal Processing | 6 | 37 | ['Engineering', 'Computer Science'] |
2,402.17733 | Tower: An Open Multilingual Large Language Model for Translation-Related
Tasks | ['Duarte M. Alves', 'José Pombal', 'Nuno M. Guerreiro', 'Pedro H. Martins', 'João Alves', 'Amin Farajian', 'Ben Peters', 'Ricardo Rei', 'Patrick Fernandes', 'Sweta Agrawal', 'Pierre Colombo', 'José G. C. de Souza', 'André F. T. Martins'] | ['cs.CL'] | While general-purpose large language models (LLMs) demonstrate proficiency on
multiple tasks within the domain of translation, approaches based on open LLMs
are competitive only when specializing on a single task. In this paper, we
propose a recipe for tailoring LLMs to multiple tasks present in translation
workflows. ... | 2024-02-27T18:09:36Z | null | null | null | Tower: An Open Multilingual Large Language Model for Translation-Related Tasks | ['Duarte M. Alves', 'José P. Pombal', 'Nuno M. Guerreiro', 'P. Martins', 'João Alves', 'Amin Farajian', 'Ben Peters', 'Ricardo Rei', 'Patrick Fernandes', 'Sweta Agrawal', 'Pierre Colombo', 'José G. C. de Souza', 'André Martins'] | 2,024 | arXiv.org | 157 | 96 | ['Computer Science'] |
2,402.17764 | The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits | ['Shuming Ma', 'Hongyu Wang', 'Lingxiao Ma', 'Lei Wang', 'Wenhui Wang', 'Shaohan Huang', 'Li Dong', 'Ruiping Wang', 'Jilong Xue', 'Furu Wei'] | ['cs.CL', 'cs.LG'] | Recent research, such as BitNet, is paving the way for a new era of 1-bit
Large Language Models (LLMs). In this work, we introduce a 1-bit LLM variant,
namely BitNet b1.58, in which every single parameter (or weight) of the LLM is
ternary {-1, 0, 1}. It matches the full-precision (i.e., FP16 or BF16)
Transformer LLM wi... | 2024-02-27T18:56:19Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,402.17766 | ShapeLLM: Universal 3D Object Understanding for Embodied Interaction | ['Zekun Qi', 'Runpei Dong', 'Shaochen Zhang', 'Haoran Geng', 'Chunrui Han', 'Zheng Ge', 'Li Yi', 'Kaisheng Ma'] | ['cs.CV'] | This paper presents ShapeLLM, the first 3D Multimodal Large Language Model
(LLM) designed for embodied interaction, exploring a universal 3D object
understanding with 3D point clouds and languages. ShapeLLM is built upon an
improved 3D encoder by extending ReCon to ReCon++ that benefits from multi-view
image distillati... | 2024-02-27T18:57:12Z | Accepted at ECCV 2024 | null | null | null | null | null | null | null | null | null |
2,402.1781 | BioT5+: Towards Generalized Biological Understanding with IUPAC
Integration and Multi-task Tuning | ['Qizhi Pei', 'Lijun Wu', 'Kaiyuan Gao', 'Xiaozhuan Liang', 'Yin Fang', 'Jinhua Zhu', 'Shufang Xie', 'Tao Qin', 'Rui Yan'] | ['q-bio.QM', 'cs.AI', 'cs.CE', 'cs.LG', 'q-bio.BM'] | Recent research trends in computational biology have increasingly focused on
integrating text and bio-entity modeling, especially in the context of
molecules and proteins. However, previous efforts like BioT5 faced challenges
in generalizing across diverse tasks and lacked a nuanced understanding of
molecular structure... | 2024-02-27T12:43:09Z | Accepted by ACL 2024 (Findings) | null | null | null | null | null | null | null | null | null |
2,402.17811 | TruthX: Alleviating Hallucinations by Editing Large Language Models in
Truthful Space | ['Shaolei Zhang', 'Tian Yu', 'Yang Feng'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models (LLMs) sometimes suffer from producing hallucinations,
especially LLMs may generate untruthful responses despite knowing the correct
knowledge. Activating the truthfulness within LLM is the key to fully unlocking
LLM's knowledge potential. In this paper, we propose TruthX, an inference-time
interv... | 2024-02-27T14:45:04Z | Accepted to ACL 2024 main conference, Project Page:
https://ictnlp.github.io/TruthX-site/ | null | null | null | null | null | null | null | null | null |
2,402.17834 | Stable LM 2 1.6B Technical Report | ['Marco Bellagente', 'Jonathan Tow', 'Dakota Mahan', 'Duy Phung', 'Maksym Zhuravinskyi', 'Reshinth Adithyan', 'James Baicoianu', 'Ben Brooks', 'Nathan Cooper', 'Ashish Datta', 'Meng Lee', 'Emad Mostaque', 'Michael Pieler', 'Nikhil Pinnaparju', 'Paulo Rocha', 'Harry Saini', 'Hannah Teufel', 'Niccolo Zanichelli', 'Carlos... | ['cs.CL', 'stat.ML'] | We introduce StableLM 2 1.6B, the first in a new generation of our language
model series. In this technical report, we present in detail the data and
training procedure leading to the base and instruction-tuned versions of
StableLM 2 1.6B. The weights for both models are available via Hugging Face for
anyone to downloa... | 2024-02-27T19:00:07Z | 23 pages, 6 figures | null | null | null | null | null | null | null | null | null |
2,402.17916 | Adversarial Math Word Problem Generation | ['Roy Xie', 'Chengxuan Huang', 'Junlin Wang', 'Bhuwan Dhingra'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) have significantly transformed the educational
landscape. As current plagiarism detection tools struggle to keep pace with
LLMs' rapid advancements, the educational community faces the challenge of
assessing students' true problem-solving abilities in the presence of LLMs. In
this work, we ... | 2024-02-27T22:07:52Z | Code/data: https://github.com/ruoyuxie/adversarial_mwps_generation | null | null | null | null | null | null | null | null | null |
2,402.17946 | SparseLLM: Towards Global Pruning for Pre-trained Language Models | ['Guangji Bai', 'Yijiang Li', 'Chen Ling', 'Kibaek Kim', 'Liang Zhao'] | ['cs.CL'] | The transformative impact of large language models (LLMs) like LLaMA and GPT
on natural language processing is countered by their prohibitive computational
demands. Pruning has emerged as a pivotal compression strategy, introducing
sparsity to enhance both memory and computational efficiency. Yet, traditional
global pr... | 2024-02-28T00:09:07Z | NeurIPS 2024 | null | null | SparseLLM: Towards Global Pruning of Pre-trained Language Models | ['Guangji Bai', 'Yijiang Li', 'Chen Ling', 'Kibaek Kim', 'Liang Zhao'] | 2,024 | Neural Information Processing Systems | 11 | 40 | ['Computer Science'] |
2,402.1806 | Benchmarking Large Language Models on Answering and Explaining
Challenging Medical Questions | ['Hanjie Chen', 'Zhouxiang Fang', 'Yash Singla', 'Mark Dredze'] | ['cs.CL'] | LLMs have demonstrated impressive performance in answering medical questions,
such as achieving passing scores on medical licensing examinations. However,
medical board exams or general clinical questions do not capture the complexity
of realistic clinical cases. Moreover, the lack of reference explanations means
we ca... | 2024-02-28T05:44:41Z | NAACL 2025 | null | null | null | null | null | null | null | null | null |
2,402.18153 | Diffusion-Based Neural Network Weights Generation | ['Bedionita Soro', 'Bruno Andreis', 'Hayeon Lee', 'Wonyong Jeong', 'Song Chong', 'Frank Hutter', 'Sung Ju Hwang'] | ['cs.LG', 'cs.AI'] | Transfer learning has gained significant attention in recent deep learning
research due to its ability to accelerate convergence and enhance performance
on new tasks. However, its success is often contingent on the similarity
between source and target data, and training on numerous datasets can be
costly, leading to bl... | 2024-02-28T08:34:23Z | 32 pages | null | null | null | null | null | null | null | null | null |
2,402.18191 | Clustering and Ranking: Diversity-preserved Instruction Selection
through Expert-aligned Quality Estimation | ['Yuan Ge', 'Yilun Liu', 'Chi Hu', 'Weibin Meng', 'Shimin Tao', 'Xiaofeng Zhao', 'Hongxia Ma', 'Li Zhang', 'Boxing Chen', 'Hao Yang', 'Bei Li', 'Tong Xiao', 'Jingbo Zhu'] | ['cs.CL'] | With contributions from the open-source community, a vast amount of
instruction tuning (IT) data has emerged. Given the significant resource
allocation required for training and evaluating models, it is advantageous to
have an efficient method for selecting high-quality IT data. However, existing
methods for instructio... | 2024-02-28T09:27:29Z | Accepted by EMNLP2024 | https://aclanthology.org/2024.emnlp-main.28/ | null | null | null | null | null | null | null | null |
2,402.18329 | Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse
Shells | ['Dmitrijs Trizna', 'Luca Demetrio', 'Battista Biggio', 'Fabio Roli'] | ['cs.CR', 'cs.LG'] | Living-off-the-land (LOTL) techniques pose a significant challenge to
security operations, exploiting legitimate tools to execute malicious commands
that evade traditional detection methods. To address this, we present a robust
augmentation framework for cyber defense systems as Security Information and
Event Managemen... | 2024-02-28T13:49:23Z | null | null | null | null | null | null | null | null | null | null |
2,402.18334 | Learning to Generate Instruction Tuning Datasets for Zero-Shot Task
Adaptation | ['Nihal V. Nayak', 'Yiyang Nan', 'Avi Trost', 'Stephen H. Bach'] | ['cs.CL', 'cs.LG'] | We introduce Bonito, an open-source model for conditional task generation
that converts unannotated text into task-specific training datasets for
instruction tuning. We aim to enable zero-shot task adaptation of large
language models on users' specialized, private data. We train Bonito by
fine-tuning a pretrained large... | 2024-02-28T13:54:57Z | ACL Findings 2024 | null | null | null | null | null | null | null | null | null |
2,402.18381 | Large Language Models As Evolution Strategies | ['Robert Tjarko Lange', 'Yingtao Tian', 'Yujin Tang'] | ['cs.AI', 'cs.LG', 'cs.NE'] | Large Transformer models are capable of implementing a plethora of so-called
in-context learning algorithms. These include gradient descent, classification,
sequence completion, transformation, and improvement. In this work, we
investigate whether large language models (LLMs), which never explicitly
encountered the tas... | 2024-02-28T15:02:17Z | 11 pages, 14 figures | null | null | null | null | null | null | null | null | null |
2,402.18567 | Diffusion Language Models Are Versatile Protein Learners | ['Xinyou Wang', 'Zaixiang Zheng', 'Fei Ye', 'Dongyu Xue', 'Shujian Huang', 'Quanquan Gu'] | ['cs.LG', 'q-bio.BM'] | This paper introduces diffusion protein language model (DPLM), a versatile
protein language model that demonstrates strong generative and predictive
capabilities for protein sequences. We first pre-train scalable DPLMs from
evolutionary-scale protein sequences within a generative self-supervised
discrete diffusion prob... | 2024-02-28T18:57:56Z | ICML 2024 camera-ready version | null | null | Diffusion Language Models Are Versatile Protein Learners | ['Xinyou Wang', 'Zaixiang Zheng', 'Fei Ye', 'Dongyu Xue', 'Shujian Huang', 'Quanquan Gu'] | 2,024 | International Conference on Machine Learning | 50 | 0 | ['Computer Science', 'Biology'] |
2,402.18571 | Arithmetic Control of LLMs for Diverse User Preferences: Directional
Preference Alignment with Multi-Objective Rewards | ['Haoxiang Wang', 'Yong Lin', 'Wei Xiong', 'Rui Yang', 'Shizhe Diao', 'Shuang Qiu', 'Han Zhao', 'Tong Zhang'] | ['cs.LG', 'cs.AI', 'cs.CL', 'stat.ML'] | Fine-grained control over large language models (LLMs) remains a significant
challenge, hindering their adaptability to diverse user needs. While
Reinforcement Learning from Human Feedback (RLHF) shows promise in aligning
LLMs, its reliance on scalar rewards often limits its ability to capture
diverse user preferences ... | 2024-02-28T18:58:25Z | The code and model are released at
https://github.com/Haoxiang-Wang/directional-preference-alignment | null | null | null | null | null | null | null | null | null |
2,402.18589 | Verif.ai: Towards an Open-Source Scientific Generative
Question-Answering System with Referenced and Verifiable Answers | ['Miloš Košprdić', 'Adela Ljajić', 'Bojana Bašaragin', 'Darija Medvecki', 'Nikola Milošević'] | ['cs.IR', 'cs.AI', 'cs.CL', 'cs.LG'] | In this paper, we present the current progress of the project Verif.ai, an
open-source scientific generative question-answering system with referenced and
verified answers. The components of the system are (1) an information retrieval
system combining semantic and lexical search techniques over scientific papers
(PubMe... | 2024-02-09T10:25:01Z | Accepted as a short paper at The Sixteenth International Conference
on Evolving Internet (INTERNET 2024) | The Sixteenth International Conference on Evolving Internet
(INTERNET 2024) | null | Verif.ai: Towards an Open-Source Scientific Generative Question-Answering System with Referenced and Verifiable Answers | ['Milos Kosprdic', 'Adela Ljajić', 'Bojana Bašaragin', 'Darija Medvecki', 'Nikola Milosevic'] | 2,024 | arXiv.org | 3 | 15 | ['Computer Science'] |
2,402.18668 | Simple linear attention language models balance the recall-throughput
tradeoff | ['Simran Arora', 'Sabri Eyuboglu', 'Michael Zhang', 'Aman Timalsina', 'Silas Alberti', 'Dylan Zinsley', 'James Zou', 'Atri Rudra', 'Christopher Ré'] | ['cs.CL', 'cs.LG'] | Recent work has shown that attention-based language models excel at recall,
the ability to ground generations in tokens previously seen in context.
However, the efficiency of attention-based models is bottle-necked during
inference by the KV-cache's aggressive memory consumption. In this work, we
explore whether we can... | 2024-02-28T19:28:27Z | null | null | null | null | null | null | null | null | null | null |
2,402.18766 | Advancing Generative AI for Portuguese with Open Decoder Gervásio PT* | ['Rodrigo Santos', 'João Silva', 'Luís Gomes', 'João Rodrigues', 'António Branco'] | ['cs.CL'] | To advance the neural decoding of Portuguese, in this paper we present a
fully open Transformer-based, instruction-tuned decoder model that sets a new
state of the art in this respect. To develop this decoder, which we named
Gerv\'asio PT*, a strong LLaMA~2 7B model was used as a starting point, and its
further improve... | 2024-02-29T00:19:13Z | null | null | null | null | null | null | null | null | null | null |
2,402.18848 | SwitchLight: Co-design of Physics-driven Architecture and Pre-training
Framework for Human Portrait Relighting | ['Hoon Kim', 'Minje Jang', 'Wonjun Yoon', 'Jisoo Lee', 'Donghyun Na', 'Sanghyun Woo'] | ['cs.CV'] | We introduce a co-designed approach for human portrait relighting that
combines a physics-guided architecture with a pre-training framework. Drawing
on the Cook-Torrance reflectance model, we have meticulously configured the
architecture design to precisely simulate light-surface interactions.
Furthermore, to overcome ... | 2024-02-29T04:52:04Z | CVPR2024. Live demos available at https://www.beeble.ai/ | null | null | null | null | null | null | null | null | null |
2,402.19043 | WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image
Synthesis | ['Paul Friedrich', 'Julia Wolleb', 'Florentin Bieder', 'Alicia Durrer', 'Philippe C. Cattin'] | ['eess.IV', 'cs.CV'] | Due to the three-dimensional nature of CT- or MR-scans, generative modeling
of medical images is a particularly challenging task. Existing approaches
mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit
the high-dimensional data into the limited GPU memory. However, these
approaches may introdu... | 2024-02-29T11:11:05Z | Accepted at DGM4MICCAI 2024. Project page:
https://pfriedri.github.io/wdm-3d-io Code: https://github.com/pfriedri/wdm-3d | null | 10.1007/978-3-031-72744-3_2 | WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis | ['Paul Friedrich', 'Julia Wolleb', 'Florentin Bieder', 'Alicia Durrer', 'Philippe C. Cattin'] | 2,024 | DGM4MICCAI@MICCAI | 20 | 37 | ['Engineering', 'Computer Science'] |
2,402.19155 | Beyond Language Models: Byte Models are Digital World Simulators | ['Shangda Wu', 'Xu Tan', 'Zili Wang', 'Rui Wang', 'Xiaobing Li', 'Maosong Sun'] | ['cs.LG'] | Traditional deep learning often overlooks bytes, the basic units of the
digital world, where all forms of information and operations are encoded and
manipulated in binary format. Inspired by the success of next token prediction
in natural language processing, we introduce bGPT, a model with next byte
prediction to simu... | 2024-02-29T13:38:07Z | 19 pages, 5 figures, 5 tables | null | null | null | null | null | null | null | null | null |
2,402.19159 | Trajectory Consistency Distillation: Improved Latent Consistency
Distillation by Semi-Linear Consistency Function with Trajectory Mapping | ['Jianbin Zheng', 'Minghui Hu', 'Zhongyi Fan', 'Chaoyue Wang', 'Changxing Ding', 'Dacheng Tao', 'Tat-Jen Cham'] | ['cs.CV'] | Latent Consistency Model (LCM) extends the Consistency Model to the latent
space and leverages the guided consistency distillation technique to achieve
impressive performance in accelerating text-to-image synthesis. However, we
observed that LCM struggles to generate images with both clarity and detailed
intricacy. Con... | 2024-02-29T13:44:14Z | Project Page: https://mhh0318.github.io/tcd | null | null | Trajectory Consistency Distillation | ['Jianbin Zheng', 'Minghui Hu', 'Zhongyi Fan', 'Chaoyue Wang', 'Changxing Ding', 'Dacheng Tao', 'Tat-Jen Cham'] | 2,024 | arXiv.org | 30 | 56 | ['Computer Science'] |
2,402.19173 | StarCoder 2 and The Stack v2: The Next Generation | ['Anton Lozhkov', 'Raymond Li', 'Loubna Ben Allal', 'Federico Cassano', 'Joel Lamy-Poirier', 'Nouamane Tazi', 'Ao Tang', 'Dmytro Pykhtar', 'Jiawei Liu', 'Yuxiang Wei', 'Tianyang Liu', 'Max Tian', 'Denis Kocetkov', 'Arthur Zucker', 'Younes Belkada', 'Zijian Wang', 'Qian Liu', 'Dmitry Abulkhanov', 'Indraneil Paul', 'Zhua... | ['cs.SE', 'cs.AI'] | The BigCode project, an open-scientific collaboration focused on the
responsible development of Large Language Models for Code (Code LLMs),
introduces StarCoder2. In partnership with Software Heritage (SWH), we build
The Stack v2 on top of the digital commons of their source code archive.
Alongside the SWH repositories... | 2024-02-29T13:53:35Z | null | null | null | null | null | null | null | null | null | null |
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