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2,410.14309 | LoGU: Long-form Generation with Uncertainty Expressions | ['Ruihan Yang', 'Caiqi Zhang', 'Zhisong Zhang', 'Xinting Huang', 'Sen Yang', 'Nigel Collier', 'Dong Yu', 'Deqing Yang'] | ['cs.CL', 'cs.AI'] | While Large Language Models (LLMs) demonstrate impressive capabilities, they
still struggle with generating factually incorrect content (i.e.,
hallucinations). A promising approach to mitigate this issue is enabling models
to express uncertainty when unsure. Previous research on uncertainty modeling
has primarily focus... | 2024-10-18T09:15:35Z | ACL 2025 Main | null | null | LoGU: Long-form Generation with Uncertainty Expressions | ['Ruihan Yang', 'Caiqi Zhang', 'Zhisong Zhang', 'Xinting Huang', 'Sen Yang', 'Nigel Collier', 'Dong Yu', 'Deqing Yang'] | 2,024 | arXiv.org | 9 | 48 | ['Computer Science'] |
2,410.14324 | HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image
Generation | ['Bo Cheng', 'Yuhang Ma', 'Liebucha Wu', 'Shanyuan Liu', 'Ao Ma', 'Xiaoyu Wu', 'Dawei Leng', 'Yuhui Yin'] | ['cs.CV'] | The task of layout-to-image generation involves synthesizing images based on
the captions of objects and their spatial positions. Existing methods still
struggle in complex layout generation, where common bad cases include object
missing, inconsistent lighting, conflicting view angles, etc. To effectively
address these... | 2024-10-18T09:36:10Z | NeurIPS2024 | null | null | null | null | null | null | null | null | null |
2,410.14464 | Electrocardiogram-Language Model for Few-Shot Question Answering with
Meta Learning | ['Jialu Tang', 'Tong Xia', 'Yuan Lu', 'Cecilia Mascolo', 'Aaqib Saeed'] | ['cs.LG'] | Electrocardiogram (ECG) interpretation requires specialized expertise, often
involving synthesizing insights from ECG signals with complex clinical queries
posed in natural language. The scarcity of labeled ECG data coupled with the
diverse nature of clinical inquiries presents a significant challenge for
developing ro... | 2024-10-18T13:48:01Z | Accepted at AHLI CHIL 2025 | null | null | null | null | null | null | null | null | null |
2,410.14596 | Teaching Models to Balance Resisting and Accepting Persuasion | ['Elias Stengel-Eskin', 'Peter Hase', 'Mohit Bansal'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) are susceptible to persuasion, which can pose
risks when models are faced with an adversarial interlocutor. We take a first
step towards defending models against persuasion while also arguing that
defense against adversarial (i.e. negative) persuasion is only half of the
equation: models sh... | 2024-10-18T16:49:36Z | NAACL Camera-Ready. Code:
https://github.com/esteng/persuasion_balanced_training | null | null | Teaching Models to Balance Resisting and Accepting Persuasion | ['Elias Stengel-Eskin', 'Peter Hase', 'Mohit Bansal'] | 2,024 | North American Chapter of the Association for Computational Linguistics | 5 | 45 | ['Computer Science'] |
2,410.14609 | DiSCo: LLM Knowledge Distillation for Efficient Sparse Retrieval in
Conversational Search | ['Simon Lupart', 'Mohammad Aliannejadi', 'Evangelos Kanoulas'] | ['cs.IR', 'cs.CL'] | Conversational Search (CS) involves retrieving relevant documents from a
corpus while considering the conversational context, integrating retrieval with
context modeling. Recent advancements in Large Language Models (LLMs) have
significantly enhanced CS by enabling query rewriting based on conversational
context. Howev... | 2024-10-18T17:03:17Z | 11 pages, 6 figures. SIGIR '25 Proceedings of the 48th International
ACM SIGIR Conference on Research and Development in Information Retrieval
July 13--18, 2025 Padua, Italy | null | null | null | null | null | null | null | null | null |
2,410.14633 | Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation
Models for Multi-Task Learning | ['Yuxiang Lu', 'Shengcao Cao', 'Yu-Xiong Wang'] | ['cs.CV'] | Vision Foundation Models (VFMs) have demonstrated outstanding performance on
numerous downstream tasks. However, due to their inherent representation biases
originating from different training paradigms, VFMs exhibit advantages and
disadvantages across distinct vision tasks. Although amalgamating the strengths
of multi... | 2024-10-18T17:32:39Z | Accepted by ICLR2025 | null | null | null | null | null | null | null | null | null |
2,410.14672 | BiGR: Harnessing Binary Latent Codes for Image Generation and Improved
Visual Representation Capabilities | ['Shaozhe Hao', 'Xuantong Liu', 'Xianbiao Qi', 'Shihao Zhao', 'Bojia Zi', 'Rong Xiao', 'Kai Han', 'Kwan-Yee K. Wong'] | ['cs.CV', 'cs.AI'] | We introduce BiGR, a novel conditional image generation model using compact
binary latent codes for generative training, focusing on enhancing both
generation and representation capabilities. BiGR is the first conditional
generative model that unifies generation and discrimination within the same
framework. BiGR featur... | 2024-10-18T17:59:04Z | Updated with additional T2I results; Project page:
https://haoosz.github.io/BiGR | null | null | null | null | null | null | null | null | null |
2,410.14675 | To Trust or Not to Trust? Enhancing Large Language Models' Situated
Faithfulness to External Contexts | ['Yukun Huang', 'Sanxing Chen', 'Hongyi Cai', 'Bhuwan Dhingra'] | ['cs.CL', 'cs.AI'] | Large Language Models (LLMs) are often augmented with external contexts, such
as those used in retrieval-augmented generation (RAG). However, these contexts
can be inaccurate or intentionally misleading, leading to conflicts with the
model's internal knowledge. We argue that robust LLMs should demonstrate
situated fait... | 2024-10-18T17:59:47Z | null | null | null | To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts | ['Yukun Huang', 'Sanxing Chen', 'H. Cai', 'Bhuwan Dhingra'] | 2,024 | International Conference on Learning Representations | 4 | 34 | ['Computer Science'] |
2,410.14687 | BrainTransformers: SNN-LLM | ['Zhengzheng Tang', 'Eva Zhu'] | ['cs.NE', 'cs.CL', 'cs.LG'] | This study introduces BrainTransformers, an innovative Large Language Model
(LLM) implemented using Spiking Neural Networks (SNN). Our key contributions
include: (1) designing SNN-compatible Transformer components such as SNNMatmul,
SNNSoftmax, and SNNSiLU; (2) implementing an SNN approximation of the SiLU
activation f... | 2024-10-03T14:17:43Z | null | null | null | BrainTransformers: SNN-LLM | ['Zhengzheng Tang', 'Eva Zhu'] | 2,024 | arXiv.org | 1 | 15 | ['Computer Science'] |
2,410.14735 | Agent Skill Acquisition for Large Language Models via CycleQD | ['So Kuroki', 'Taishi Nakamura', 'Takuya Akiba', 'Yujin Tang'] | ['cs.CL', 'cs.AI', 'cs.NE'] | Training large language models to acquire specific skills remains a
challenging endeavor. Conventional training approaches often struggle with data
distribution imbalances and inadequacies in objective functions that do not
align well with task-specific performance. To address these challenges, we
introduce CycleQD, a ... | 2024-10-16T20:27:15Z | To appear at the 13th International Conference on Learning
Representations (ICLR 2025) | null | null | null | null | null | null | null | null | null |
2,410.14745 | Semi-supervised Fine-tuning for Large Language Models | ['Junyu Luo', 'Xiao Luo', 'Xiusi Chen', 'Zhiping Xiao', 'Wei Ju', 'Ming Zhang'] | ['cs.CL', 'cs.AI'] | Supervised fine-tuning (SFT) is crucial in adapting large language model
(LLMs) to a specific domain or task. However, only a limited amount of labeled
data is available in practical applications, which poses a severe challenge for
SFT in yielding satisfactory results. Therefore, a data-efficient framework
that can ful... | 2024-10-17T16:59:46Z | Github Repo: https://github.com/luo-junyu/SemiEvol | NAACL 2025 | null | Semi-supervised Fine-tuning for Large Language Models | ['Junyu Luo', 'Xiao Luo', 'Xiusi Chen', 'Zhiping Xiao', 'Wei Ju', 'Ming Zhang'] | 2,024 | North American Chapter of the Association for Computational Linguistics | 1 | 65 | ['Computer Science'] |
2,410.14815 | Adapting Multilingual LLMs to Low-Resource Languages using Continued
Pre-training and Synthetic Corpus | ['Raviraj Joshi', 'Kanishk Singla', 'Anusha Kamath', 'Raunak Kalani', 'Rakesh Paul', 'Utkarsh Vaidya', 'Sanjay Singh Chauhan', 'Niranjan Wartikar', 'Eileen Long'] | ['cs.CL', 'cs.LG'] | Multilingual LLMs support a variety of languages; however, their performance
is suboptimal for low-resource languages. In this work, we emphasize the
importance of continued pre-training of multilingual LLMs and the use of
translation-based synthetic pre-training corpora for improving LLMs in
low-resource languages. We... | 2024-10-18T18:35:19Z | null | null | null | null | null | null | null | null | null | null |
2,410.15027 | Group Diffusion Transformers are Unsupervised Multitask Learners | ['Lianghua Huang', 'Wei Wang', 'Zhi-Fan Wu', 'Huanzhang Dou', 'Yupeng Shi', 'Yutong Feng', 'Chen Liang', 'Yu Liu', 'Jingren Zhou'] | ['cs.CV'] | While large language models (LLMs) have revolutionized natural language
processing with their task-agnostic capabilities, visual generation tasks such
as image translation, style transfer, and character customization still rely
heavily on supervised, task-specific datasets. In this work, we introduce Group
Diffusion Tr... | 2024-10-19T07:53:15Z | null | null | null | null | null | null | null | null | null | null |
2,410.15148 | Less is More: Parameter-Efficient Selection of Intermediate Tasks for
Transfer Learning | ['David Schulte', 'Felix Hamborg', 'Alan Akbik'] | ['cs.CL', 'cs.LG'] | Intermediate task transfer learning can greatly improve model performance.
If, for example, one has little training data for emotion detection, first
fine-tuning a language model on a sentiment classification dataset may improve
performance strongly. But which task to choose for transfer learning? Prior
methods produci... | 2024-10-19T16:22:04Z | EMNLP 2024 Main Conference | null | null | null | null | null | null | null | null | null |
2,410.15308 | LlamaLens: Specialized Multilingual LLM for Analyzing News and Social
Media Content | ['Mohamed Bayan Kmainasi', 'Ali Ezzat Shahroor', 'Maram Hasanain', 'Sahinur Rahman Laskar', 'Naeemul Hassan', 'Firoj Alam'] | ['cs.CL', 'cs.AI', '68T50', 'F.2.2; I.2.7'] | Large Language Models (LLMs) have demonstrated remarkable success as
general-purpose task solvers across various fields. However, their capabilities
remain limited when addressing domain-specific problems, particularly in
downstream NLP tasks. Research has shown that models fine-tuned on
instruction-based downstream NL... | 2024-10-20T06:37:37Z | LLMs, Multilingual, Language Diversity, Large Language Models, Social
Media, News Media, Specialized LLMs, Fact-checking, Media Analysis, Arabic,
Hindi, English | null | null | null | null | null | null | null | null | null |
2,410.15316 | Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant | ['Alan Dao', 'Dinh Bach Vu', 'Huy Hoang Ha'] | ['cs.CL', 'cs.SD', 'eess.AS'] | Large Language Models (LLMs) have revolutionized natural language processing,
but their application to speech-based tasks remains challenging due to the
complexities of integrating audio and text modalities. This paper introduces
Ichigo, a mixed-modal model that seamlessly processes interleaved sequences of
speech and ... | 2024-10-20T07:03:49Z | null | null | null | Ichigo: Mixed-Modal Early-Fusion Realtime Voice Assistant | ['Alan Dao', 'Dinh Bach Vu', 'Huy Hoang Ha'] | 2,024 | arXiv.org | 5 | 53 | ['Computer Science', 'Engineering'] |
2,410.15458 | Allegro: Open the Black Box of Commercial-Level Video Generation Model | ['Yuan Zhou', 'Qiuyue Wang', 'Yuxuan Cai', 'Huan Yang'] | ['cs.CV'] | Significant advancements have been made in the field of video generation,
with the open-source community contributing a wealth of research papers and
tools for training high-quality models. However, despite these efforts, the
available information and resources remain insufficient for achieving
commercial-level perform... | 2024-10-20T17:51:35Z | null | null | null | null | null | null | null | null | null | null |
2,410.15608 | Moonshine: Speech Recognition for Live Transcription and Voice Commands | ['Nat Jeffries', 'Evan King', 'Manjunath Kudlur', 'Guy Nicholson', 'James Wang', 'Pete Warden'] | ['cs.SD', 'cs.CL', 'cs.LG', 'eess.AS'] | This paper introduces Moonshine, a family of speech recognition models
optimized for live transcription and voice command processing. Moonshine is
based on an encoder-decoder transformer architecture and employs Rotary
Position Embedding (RoPE) instead of traditional absolute position embeddings.
The model is trained o... | 2024-10-21T03:13:20Z | 7 pages, 6 figures, 3 tables | null | null | null | null | null | null | null | null | null |
2,410.15636 | LucidFusion: Reconstructing 3D Gaussians with Arbitrary Unposed Images | ['Hao He', 'Yixun Liang', 'Luozhou Wang', 'Yuanhao Cai', 'Xinli Xu', 'Hao-Xiang Guo', 'Xiang Wen', 'Yingcong Chen'] | ['cs.CV'] | Recent large reconstruction models have made notable progress in generating
high-quality 3D objects from single images. However, current reconstruction
methods often rely on explicit camera pose estimation or fixed viewpoints,
restricting their flexibility and practical applicability. We reformulate 3D
reconstruction a... | 2024-10-21T04:47:01Z | 11 pages, 10 figures, [project
page](https://heye0507.github.io/LucidFusion_page/) | null | null | LucidFusion: Reconstructing 3D Gaussians with Arbitrary Unposed Images | ['Hao He', 'Yixun Liang', 'Luozhou Wang', 'Yuanhao Cai', 'Xinli Xu', 'Hao-Xiang Guo', 'Xiang Wen', 'Yingcong Chen'] | 2,024 | null | 0 | 51 | ['Computer Science'] |
2,410.157 | InternLM2.5-StepProver: Advancing Automated Theorem Proving via Expert
Iteration on Large-Scale LEAN Problems | ['Zijian Wu', 'Suozhi Huang', 'Zhejian Zhou', 'Huaiyuan Ying', 'Jiayu Wang', 'Dahua Lin', 'Kai Chen'] | ['cs.AI', 'cs.CL'] | Large Language Models (LLMs) have emerged as powerful tools in mathematical
theorem proving, particularly when utilizing formal languages such as LEAN. The
major learning paradigm is expert iteration, which necessitates a pre-defined
dataset comprising numerous mathematical problems. In this process, LLMs
attempt to pr... | 2024-10-21T07:18:23Z | null | null | null | null | null | null | null | null | null | null |
2,410.15735 | AutoTrain: No-code training for state-of-the-art models | ['Abhishek Thakur'] | ['cs.AI'] | With the advancements in open-source models, training (or finetuning) models
on custom datasets has become a crucial part of developing solutions which are
tailored to specific industrial or open-source applications. Yet, there is no
single tool which simplifies the process of training across different types of
modalit... | 2024-10-21T07:53:32Z | null | null | null | null | null | null | null | null | null | null |
2,410.15926 | Mitigating Object Hallucination via Concentric Causal Attention | ['Yun Xing', 'Yiheng Li', 'Ivan Laptev', 'Shijian Lu'] | ['cs.CV', 'cs.CL'] | Recent Large Vision Language Models (LVLMs) present remarkable zero-shot
conversational and reasoning capabilities given multimodal queries.
Nevertheless, they suffer from object hallucination, a phenomenon where LVLMs
are prone to generate textual responses not factually aligned with image
inputs. Our pilot study reve... | 2024-10-21T11:54:53Z | To appear at NeurIPS 2024. Code is available at
https://github.com/xing0047/cca-llava | null | null | null | null | null | null | null | null | null |
2,410.15957 | CamI2V: Camera-Controlled Image-to-Video Diffusion Model | ['Guangcong Zheng', 'Teng Li', 'Rui Jiang', 'Yehao Lu', 'Tao Wu', 'Xi Li'] | ['cs.CV'] | Recent advancements have integrated camera pose as a user-friendly and
physics-informed condition in video diffusion models, enabling precise camera
control. In this paper, we identify one of the key challenges as effectively
modeling noisy cross-frame interactions to enhance geometry consistency and
camera controllabi... | 2024-10-21T12:36:27Z | null | null | null | CamI2V: Camera-Controlled Image-to-Video Diffusion Model | ['Guangcong Zheng', 'Teng Li', 'Rui Jiang', 'Yehao Lu', 'Tao Wu', 'Xi Li'] | 2,024 | arXiv.org | 27 | 72 | ['Computer Science'] |
2,410.16153 | Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages | ['Xiang Yue', 'Yueqi Song', 'Akari Asai', 'Seungone Kim', 'Jean de Dieu Nyandwi', 'Simran Khanuja', 'Anjali Kantharuban', 'Lintang Sutawika', 'Sathyanarayanan Ramamoorthy', 'Graham Neubig'] | ['cs.CL', 'cs.CV'] | Despite recent advances in multimodal large language models (MLLMs), their
development has predominantly focused on English- and western-centric datasets
and tasks, leaving most of the world's languages and diverse cultural contexts
underrepresented. This paper introduces Pangea, a multilingual multimodal LLM
trained o... | 2024-10-21T16:19:41Z | 54 pages, 27 figures | null | null | null | null | null | null | null | null | null |
2,410.16166 | Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM
Pretraining | ['Han Huang', 'Yuqi Huo', 'Zijia Zhao', 'Haoyu Lu', 'Shu Wu', 'Bingning Wang', 'Qiang Liu', 'Weipeng Chen', 'Liang Wang'] | ['cs.CV', 'cs.CL'] | Multimodal large language models (MLLMs) have made significant strides by
integrating visual and textual modalities. A critical factor in training MLLMs
is the quality of image-text pairs within multimodal pretraining datasets.
However, $\textit {de facto}$ filter-based data quality enhancement paradigms
often discard ... | 2024-10-21T16:32:41Z | null | null | null | null | null | null | null | null | null | null |
2,410.16184 | RM-Bench: Benchmarking Reward Models of Language Models with Subtlety
and Style | ['Yantao Liu', 'Zijun Yao', 'Rui Min', 'Yixin Cao', 'Lei Hou', 'Juanzi Li'] | ['cs.CL'] | Reward models are critical in techniques like Reinforcement Learning from
Human Feedback (RLHF) and Inference Scaling Laws, where they guide language
model alignment and select optimal responses. Despite their importance,
existing reward model benchmarks often evaluate models by asking them to
distinguish between respo... | 2024-10-21T16:48:26Z | null | null | null | null | null | null | null | null | null | null |
2,410.16198 | Improve Vision Language Model Chain-of-thought Reasoning | ['Ruohong Zhang', 'Bowen Zhang', 'Yanghao Li', 'Haotian Zhang', 'Zhiqing Sun', 'Zhe Gan', 'Yinfei Yang', 'Ruoming Pang', 'Yiming Yang'] | ['cs.AI', 'cs.CV', '68T07'] | Chain-of-thought (CoT) reasoning in vision language models (VLMs) is crucial
for improving interpretability and trustworthiness. However, current training
recipes lack robust CoT reasoning data, relying on datasets dominated by short
annotations with minimal rationales. In this work, we show that training VLM on
short ... | 2024-10-21T17:00:06Z | 10 pages + appendix | null | null | null | null | null | null | null | null | null |
2,410.16256 | CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and
Evolution | ['Maosong Cao', 'Alexander Lam', 'Haodong Duan', 'Hongwei Liu', 'Songyang Zhang', 'Kai Chen'] | ['cs.CL', 'cs.AI'] | Efficient and accurate evaluation is crucial for the continuous improvement
of large language models (LLMs). Among various assessment methods, subjective
evaluation has garnered significant attention due to its superior alignment
with real-world usage scenarios and human preferences. However, human-based
evaluations ar... | 2024-10-21T17:56:51Z | Technical Report, Code and Models:
https://github.com/open-compass/CompassJudger | null | null | CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution | ['Maosong Cao', 'Alexander Lam', 'Haodong Duan', 'Hong-wei Liu', 'Songyang Zhang', 'Kai Chen'] | 2,024 | arXiv.org | 20 | 22 | ['Computer Science'] |
2,410.16257 | Elucidating the design space of language models for image generation | ['Xuantong Liu', 'Shaozhe Hao', 'Xianbiao Qi', 'Tianyang Hu', 'Jun Wang', 'Rong Xiao', 'Yuan Yao'] | ['cs.CV'] | The success of autoregressive (AR) language models in text generation has
inspired the computer vision community to adopt Large Language Models (LLMs)
for image generation. However, considering the essential differences between
text and image modalities, the design space of language models for image
generation remains ... | 2024-10-21T17:57:04Z | Project page: https://pepper-lll.github.io/LMforImageGeneration/ | null | null | Elucidating the design space of language models for image generation | ['Xuantong Liu', 'Shaozhe Hao', 'Xianbiao Qi', 'Tianyang Hu', 'Jun Wang', 'Rong Xiao', 'Yuan Yao'] | 2,024 | arXiv.org | 3 | 63 | ['Computer Science'] |
2,410.16261 | Mini-InternVL: A Flexible-Transfer Pocket Multimodal Model with 5%
Parameters and 90% Performance | ['Zhangwei Gao', 'Zhe Chen', 'Erfei Cui', 'Yiming Ren', 'Weiyun Wang', 'Jinguo Zhu', 'Hao Tian', 'Shenglong Ye', 'Junjun He', 'Xizhou Zhu', 'Lewei Lu', 'Tong Lu', 'Yu Qiao', 'Jifeng Dai', 'Wenhai Wang'] | ['cs.CV'] | Multimodal large language models (MLLMs) have demonstrated impressive
performance in vision-language tasks across a broad spectrum of domains.
However, the large model scale and associated high computational costs pose
significant challenges for training and deploying MLLMs on consumer-grade GPUs
or edge devices, there... | 2024-10-21T17:58:20Z | Technical report | null | null | null | null | null | null | null | null | null |
2,410.16267 | xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video
Even in VLMs | ['Michael S. Ryoo', 'Honglu Zhou', 'Shrikant Kendre', 'Can Qin', 'Le Xue', 'Manli Shu', 'Jongwoo Park', 'Kanchana Ranasinghe', 'Silvio Savarese', 'Ran Xu', 'Caiming Xiong', 'Juan Carlos Niebles'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG'] | We present xGen-MM-Vid (BLIP-3-Video): a multimodal language model for
videos, particularly designed to efficiently capture temporal information over
multiple frames. BLIP-3-Video takes advantage of the 'temporal encoder' in
addition to the conventional visual tokenizer, which maps a sequence of tokens
over multiple fr... | 2024-10-21T17:59:11Z | null | null | null | xGen-MM-Vid (BLIP-3-Video): You Only Need 32 Tokens to Represent a Video Even in VLMs | ['Michael S Ryoo', 'Honglu Zhou', 'Shrikant B. Kendre', 'Can Qin', 'Le Xue', 'Manli Shu', 'Silvio Savarese', 'Ran Xu', 'Caiming Xiong', 'Juan Carlos Niebles'] | 2,024 | arXiv.org | 15 | 51 | ['Computer Science'] |
2,410.1629 | A Unified Model for Compressed Sensing MRI Across Undersampling Patterns | ['Armeet Singh Jatyani', 'Jiayun Wang', 'Aditi Chandrashekar', 'Zihui Wu', 'Miguel Liu-Schiaffini', 'Bahareh Tolooshams', 'Anima Anandkumar'] | ['eess.IV', 'cs.CV'] | Compressed Sensing MRI reconstructs images of the body's internal anatomy
from undersampled measurements, thereby reducing scan time. Recently, deep
learning has shown great potential for reconstructing high-fidelity images from
highly undersampled measurements. However, one needs to train multiple models
for different... | 2024-10-05T20:03:57Z | Accepted at 2025 Conference on Computer Vision and Pattern
Recognition | null | null | A Unified Model for Compressed Sensing MRI Across Undersampling Patterns | ['Armeet Singh Jatyani', 'Jiayun Wang', 'Zihui Wu', 'Miguel Liu-Schiaffini', 'Bahareh Tolooshams', 'Anima Anandkumar'] | 2,024 | null | 2 | 42 | ['Engineering', 'Computer Science'] |
2,410.16665 | SafetyAnalyst: Interpretable, Transparent, and Steerable Safety
Moderation for AI Behavior | ['Jing-Jing Li', 'Valentina Pyatkin', 'Max Kleiman-Weiner', 'Liwei Jiang', 'Nouha Dziri', 'Anne G. E. Collins', 'Jana Schaich Borg', 'Maarten Sap', 'Yejin Choi', 'Sydney Levine'] | ['cs.CL', 'cs.CY'] | The ideal AI safety moderation system would be both structurally
interpretable (so its decisions can be reliably explained) and steerable (to
align to safety standards and reflect a community's values), which current
systems fall short on. To address this gap, we present SafetyAnalyst, a novel
AI safety moderation fram... | 2024-10-22T03:38:37Z | Accepted to ICML 2025 | null | null | null | null | null | null | null | null | null |
2,410.16703 | PLDR-LLM: Large Language Model from Power Law Decoder Representations | ['Burc Gokden'] | ['cs.CL', 'cs.AI'] | We present the Large Language Model from Power Law Decoder Representations
(PLDR-LLM), a language model that leverages non-linear and linear
transformations through Power Law Graph Attention mechanism to generate
well-defined deductive and inductive outputs. We pretrain the PLDR-LLMs of
varying layer sizes with a small... | 2024-10-22T05:16:19Z | 22 pages, 4 figures, 10 tables | null | null | PLDR-LLM: Large Language Model from Power Law Decoder Representations | ['Burc Gokden'] | 2,024 | arXiv.org | 1 | 45 | ['Computer Science'] |
2,410.16794 | One-Step Diffusion Distillation through Score Implicit Matching | ['Weijian Luo', 'Zemin Huang', 'Zhengyang Geng', 'J. Zico Kolter', 'Guo-jun Qi'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Despite their strong performances on many generative tasks, diffusion models
require a large number of sampling steps in order to generate realistic
samples. This has motivated the community to develop effective methods to
distill pre-trained diffusion models into more efficient models, but these
methods still typicall... | 2024-10-22T08:17:20Z | Accepted by NeurIPS 2024 | NeurIPS 2024 | null | null | null | null | null | null | null | null |
2,410.1721 | Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh
through Large Language Modeling | ['Azmine Toushik Wasi', 'Wahid Faisal', 'Mst Rafia Islam', 'Mahathir Mohammad Bappy'] | ['cs.CL', 'cs.AI', 'cs.CY'] | Purpose: Bangladesh's legal system struggles with major challenges like
delays, complexity, high costs, and millions of unresolved cases, which deter
many from pursuing legal action due to lack of knowledge or financial
constraints. This research seeks to develop a specialized Large Language Model
(LLM) to assist in th... | 2024-10-22T17:34:59Z | In Review | null | null | Exploring Possibilities of AI-Powered Legal Assistance in Bangladesh through Large Language Modeling | ['Azmine Toushik Wasi', 'Wahid Faisal', 'Mst Rafia Islam', 'M. Bappy'] | 2,024 | arXiv.org | 0 | 29 | ['Computer Science'] |
2,410.17215 | MiniPLM: Knowledge Distillation for Pre-Training Language Models | ['Yuxian Gu', 'Hao Zhou', 'Fandong Meng', 'Jie Zhou', 'Minlie Huang'] | ['cs.CL'] | Knowledge distillation (KD) is widely used to train small, high-performing
student language models (LMs) using large teacher LMs. While effective in
fine-tuning, KD during pre-training faces efficiency, flexibility, and
effectiveness issues. Existing methods either incur high computational costs
due to online teacher i... | 2024-10-22T17:40:32Z | ICLR 2025 | null | null | MiniPLM: Knowledge Distillation for Pre-Training Language Models | ['Yuxian Gu', 'Hao Zhou', 'Fandong Meng', 'Jie Zhou', 'Minlie Huang'] | 2,024 | International Conference on Learning Representations | 7 | 92 | ['Computer Science'] |
2,410.17225 | Dhoroni: Exploring Bengali Climate Change and Environmental Views with a
Multi-Perspective News Dataset and Natural Language Processing | ['Azmine Toushik Wasi', 'Wahid Faisal', 'Taj Ahmad', 'Abdur Rahman', 'Mst Rafia Islam'] | ['cs.CL', 'cs.CY', 'cs.LG', 'stat.AP'] | Climate change poses critical challenges globally, disproportionately
affecting low-income countries that often lack resources and linguistic
representation on the international stage. Despite Bangladesh's status as one
of the most vulnerable nations to climate impacts, research gaps persist in
Bengali-language studies... | 2024-10-22T17:47:05Z | In Review | null | null | Dhoroni: Exploring Bengali Climate Change and Environmental Views with a Multi-Perspective News Dataset and Natural Language Processing | ['Azmine Toushik Wasi', 'Wahid Faisal', 'Taj Ahmad', 'Abdur Rahman', 'Mst Rafia Islam'] | 2,024 | arXiv.org | 0 | 51 | ['Computer Science', 'Mathematics'] |
2,410.17241 | Frontiers in Intelligent Colonoscopy | ['Ge-Peng Ji', 'Jingyi Liu', 'Peng Xu', 'Nick Barnes', 'Fahad Shahbaz Khan', 'Salman Khan', 'Deng-Ping Fan'] | ['eess.IV', 'cs.CV'] | Colonoscopy is currently one of the most sensitive screening methods for
colorectal cancer. This study investigates the frontiers of intelligent
colonoscopy techniques and their prospective implications for multimodal
medical applications. With this goal, we begin by assessing the current
data-centric and model-centric... | 2024-10-22T17:57:12Z | [Work in progress] A comprehensive survey of intelligent colonoscopy
in the multimodal era. [Updated Version V2] New training strategy for
colonoscopy-specific multimodal language model | null | null | null | null | null | null | null | null | null |
2,410.17242 | LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias | ['Haian Jin', 'Hanwen Jiang', 'Hao Tan', 'Kai Zhang', 'Sai Bi', 'Tianyuan Zhang', 'Fujun Luan', 'Noah Snavely', 'Zexiang Xu'] | ['cs.CV', 'cs.GR', 'cs.LG'] | We propose the Large View Synthesis Model (LVSM), a novel transformer-based
approach for scalable and generalizable novel view synthesis from sparse-view
inputs. We introduce two architectures: (1) an encoder-decoder LVSM, which
encodes input image tokens into a fixed number of 1D latent tokens, functioning
as a fully ... | 2024-10-22T17:58:28Z | project page: https://haian-jin.github.io/projects/LVSM/ | null | null | null | null | null | null | null | null | null |
2,410.17251 | Altogether: Image Captioning via Re-aligning Alt-text | ['Hu Xu', 'Po-Yao Huang', 'Xiaoqing Ellen Tan', 'Ching-Feng Yeh', 'Jacob Kahn', 'Christine Jou', 'Gargi Ghosh', 'Omer Levy', 'Luke Zettlemoyer', 'Wen-tau Yih', 'Shang-Wen Li', 'Saining Xie', 'Christoph Feichtenhofer'] | ['cs.CV', 'cs.CL'] | This paper focuses on creating synthetic data to improve the quality of image
captions. Existing works typically have two shortcomings. First, they caption
images from scratch, ignoring existing alt-text metadata, and second, lack
transparency if the captioners' training data (e.g. GPT) is unknown. In this
paper, we st... | 2024-10-22T17:59:57Z | accepted by EMNLP 2024; Meta CLIP 1.2 Data Engine | null | null | null | null | null | null | null | null | null |
2,410.17337 | Captions Speak Louder than Images (CASLIE): Generalizing Foundation
Models for E-commerce from High-quality Multimodal Instruction Data | ['Xinyi Ling', 'Bo Peng', 'Hanwen Du', 'Zhihui Zhu', 'Xia Ning'] | ['cs.CL', 'cs.AI', 'cs.IR'] | Leveraging multimodal data to drive breakthroughs in e-commerce applications
through Multimodal Foundation Models (MFMs) is gaining increasing attention
from the research community. However, there are significant challenges that
hinder the optimal use of multimodal e-commerce data by foundation models: (1)
the scarcity... | 2024-10-22T18:11:43Z | Xinyi Ling and Bo Peng contributed equally to this paper | null | null | Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data | ['Xinyi Ling', 'B. Peng', 'Hanwen Du', 'Zhihui Zhu', 'Xia Ning'] | 2,024 | arXiv.org | 0 | 50 | ['Computer Science'] |
2,410.17434 | LongVU: Spatiotemporal Adaptive Compression for Long Video-Language
Understanding | ['Xiaoqian Shen', 'Yunyang Xiong', 'Changsheng Zhao', 'Lemeng Wu', 'Jun Chen', 'Chenchen Zhu', 'Zechun Liu', 'Fanyi Xiao', 'Balakrishnan Varadarajan', 'Florian Bordes', 'Zhuang Liu', 'Hu Xu', 'Hyunwoo J. Kim', 'Bilge Soran', 'Raghuraman Krishnamoorthi', 'Mohamed Elhoseiny', 'Vikas Chandra'] | ['cs.CV'] | Multimodal Large Language Models (MLLMs) have shown promising progress in
understanding and analyzing video content. However, processing long videos
remains a significant challenge constrained by LLM's context size. To address
this limitation, we propose LongVU, a spatiotemporal adaptive compression
mechanism thats red... | 2024-10-22T21:21:37Z | Project page: https://vision-cair.github.io/LongVU | null | null | null | null | null | null | null | null | null |
2,410.17437 | Improving Automatic Speech Recognition with Decoder-Centric
Regularisation in Encoder-Decoder Models | ['Alexander Polok', 'Santosh Kesiraju', 'Karel Beneš', 'Lukáš Burget', 'Jan Černocký'] | ['eess.AS'] | This paper proposes a simple yet effective way of regularising the
encoder-decoder-based automatic speech recognition (ASR) models that enhance
the robustness of the model and improve the generalisation to out-of-domain
scenarios. The proposed approach is dubbed as
$\textbf{De}$coder-$\textbf{C}$entric $\textbf{R}$egul... | 2024-10-22T21:27:30Z | null | null | null | null | null | null | null | null | null | null |
2,410.17491 | X-MOBILITY: End-To-End Generalizable Navigation via World Modeling | ['Wei Liu', 'Huihua Zhao', 'Chenran Li', 'Joydeep Biswas', 'Billy Okal', 'Pulkit Goyal', 'Yan Chang', 'Soha Pouya'] | ['cs.RO'] | General-purpose navigation in challenging environments remains a significant
problem in robotics, with current state-of-the-art approaches facing myriad
limitations. Classical approaches struggle with cluttered settings and require
extensive tuning, while learning-based methods face difficulties generalizing
to out-of-... | 2024-10-23T01:11:29Z | null | null | null | null | null | null | null | null | null | null |
2,410.17599 | Cross-model Control: Improving Multiple Large Language Models in
One-time Training | ['Jiayi Wu', 'Hao Sun', 'Hengyi Cai', 'Lixin Su', 'Shuaiqiang Wang', 'Dawei Yin', 'Xiang Li', 'Ming Gao'] | ['cs.CL'] | The number of large language models (LLMs) with varying parameter scales and
vocabularies is increasing. While they deliver powerful performance, they also
face a set of common optimization needs to meet specific requirements or
standards, such as instruction following or avoiding the output of sensitive
information fr... | 2024-10-23T06:52:09Z | Accepted by NeurIPS 2024 | null | null | null | null | null | null | null | null | null |
2,410.17736 | MojoBench: Language Modeling and Benchmarks for Mojo | ['Nishat Raihan', 'Joanna C. S. Santos', 'Marcos Zampieri'] | ['cs.CL'] | The recently introduced Mojo programming language (PL) by Modular, has
received significant attention in the scientific community due to its claimed
significant speed boost over Python. Despite advancements in code Large
Language Models (LLMs) across various PLs, Mojo remains unexplored in this
context. To address this... | 2024-10-23T10:11:40Z | null | null | null | null | null | null | null | null | null | null |
2,410.17856 | ROCKET-1: Mastering Open-World Interaction with Visual-Temporal Context
Prompting | ['Shaofei Cai', 'Zihao Wang', 'Kewei Lian', 'Zhancun Mu', 'Xiaojian Ma', 'Anji Liu', 'Yitao Liang'] | ['cs.CV', 'cs.AI'] | Vision-language models (VLMs) have excelled in multimodal tasks, but adapting
them to embodied decision-making in open-world environments presents
challenges. One critical issue is bridging the gap between discrete entities in
low-level observations and the abstract concepts required for effective
planning. A common so... | 2024-10-23T13:26:59Z | null | null | null | null | null | null | null | null | null | null |
2,410.17891 | Scaling Diffusion Language Models via Adaptation from Autoregressive
Models | ['Shansan Gong', 'Shivam Agarwal', 'Yizhe Zhang', 'Jiacheng Ye', 'Lin Zheng', 'Mukai Li', 'Chenxin An', 'Peilin Zhao', 'Wei Bi', 'Jiawei Han', 'Hao Peng', 'Lingpeng Kong'] | ['cs.CL'] | Diffusion Language Models (DLMs) have emerged as a promising new paradigm for
text generative modeling, potentially addressing limitations of autoregressive
(AR) models. However, current DLMs have been studied at a smaller scale
compared to their AR counterparts and lack fair comparison on language modeling
benchmarks.... | 2024-10-23T14:04:22Z | ICLR 2025. (minor updates) Code: https://github.com/HKUNLP/DiffuLLaMA | null | null | null | null | null | null | null | null | null |
2,410.17897 | Value Residual Learning | ['Zhanchao Zhou', 'Tianyi Wu', 'Zhiyun Jiang', 'Fares Obeid', 'Zhenzhong Lan'] | ['cs.CL'] | While Transformer models have achieved remarkable success in various domains,
the effectiveness of information propagation through deep networks remains a
critical challenge. Standard hidden state residuals often fail to adequately
preserve initial token-level information in deeper layers. This paper
introduces ResForm... | 2024-10-23T14:15:07Z | null | null | null | Value Residual Learning | ['Zhanchao Zhou', 'Tianyi Wu', 'Zhiyun Jiang', 'Fares Obeid', 'Zhenzhong Lan'] | 2,024 | null | 1 | 40 | ['Computer Science'] |
2,410.18032 | GraphTeam: Facilitating Large Language Model-based Graph Analysis via
Multi-Agent Collaboration | ['Xin Sky Li', 'Qizhi Chu', 'Yubin Chen', 'Yang Liu', 'Yaoqi Liu', 'Zekai Yu', 'Weize Chen', 'Chen Qian', 'Chuan Shi', 'Cheng Yang'] | ['cs.AI', 'cs.CL', 'cs.MA'] | Graphs are widely used for modeling relational data in real-world scenarios,
such as social networks and urban computing. Existing LLM-based graph analysis
approaches either integrate graph neural networks (GNNs) for specific machine
learning tasks, limiting their transferability, or rely solely on LLMs'
internal reaso... | 2024-10-23T17:02:59Z | null | null | null | null | null | null | null | null | null | null |
2,410.18105 | Improving Embedding Accuracy for Document Retrieval Using Entity
Relationship Maps and Model-Aware Contrastive Sampling | ['Thea Aviss'] | ['cs.IR', 'cs.AI', 'cs.CL'] | In this paper we present APEX-Embedding-7B (Advanced Processing for Epistemic
eXtraction), a 7-billion parameter decoder-only text Feature Extraction Model,
specifically designed for Document Retrieval-Augmented Generation (RAG) tasks.
Our approach employs two training techniques that yield an emergent improvement
in f... | 2024-10-08T17:36:48Z | 10 Pages, 9 Figures | null | null | Improving Embedding Accuracy for Document Retrieval Using Entity Relationship Maps and Model-Aware Contrastive Sampling | ['Thea Aviss'] | 2,024 | arXiv.org | 0 | 0 | ['Computer Science'] |
2,410.18164 | TabDPT: Scaling Tabular Foundation Models on Real Data | ['Junwei Ma', 'Valentin Thomas', 'Rasa Hosseinzadeh', 'Hamidreza Kamkari', 'Alex Labach', 'Jesse C. Cresswell', 'Keyvan Golestan', 'Guangwei Yu', 'Anthony L. Caterini', 'Maksims Volkovs'] | ['cs.LG', 'cs.AI', 'stat.ML'] | Tabular data is one of the most ubiquitous sources of information worldwide,
spanning a wide variety of domains. This inherent heterogeneity has slowed the
development of Tabular Foundation Models (TFMs) capable of fast generalization
to unseen datasets. In-Context Learning (ICL) has recently emerged as a
promising sol... | 2024-10-23T18:00:00Z | Inference repo: github.com/layer6ai-labs/TabDPT-inference; Training
repo: github.com/layer6ai-labs/TabDPT-training | null | null | null | null | null | null | null | null | null |
2,410.18362 | WAFFLE: Finetuning Multi-Modal Model for Automated Front-End Development | ['Shanchao Liang', 'Nan Jiang', 'Shangshu Qian', 'Lin Tan'] | ['cs.SE', 'cs.CL', 'cs.CV'] | Web development involves turning UI designs into functional webpages, which
can be difficult for both beginners and experienced developers due to the
complexity of HTML's hierarchical structures and styles. While Large Language
Models (LLMs) have shown promise in generating source code, two major
challenges persist in ... | 2024-10-24T01:49:49Z | null | null | null | WAFFLE: Multi-Modal Model for Automated Front-End Development | ['Shanchao Liang', 'Nan Jiang', 'Shangshu Qian', 'Lin Tan'] | 2,024 | arXiv.org | 1 | 39 | ['Computer Science'] |
2,410.18387 | Interpretable Bilingual Multimodal Large Language Model for Diverse
Biomedical Tasks | ['Lehan Wang', 'Haonan Wang', 'Honglong Yang', 'Jiaji Mao', 'Zehong Yang', 'Jun Shen', 'Xiaomeng Li'] | ['cs.CV'] | Several medical Multimodal Large Languange Models (MLLMs) have been developed
to address tasks involving visual images with textual instructions across
various medical modalities, achieving impressive results. Most current medical
generalist models are region-agnostic, treating the entire image as a holistic
representa... | 2024-10-24T02:55:41Z | Accepted in ICLR 2025 | null | null | Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks | ['Lehan Wang', 'Haonan Wang', 'Honglong Yang', 'Jiaji Mao', 'Zehong Yang', 'Jun Shen', 'Xiaomeng Li'] | 2,024 | International Conference on Learning Representations | 6 | 63 | ['Computer Science'] |
2,410.18417 | Large Language Models Reflect the Ideology of their Creators | ['Maarten Buyl', 'Alexander Rogiers', 'Sander Noels', 'Guillaume Bied', 'Iris Dominguez-Catena', 'Edith Heiter', 'Iman Johary', 'Alexandru-Cristian Mara', 'Raphaël Romero', 'Jefrey Lijffijt', 'Tijl De Bie'] | ['cs.CL', 'cs.LG'] | Large language models (LLMs) are trained on vast amounts of data to generate
natural language, enabling them to perform tasks like text summarization and
question answering. These models have become popular in artificial intelligence
(AI) assistants like ChatGPT and already play an influential role in how humans
access... | 2024-10-24T04:02:30Z | null | null | null | Large Language Models Reflect the Ideology of their Creators | ['Maarten Buyl', 'Alexander Rogiers', 'Sander Noels', 'Iris Dominguez-Catena', 'Edith Heiter', 'Raphaël Romero', 'Iman Johary', 'A. Mara', 'Jefrey Lijffijt', 'T. D. Bie'] | 2,024 | arXiv.org | 24 | 35 | ['Computer Science'] |
2,410.18451 | Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs | ['Chris Yuhao Liu', 'Liang Zeng', 'Jiacai Liu', 'Rui Yan', 'Jujie He', 'Chaojie Wang', 'Shuicheng Yan', 'Yang Liu', 'Yahui Zhou'] | ['cs.AI', 'cs.CL'] | In this report, we introduce a collection of methods to enhance reward
modeling for LLMs, focusing specifically on data-centric techniques. We propose
effective data selection and filtering strategies for curating high-quality
open-source preference datasets, culminating in the Skywork-Reward data
collection, which con... | 2024-10-24T06:06:26Z | null | null | null | Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs | ['Chris Liu', 'Liang Zeng', 'Jiacai Liu', 'Rui Yan', 'Jujie He', 'Chaojie Wang', 'Shuicheng Yan', 'Yang Liu', 'Yahui Zhou'] | 2,024 | arXiv.org | 116 | 50 | ['Computer Science'] |
2,410.18469 | ADVLLM: Iterative Self-Tuning LLMs for Enhanced Jailbreaking
Capabilities | ['Chung-En Sun', 'Xiaodong Liu', 'Weiwei Yang', 'Tsui-Wei Weng', 'Hao Cheng', 'Aidan San', 'Michel Galley', 'Jianfeng Gao'] | ['cs.CL', 'cs.LG'] | Recent research has shown that Large Language Models (LLMs) are vulnerable to
automated jailbreak attacks, where adversarial suffixes crafted by algorithms
appended to harmful queries bypass safety alignment and trigger unintended
responses. Current methods for generating these suffixes are computationally
expensive an... | 2024-10-24T06:36:12Z | Accepted to NAACL 2025 Main (oral) | null | null | null | null | null | null | null | null | null |
2,410.18481 | Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence
Embeddings for Automatic Dialog Flow Extraction | ['Sergio Burdisso', 'Srikanth Madikeri', 'Petr Motlicek'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Efficiently deriving structured workflows from unannotated dialogs remains an
underexplored and formidable challenge in computational linguistics. Automating
this process could significantly accelerate the manual design of workflows in
new domains and enable the grounding of large language models in
domain-specific flo... | 2024-10-24T07:10:18Z | Accepted to EMNLP 2024 main conference | https://aclanthology.org/2024.emnlp-main.310/ | null | Dialog2Flow: Pre-training Soft-Contrastive Action-Driven Sentence Embeddings for Automatic Dialog Flow Extraction | ['Sergio Burdisso', 'S. Madikeri', 'P. Motlícek'] | 2,024 | Conference on Empirical Methods in Natural Language Processing | 3 | 63 | ['Computer Science'] |
2,410.18505 | CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for
pre-training large language models | ['Liangdong Wang', 'Bo-Wen Zhang', 'Chengwei Wu', 'Hanyu Zhao', 'Xiaofeng Shi', 'Shuhao Gu', 'Jijie Li', 'Quanyue Ma', 'TengFei Pan', 'Guang Liu'] | ['cs.CL'] | We present CCI3.0-HQ (https://huggingface.co/datasets/BAAI/CCI3-HQ), a
high-quality 500GB subset of the Chinese Corpora Internet 3.0
(CCI3.0)(https://huggingface.co/datasets/BAAI/CCI3-Data), developed using a
novel two-stage hybrid filtering pipeline that significantly enhances data
quality. To evaluate its effectivene... | 2024-10-24T07:50:07Z | null | null | null | CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language models | ['Liangdong Wang', 'Bo-wen Zhang', 'Chengwei Wu', 'Hanyu Zhao', 'Xiaofeng Shi', 'Shuhao Gu', 'Jijie Li', 'Quanyue Ma', 'Tengfei Pan', 'Guang Liu'] | 2,024 | arXiv.org | 4 | 20 | ['Computer Science'] |
2,410.18514 | Scaling up Masked Diffusion Models on Text | ['Shen Nie', 'Fengqi Zhu', 'Chao Du', 'Tianyu Pang', 'Qian Liu', 'Guangtao Zeng', 'Min Lin', 'Chongxuan Li'] | ['cs.AI', 'cs.CL', 'cs.LG'] | Masked diffusion models (MDMs) have shown promise in language modeling, yet
their scalability and effectiveness in core language tasks, such as text
generation and language understanding, remain underexplored. This paper
establishes the first scaling law for MDMs, demonstrating a scaling rate
comparable to autoregressi... | 2024-10-24T08:01:22Z | null | null | null | Scaling up Masked Diffusion Models on Text | ['Shen Nie', 'Fengqi Zhu', 'Chao Du', 'Tianyu Pang', 'Qian Liu', 'Guangtao Zeng', 'Min Lin', 'Chongxuan Li'] | 2,024 | International Conference on Learning Representations | 30 | 81 | ['Computer Science'] |
2,410.18558 | Infinity-MM: Scaling Multimodal Performance with Large-Scale and
High-Quality Instruction Data | ['Shuhao Gu', 'Jialing Zhang', 'Siyuan Zhou', 'Kevin Yu', 'Zhaohu Xing', 'Liangdong Wang', 'Zhou Cao', 'Jintao Jia', 'Zhuoyi Zhang', 'Yixuan Wang', 'Zhenchong Hu', 'Bo-Wen Zhang', 'Jijie Li', 'Dong Liang', 'Yingli Zhao', 'Songjing Wang', 'Yulong Ao', 'Yiming Ju', 'Huanhuan Ma', 'Xiaotong Li', 'Haiwen Diao', 'Yufeng Cui... | ['cs.CL'] | Recently, Vision-Language Models (VLMs) have achieved remarkable progress in
multimodal tasks, and multimodal instruction data serves as the foundation for
enhancing VLM capabilities. Despite the availability of several open-source
multimodal datasets, limitations in the scale and quality of open-source
instruction dat... | 2024-10-24T09:03:48Z | null | null | null | null | null | null | null | null | null | null |
2,410.18565 | Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and
Evaluation | ['Krzysztof Ociepa', 'Łukasz Flis', 'Krzysztof Wróbel', 'Adrian Gwoździej', 'Remigiusz Kinas'] | ['cs.CL', 'cs.AI', 'I.2.7'] | We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for
Polish language processing. Trained on curated Polish corpora, this model
addresses key challenges in language model development through innovative
techniques. These include Weighted Instruction Cross-Entropy Loss, which
balances the learning ... | 2024-10-24T09:16:09Z | null | null | null | Bielik 7B v0.1: A Polish Language Model - Development, Insights, and Evaluation | ['Krzysztof Ociepa', 'Lukasz Flis', "Krzysztof Wr'obel", "Adrian Gwo'zdziej", 'Remigiusz Kinas'] | 2,024 | arXiv.org | 4 | 49 | ['Computer Science'] |
2,410.18603 | AgentStore: Scalable Integration of Heterogeneous Agents As Specialized
Generalist Computer Assistant | ['Chengyou Jia', 'Minnan Luo', 'Zhuohang Dang', 'Qiushi Sun', 'Fangzhi Xu', 'Junlin Hu', 'Tianbao Xie', 'Zhiyong Wu'] | ['cs.AI', 'cs.RO'] | Digital agents capable of automating complex computer tasks have attracted
considerable attention due to their immense potential to enhance human-computer
interaction. However, existing agent methods exhibit deficiencies in their
generalization and specialization capabilities, especially in handling
open-ended computer... | 2024-10-24T09:58:40Z | null | null | null | null | null | null | null | null | null | null |
2,410.18634 | Little Giants: Synthesizing High-Quality Embedding Data at Scale | ['Haonan Chen', 'Liang Wang', 'Nan Yang', 'Yutao Zhu', 'Ziliang Zhao', 'Furu Wei', 'Zhicheng Dou'] | ['cs.CL', 'cs.AI', 'cs.IR'] | Synthetic data generation has become an increasingly popular way of training
models without the need for large, manually labeled datasets. For tasks like
text embedding, synthetic data offers diverse and scalable training examples,
significantly reducing the cost of human annotation. However, most current
approaches re... | 2024-10-24T10:47:30Z | null | null | null | null | null | null | null | null | null | null |
2,410.18666 | DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe
Dataset Curation | ['Yuang Ai', 'Xiaoqiang Zhou', 'Huaibo Huang', 'Xiaotian Han', 'Zhengyu Chen', 'Quanzeng You', 'Hongxia Yang'] | ['cs.CV'] | Image restoration (IR) in real-world scenarios presents significant
challenges due to the lack of high-capacity models and comprehensive datasets.
To tackle these issues, we present a dual strategy: GenIR, an innovative data
curation pipeline, and DreamClear, a cutting-edge Diffusion Transformer
(DiT)-based image resto... | 2024-10-24T11:57:20Z | Accepted by NeurIPS 2024 | null | null | null | null | null | null | null | null | null |
2,410.18693 | Unleashing LLM Reasoning Capability via Scalable Question Synthesis from
Scratch | ['Yuyang Ding', 'Xinyu Shi', 'Xiaobo Liang', 'Juntao Li', 'Zhaopeng Tu', 'Qiaoming Zhu', 'Min Zhang'] | ['cs.CL', 'cs.AI'] | Improving the mathematical reasoning capabilities of Large Language Models
(LLMs) is critical for advancing artificial intelligence. However, access to
extensive, diverse, and high-quality reasoning datasets remains a significant
challenge, particularly for the open-source community. In this paper, we
propose ScaleQues... | 2024-10-24T12:42:04Z | ACL 2025 | null | null | null | null | null | null | null | null | null |
2,410.18775 | Robust Watermarking Using Generative Priors Against Image Editing: From
Benchmarking to Advances | ['Shilin Lu', 'Zihan Zhou', 'Jiayou Lu', 'Yuanzhi Zhu', 'Adams Wai-Kin Kong'] | ['cs.CV', 'cs.AI', 'cs.CR'] | Current image watermarking methods are vulnerable to advanced image editing
techniques enabled by large-scale text-to-image models. These models can
distort embedded watermarks during editing, posing significant challenges to
copyright protection. In this work, we introduce W-Bench, the first
comprehensive benchmark de... | 2024-10-24T14:28:32Z | Accepted by ICLR 2025 | null | null | null | null | null | null | null | null | null |
2,410.18857 | Probabilistic Language-Image Pre-Training | ['Sanghyuk Chun', 'Wonjae Kim', 'Song Park', 'Sangdoo Yun'] | ['cs.CV', 'cs.LG'] | Vision-language models (VLMs) embed aligned image-text pairs into a joint
space but often rely on deterministic embeddings, assuming a one-to-one
correspondence between images and texts. This oversimplifies real-world
relationships, which are inherently many-to-many, with multiple captions
describing a single image and... | 2024-10-24T15:42:25Z | Code: https://github.com/naver-ai/prolip HuggingFace Hub:
https://huggingface.co/collections/SanghyukChun/prolip-6712595dfc87fd8597350291
33 pages, 4.8 MB; LongProLIP paper: arXiv:2503.08048 | null | null | Probabilistic Language-Image Pre-Training | ['Sanghyuk Chun', 'Wonjae Kim', 'Song Park', 'Sangdoo Yun'] | 2,024 | International Conference on Learning Representations | 6 | 78 | ['Computer Science'] |
2,410.18902 | LLMs for Extremely Low-Resource Finno-Ugric Languages | ['Taido Purason', 'Hele-Andra Kuulmets', 'Mark Fishel'] | ['cs.CL'] | The advancement of large language models (LLMs) has predominantly focused on
high-resource languages, leaving low-resource languages, such as those in the
Finno-Ugric family, significantly underrepresented. This paper addresses this
gap by focusing on V\~oro, Livonian, and Komi. We cover almost the entire cycle
of LLM ... | 2024-10-24T16:48:12Z | null | Findings of the Association for Computational Linguistics: NAACL
2025, pages 6677-6697 | null | null | null | null | null | null | null | null |
2,410.18977 | Pay Attention and Move Better: Harnessing Attention for Interactive
Motion Generation and Training-free Editing | ['Ling-Hao Chen', 'Shunlin Lu', 'Wenxun Dai', 'Zhiyang Dou', 'Xuan Ju', 'Jingbo Wang', 'Taku Komura', 'Lei Zhang'] | ['cs.CV'] | This research delves into the problem of interactive editing of human motion
generation. Previous motion diffusion models lack explicit modeling of the
word-level text-motion correspondence and good explainability, hence
restricting their fine-grained editing ability. To address this issue, we
propose an attention-base... | 2024-10-24T17:59:45Z | Updated MotionCLR technical report | null | null | null | null | null | null | null | null | null |
2,410.18978 | Framer: Interactive Frame Interpolation | ['Wen Wang', 'Qiuyu Wang', 'Kecheng Zheng', 'Hao Ouyang', 'Zhekai Chen', 'Biao Gong', 'Hao Chen', 'Yujun Shen', 'Chunhua Shen'] | ['cs.CV'] | We propose Framer for interactive frame interpolation, which targets
producing smoothly transitioning frames between two images as per user
creativity. Concretely, besides taking the start and end frames as inputs, our
approach supports customizing the transition process by tailoring the
trajectory of some selected key... | 2024-10-24T17:59:51Z | Project page: https://aim-uofa.github.io/Framer/ | null | null | null | null | null | null | null | null | null |
2,410.19008 | Teach Multimodal LLMs to Comprehend Electrocardiographic Images | ['Ruoqi Liu', 'Yuelin Bai', 'Xiang Yue', 'Ping Zhang'] | ['eess.IV', 'cs.AI', 'cs.CV'] | The electrocardiogram (ECG) is an essential non-invasive diagnostic tool for
assessing cardiac conditions. Existing automatic interpretation methods suffer
from limited generalizability, focusing on a narrow range of cardiac
conditions, and typically depend on raw physiological signals, which may not be
readily availab... | 2024-10-21T20:26:41Z | null | null | null | null | null | null | null | null | null | null |
2,410.19278 | Applying sparse autoencoders to unlearn knowledge in language models | ['Eoin Farrell', 'Yeu-Tong Lau', 'Arthur Conmy'] | ['cs.LG', 'cs.AI'] | We investigate whether sparse autoencoders (SAEs) can be used to remove
knowledge from language models. We use the biology subset of the Weapons of
Mass Destruction Proxy dataset and test on the gemma-2b-it and gemma-2-2b-it
language models. We demonstrate that individual interpretable biology-related
SAE features can ... | 2024-10-25T03:21:14Z | null | null | null | null | null | null | null | null | null | null |
2,410.1929 | Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite
Learning | ['Yujian Liu', 'Shiyu Chang', 'Tommi Jaakkola', 'Yang Zhang'] | ['cs.CL'] | Recent studies have identified one aggravating factor of LLM hallucinations
as the knowledge inconsistency between pre-training and fine-tuning, where
unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong
outputs. In this paper, we propose a novel fine-tuning strategy called
Prereq-Tune to addres... | 2024-10-25T03:48:51Z | null | null | null | Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning | ['Yujian Liu', 'Shiyu Chang', 'T. Jaakkola', 'Yang Zhang'] | 2,024 | International Conference on Learning Representations | 1 | 45 | ['Computer Science'] |
2,410.19324 | Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space
diffusion | ['Emiel Hoogeboom', 'Thomas Mensink', 'Jonathan Heek', 'Kay Lamerigts', 'Ruiqi Gao', 'Tim Salimans'] | ['cs.CV', 'cs.LG', 'stat.ML'] | Latent diffusion models have become the popular choice for scaling up
diffusion models for high resolution image synthesis. Compared to pixel-space
models that are trained end-to-end, latent models are perceived to be more
efficient and to produce higher image quality at high resolution. Here we
challenge these notions... | 2024-10-25T06:20:06Z | Accepted to CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,410.1959 | MonoDGP: Monocular 3D Object Detection with Decoupled-Query and
Geometry-Error Priors | ['Fanqi Pu', 'Yifan Wang', 'Jiru Deng', 'Wenming Yang'] | ['cs.CV'] | Perspective projection has been extensively utilized in monocular 3D object
detection methods. It introduces geometric priors from 2D bounding boxes and 3D
object dimensions to reduce the uncertainty of depth estimation. However, due
to depth errors originating from the object's visual surface, the height of the
boundi... | 2024-10-25T14:31:43Z | null | null | null | MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors | ['Fanqi Pu', 'Yifan Wang', 'Jiru Deng', 'Wenming Yang'] | 2,024 | Computer Vision and Pattern Recognition | 3 | 67 | ['Computer Science'] |
2,410.19635 | Frozen-DETR: Enhancing DETR with Image Understanding from Frozen
Foundation Models | ['Shenghao Fu', 'Junkai Yan', 'Qize Yang', 'Xihan Wei', 'Xiaohua Xie', 'Wei-Shi Zheng'] | ['cs.CV'] | Recent vision foundation models can extract universal representations and
show impressive abilities in various tasks. However, their application on
object detection is largely overlooked, especially without fine-tuning them. In
this work, we show that frozen foundation models can be a versatile feature
enhancer, even t... | 2024-10-25T15:38:24Z | Accepted to NeurIPS 2024 | null | null | null | null | null | null | null | null | null |
2,410.19704 | Multi-view biomedical foundation models for molecule-target and property
prediction | ['Parthasarathy Suryanarayanan', 'Yunguang Qiu', 'Shreyans Sethi', 'Diwakar Mahajan', 'Hongyang Li', 'Yuxin Yang', 'Elif Eyigoz', 'Aldo Guzman Saenz', 'Daniel E. Platt', 'Timothy H. Rumbell', 'Kenney Ng', 'Sanjoy Dey', 'Myson Burch', 'Bum Chul Kwon', 'Pablo Meyer', 'Feixiong Cheng', 'Jianying Hu', 'Joseph A. Morrone'] | ['q-bio.BM', 'cs.AI', 'cs.LG'] | Quality molecular representations are key to foundation model development in
bio-medical research. Previous efforts have typically focused on a single
representation or molecular view, which may have strengths or weaknesses on a
given task. We develop Multi-view Molecular Embedding with Late Fusion
(MMELON), an approac... | 2024-10-25T17:22:33Z | 40 pages including supplement. 10 figures, 8 tables | null | null | null | null | null | null | null | null | null |
2,410.19818 | UniMTS: Unified Pre-training for Motion Time Series | ['Xiyuan Zhang', 'Diyan Teng', 'Ranak Roy Chowdhury', 'Shuheng Li', 'Dezhi Hong', 'Rajesh K. Gupta', 'Jingbo Shang'] | ['eess.SP', 'cs.AI', 'cs.LG'] | Motion time series collected from mobile and wearable devices such as
smartphones and smartwatches offer significant insights into human behavioral
patterns, with wide applications in healthcare, automation, IoT, and AR/XR due
to their low-power, always-on nature. However, given security and privacy
concerns, building ... | 2024-10-18T06:39:13Z | NeurIPS 2024. Code: https://github.com/xiyuanzh/UniMTS. Model:
https://huggingface.co/xiyuanz/UniMTS | null | null | null | null | null | null | null | null | null |
2,410.20088 | RARe: Retrieval Augmented Retrieval with In-Context Examples | ['Atula Tejaswi', 'Yoonsang Lee', 'Sujay Sanghavi', 'Eunsol Choi'] | ['cs.CL', 'cs.AI', 'cs.IR'] | We investigate whether in-context examples, widely used in decoder-only
language models (LLMs), can improve embedding model performance in retrieval
tasks. Unlike in LLMs, naively prepending in-context examples (query-document
pairs) to the target query at inference time does not work out of the box. We
introduce a sim... | 2024-10-26T05:46:20Z | null | null | null | null | null | null | null | null | null | null |
2,410.20163 | UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers | ['Dehai Min', 'Zhiyang Xu', 'Guilin Qi', 'Lifu Huang', 'Chenyu You'] | ['cs.IR', 'cs.CL'] | Existing information retrieval (IR) models often assume a homogeneous
structure for knowledge sources and user queries, limiting their applicability
in real-world settings where retrieval is inherently heterogeneous and diverse.
In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous
knowledge re... | 2024-10-26T12:34:07Z | NAACL 2025, Main, Long Paper | null | null | null | null | null | null | null | null | null |
2,410.20202 | An Efficient Watermarking Method for Latent Diffusion Models via
Low-Rank Adaptation | ['Dongdong Lin', 'Yue Li', 'Benedetta Tondi', 'Bin Li', 'Mauro Barni'] | ['cs.CV'] | The rapid proliferation of deep neural networks (DNNs) is driving a surge in
model watermarking technologies, as the trained deep models themselves serve as
intellectual properties. The core of existing model watermarking techniques
involves modifying or tuning the models' weights. However, with the emergence
of increa... | 2024-10-26T15:23:49Z | null | null | null | An Efficient Watermarking Method for Latent Diffusion Models via Low-Rank Adaptation | ['Dongdong Lin', 'Yue Li', 'B. Tondi', 'Bin Li', 'Mauro Barni'] | 2,024 | arXiv.org | 1 | 37 | ['Computer Science'] |
2,410.20268 | Centaur: a foundation model of human cognition | ['Marcel Binz', 'Elif Akata', 'Matthias Bethge', 'Franziska Brändle', 'Fred Callaway', 'Julian Coda-Forno', 'Peter Dayan', 'Can Demircan', 'Maria K. Eckstein', 'Noémi Éltető', 'Thomas L. Griffiths', 'Susanne Haridi', 'Akshay K. Jagadish', 'Li Ji-An', 'Alexander Kipnis', 'Sreejan Kumar', 'Tobias Ludwig', 'Marvin Mathony... | ['cs.LG'] | Establishing a unified theory of cognition has been a major goal of
psychology. While there have been previous attempts to instantiate such
theories by building computational models, we currently do not have one model
that captures the human mind in its entirety. A first step in this direction is
to create a model that... | 2024-10-26T20:39:41Z | null | null | null | null | null | null | null | null | null | null |
2,410.20526 | Llama Scope: Extracting Millions of Features from Llama-3.1-8B with
Sparse Autoencoders | ['Zhengfu He', 'Wentao Shu', 'Xuyang Ge', 'Lingjie Chen', 'Junxuan Wang', 'Yunhua Zhou', 'Frances Liu', 'Qipeng Guo', 'Xuanjing Huang', 'Zuxuan Wu', 'Yu-Gang Jiang', 'Xipeng Qiu'] | ['cs.LG', 'cs.CL'] | Sparse Autoencoders (SAEs) have emerged as a powerful unsupervised method for
extracting sparse representations from language models, yet scalable training
remains a significant challenge. We introduce a suite of 256 SAEs, trained on
each layer and sublayer of the Llama-3.1-8B-Base model, with 32K and 128K
features. Mo... | 2024-10-27T17:33:49Z | 22pages, 12 figures | null | null | null | null | null | null | null | null | null |
2,410.20527 | CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for
Parallel Programming | ['Ali TehraniJamsaz', 'Arijit Bhattacharjee', 'Le Chen', 'Nesreen K. Ahmed', 'Amir Yazdanbakhsh', 'Ali Jannesari'] | ['cs.DC', 'cs.AI', 'cs.LG', 'cs.PF', 'cs.PL', 'cs.SE'] | Recent advancements in Large Language Models (LLMs) have renewed interest in
automatic programming language translation. Encoder-decoder transformer models,
in particular, have shown promise in translating between different programming
languages. However, translating between a language and its high-performance
computin... | 2024-10-27T17:34:07Z | null | null | null | null | null | null | null | null | null | null |
2,410.20651 | SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA
Through Six-Dimensional Feature Analysis | ['Huzaifa Pardawala', 'Siddhant Sukhani', 'Agam Shah', 'Veer Kejriwal', 'Abhishek Pillai', 'Rohan Bhasin', 'Andrew DiBiasio', 'Tarun Mandapati', 'Dhruv Adha', 'Sudheer Chava'] | ['cs.CL', 'cs.AI'] | Fact-checking is extensively studied in the context of misinformation and
disinformation, addressing objective inaccuracies. However, a softer form of
misinformation involves responses that are factually correct but lack certain
features such as clarity and relevance. This challenge is prevalent in formal
Question-Answ... | 2024-10-28T01:17:34Z | Accepted at NeurIPS 2024 | null | null | SubjECTive-QA: Measuring Subjectivity in Earnings Call Transcripts' QA Through Six-Dimensional Feature Analysis | ['Huzaifa Pardawala', 'Siddhant Sukhani', 'Agam Shah', 'Veer Kejriwal', 'Abhishek Pillai', 'Rohan Bhasin', 'Andrew DiBiasio', 'Tarun Mandapati', 'Dhruv Adha', 'S. Chava'] | 2,024 | Neural Information Processing Systems | 2 | 64 | ['Computer Science'] |
2,410.20722 | Interpretable Image Classification with Adaptive Prototype-based Vision
Transformers | ['Chiyu Ma', 'Jon Donnelly', 'Wenjun Liu', 'Soroush Vosoughi', 'Cynthia Rudin', 'Chaofan Chen'] | ['cs.CV'] | We present ProtoViT, a method for interpretable image classification
combining deep learning and case-based reasoning. This method classifies an
image by comparing it to a set of learned prototypes, providing explanations of
the form ``this looks like that.'' In our model, a prototype consists of
\textit{parts}, which ... | 2024-10-28T04:33:28Z | null | null | null | null | null | null | null | null | null | null |
2,410.20771 | MrT5: Dynamic Token Merging for Efficient Byte-level Language Models | ['Julie Kallini', 'Shikhar Murty', 'Christopher D. Manning', 'Christopher Potts', 'Róbert Csordás'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Models that rely on subword tokenization have significant drawbacks, such as
sensitivity to character-level noise like spelling errors and inconsistent
compression rates across different languages and scripts. While character- or
byte-level models like ByT5 attempt to address these concerns, they have not
gained widesp... | 2024-10-28T06:14:12Z | null | null | null | null | null | null | null | null | null | null |
2,410.20898 | David and Goliath: Small One-step Model Beats Large Diffusion with Score
Post-training | ['Weijian Luo', 'Colin Zhang', 'Debing Zhang', 'Zhengyang Geng'] | ['cs.CV', 'cs.AI', 'cs.LG', 'cs.MM'] | We propose Diff-Instruct* (DI*), a data-efficient post-training approach for
one-step text-to-image generative models to improve its human preferences
without requiring image data. Our method frames alignment as online
reinforcement learning from human feedback (RLHF), which optimizes the one-step
model to maximize hum... | 2024-10-28T10:26:19Z | Revision: paper accepted by the ICML2025 main conference | null | null | null | null | null | null | null | null | null |
2,410.20964 | DeTeCtive: Detecting AI-generated Text via Multi-Level Contrastive
Learning | ['Xun Guo', 'Shan Zhang', 'Yongxin He', 'Ting Zhang', 'Wanquan Feng', 'Haibin Huang', 'Chongyang Ma'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Current techniques for detecting AI-generated text are largely confined to
manual feature crafting and supervised binary classification paradigms. These
methodologies typically lead to performance bottlenecks and unsatisfactory
generalizability. Consequently, these methods are often inapplicable for
out-of-distribution... | 2024-10-28T12:34:49Z | To appear in NeurIPS 2024. Code is available at
https://github.com/heyongxin233/DeTeCtive | null | null | null | null | null | null | null | null | null |
2,410.21035 | Beyond Autoregression: Fast LLMs via Self-Distillation Through Time | ['Justin Deschenaux', 'Caglar Gulcehre'] | ['cs.LG', 'cs.CL'] | Autoregressive (AR) Large Language Models (LLMs) have demonstrated
significant success across numerous tasks. However, the AR modeling paradigm
presents certain limitations; for instance, contemporary autoregressive LLMs
are trained to generate one token at a time, which can result in noticeable
latency. Recent advance... | 2024-10-28T13:56:30Z | null | null | null | null | null | null | null | null | null | null |
2,410.21139 | uOttawa at LegalLens-2024: Transformer-based Classification Experiments | ['Nima Meghdadi', 'Diana Inkpen'] | ['cs.CL'] | This paper presents the methods used for LegalLens-2024 shared task, which
focused on detecting legal violations within unstructured textual data and
associating these violations with potentially affected individuals. The shared
task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B)
Legal Natural ... | 2024-10-28T15:42:45Z | Just accepted at the the EMNLP conference | null | null | null | null | null | null | null | null | null |
2,410.21228 | LoRA vs Full Fine-tuning: An Illusion of Equivalence | ['Reece Shuttleworth', 'Jacob Andreas', 'Antonio Torralba', 'Pratyusha Sharma'] | ['cs.LG', 'cs.CL'] | Fine-tuning is a crucial paradigm for adapting pre-trained large language
models to downstream tasks. Recently, methods like Low-Rank Adaptation (LoRA)
have been shown to effectively fine-tune LLMs with an extreme reduction in
trainable parameters. But, \emph{are their learned solutions really
equivalent?} We study how... | 2024-10-28T17:14:01Z | null | null | null | null | null | null | null | null | null | null |
2,410.21252 | LongReward: Improving Long-context Large Language Models with AI
Feedback | ['Jiajie Zhang', 'Zhongni Hou', 'Xin Lv', 'Shulin Cao', 'Zhenyu Hou', 'Yilin Niu', 'Lei Hou', 'Yuxiao Dong', 'Ling Feng', 'Juanzi Li'] | ['cs.CL', 'cs.LG'] | Though significant advancements have been achieved in developing long-context
large language models (LLMs), the compromised quality of LLM-synthesized data
for supervised fine-tuning (SFT) often affects the long-context performance of
SFT models and leads to inherent limitations. In principle, reinforcement
learning (R... | 2024-10-28T17:50:42Z | null | null | null | null | null | null | null | null | null | null |
2,410.21264 | LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior | ['Hanyu Wang', 'Saksham Suri', 'Yixuan Ren', 'Hao Chen', 'Abhinav Shrivastava'] | ['cs.CV', 'cs.AI'] | We present LARP, a novel video tokenizer designed to overcome limitations in
current video tokenization methods for autoregressive (AR) generative models.
Unlike traditional patchwise tokenizers that directly encode local visual
patches into discrete tokens, LARP introduces a holistic tokenization scheme
that gathers i... | 2024-10-28T17:57:07Z | ICLR 2025. Project page: https://hywang66.github.io/larp/ | null | null | LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior | ['Hanyu Wang', 'Saksham Suri', 'Yixuan Ren', 'Hao Chen', 'Abhinav Shrivastava'] | 2,024 | International Conference on Learning Representations | 12 | 72 | ['Computer Science'] |
2,410.21485 | SpeechQE: Estimating the Quality of Direct Speech Translation | ['HyoJung Han', 'Kevin Duh', 'Marine Carpuat'] | ['cs.CL'] | Recent advances in automatic quality estimation for machine translation have
exclusively focused on written language, leaving the speech modality
underexplored. In this work, we formulate the task of quality estimation for
speech translation (SpeechQE), construct a benchmark, and evaluate a family of
systems based on c... | 2024-10-28T19:50:04Z | EMNLP2024 | null | null | null | null | null | null | null | null | null |
2,410.21638 | Adapting Diffusion Models for Improved Prompt Compliance and
Controllable Image Synthesis | ['Deepak Sridhar', 'Abhishek Peri', 'Rohith Rachala', 'Nuno Vasconcelos'] | ['cs.CV'] | Recent advances in generative modeling with diffusion processes (DPs) enabled
breakthroughs in image synthesis. Despite impressive image quality, these
models have various prompt compliance problems, including low recall in
generating multiple objects, difficulty in generating text in images, and
meeting constraints li... | 2024-10-29T00:54:00Z | Accepted to NeurIPS 2024 conference. Project Page:
https://deepaksridhar.github.io/factorgraphdiffusion.github.io/ | null | null | Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image Synthesis | ['Deepak Sridhar', 'Abhishek Peri', 'Rohith Rachala', 'Nuno Vasconcelos'] | 2,024 | Neural Information Processing Systems | 1 | 60 | ['Computer Science'] |
2,410.21723 | Fine-tuning Large Language Models for DGA and DNS Exfiltration Detection | ['Md Abu Sayed', 'Asif Rahman', 'Christopher Kiekintveld', 'Sebastian Garcia'] | ['cs.CR'] | Domain Generation Algorithms (DGAs) are malicious techniques used by malware
to dynamically generate seemingly random domain names for communication with
Command & Control (C&C) servers. Due to the fast and simple generation of DGA
domains, detection methods must be highly efficient and precise to be
effective. Large L... | 2024-10-29T04:22:28Z | Accepted in Proceedings of the Workshop at AI for Cyber Threat
Intelligence (WAITI), 2024 | null | null | null | null | null | null | null | null | null |
2,410.21801 | PerSRV: Personalized Sticker Retrieval with Vision-Language Model | ['Heng Er Metilda Chee', 'Jiayin Wang', 'Zhiqiang Guo', 'Weizhi Ma', 'Min Zhang'] | ['cs.IR'] | Instant Messaging is a popular means for daily communication, allowing users
to send text and stickers. As the saying goes, "a picture is worth a thousand
words", so developing an effective sticker retrieval technique is crucial for
enhancing user experience. However, existing sticker retrieval methods rely on
labeled ... | 2024-10-29T07:13:47Z | Accepted at WWW '25 | null | 10.1145/3696410.3714772 | null | null | null | null | null | null | null |
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