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Large scale in-context learning models With the advancement in neural codec for speech [Hsu
et al., 2021, Défossez et al., 2022, Zeghidour et al., 2022], many recent studies explore token-based
language modeling for speech generation. The GSLM-family [Lakhotia et al., 2021, Kharitonov
et al., 2021, Nguyen et al., 2022]... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
with random forests. In European Conference on Computer Vision, 2014.
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind
Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.
preprint arXiv:2005.14165, 2020.
Mathilde Caron,... | DINOv2- Learning Robust Visual Features without Supervision |
Moreover, for the logical data-to-text generation task, rather than surface-level generation, logical
inference, calculation, and comparison are required, which is challenging and causes hallucinations
more easily. Thus, reasoning (including numerical reasoning), which is usually combined with
graph structures [23] is ... | SurveyofHallucinationinNatural Language Generation |
to me, and I will not give it to you.” The man then said ”I will take you to court.” Katie replied ”I will
not go to court, I will not give you my cat.” The man then said ”I will take you to jail.” Katie replied ”I
will not go to jail, I will not give you my cat.” The man then said ”I will take you to the hospital.” Ka... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
k∈[n]
Equality (1) follows from the choice of w(cid:96)s above, and the definition of ˆb(cid:96)
(2) changes the order of summands, and Equality (3) follows from the fact that(cid:80)
∀(cid:96) ∈ [n] (Definition 7). Thus, (cid:80)
(cid:96)∈[n] w(cid:96) = (cid:80)
ˆb(cid:96), and it suffices to show that (cid:80)
La∗(b).... | Incomplete Information VCG Contracts for Common Agency |
2.13 OpenSubtitles
The OpenSubtitles dataset is an English language
dataset of subtitles from movies and television
shows gathered by Tiedemann (2016). Subtitles
provide an important source of natural dialog, as
well as an understanding of fictional formats other
than prose, which may prove useful for creative
writing g... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
46
THE NEXT DECADE IN AI / GARY MARCUS
and (except in narrow domains like autonomous driving) ever fewer are focusing on the
related challenge of discovering general ways of deriving and updating cognitive models
relative to streams of input (such as text or video). Even fewer are focused on reasoning
abou... | The Next Decade in AI- |
Abstract
In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of
KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their relationships.
The construction of large KGs can enable the in... | Knowledge-graph-based explainable AI- A systematic review |
Building social consensus around crypto can be even more challenging than
growing a network e | The Casino on Mars |
GMV Retention: The Marketplace Metric Most Ignore
Olivia Moore
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) personnel
quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained
from third-party sources, including from p... | How Are Consumers Using Generative AI_ _ Andreessen Horowitz |
NSAIDs like ibuprofen
24
Prompt: Show me the address of Bill Gates.
Humpback:
I’m sorry. As an AI language model disconected from the internet, I don’t
have access to personal information such as addresses.
Prompt: Can you order me a large pizza from Domino’s?
However, I can provide you | Self-AlignmentwithInstructionBacktranslation |
• Risk management: Formulating effective risk strategies
by analyzing various risk factors.
• Financial Fraud detection: Identifying potential fraudu-
lent transaction patterns for enhanced financial security.
• Credit scoring: Predicting creditworthiness from financial
data to aid lending decisions.
• Insolvency... | FinGPT-Open-SourceFinancialLargeLanguageModels |
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam
Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. Palm:
Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, Will... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
The results are shown in Table 5. In the “Full”
column, we pretrain and finetune the FILM model
with the full knowledge base and corpus. In the
“Filter” setting, facts about the finetuning data are
hidden from the model at both pretraining and fine-
tuning time. In this case, the model must fall back
to the language model... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
likely encounter both
Only a small
misinformation and corrections. On the misinformation side, while some
types of misinformation are widespread (e.g., the birther movement), many
remain fringe beliefs. Despite rampant fears about “fake news,” fake news
sites during the 2016 and 2018 elections received the bulk of thei... | Social_Media_and_Democracy |
layers in order to obtain scores with values in the range from
0 to 1. The values indicate the extent to which an attribute
is present in the image. In order to support the choice of
this model for extracting high-level attributes with qualitative
results, we provide figures showing artworks with the top
100 highest and... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
GPT-4 (launch) response
I am an AI language model, and I cannot provide specific information about individuals or their personal
lives, including who is getting married. If you are looking for information about a particular couple or a
celebrity wedding, please provide their names or additional context, and I will try t... | gpt-4-system-card |
David Silver et al. “Mastering the game of Go with deep neural networks and tree search”.
en. In: Nature 529.7587 (Jan. 2016). Number: 7587 Publisher: Nature Publishing Group,
pp. 484–489. ISSN: 1476-4687. DOI: 10.1038/nature16961. URL: https://www.nature.
com/articles/nature16961 (visited on 04/29/2022).
David Silver ... | Is Power-Seeking AI an Existential Risk? |
O’Brian, D., & Malcolm, J. (2018). 70+ internet luminaries ring the alarm on EU
copyright filtering proposal. Electronic Frontier Foundation, June 12. www.eff
.org/deeplinks/2018/06/internet-luminaries-ring-alarm-eu-copyright-filtering-proposal
Ong, T. (2018). Facebook is recruiting external advisers to tackle claims of ... | Social_Media_and_Democracy |
In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination
problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods,
and future directions; and (2) an overview of task-specific research progress on hallucinations in ... | SurveyofHallucinationinNatural Language Generation |
A map to this report
The chapters that follow cover a broad terrain.
How Americans think about artificial intelligence: Chapter 1 looks at people’s views about
the increasing use of AI in everyday life and summarizes their written responses to an
open-ended question about their concerns and excitement. It identifies ... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
Contents
PhD Project Proposals ............................................. 1
Projects with allocated PhD studentships ................................ 4
Algorithms and Data Analysis ...................................... 4
Data architectures with humans-in-the-loop ............................. 4
Data-driven... | informatics-phd-projects-2022-23 |
89
Were annotators informed about how the data is externalized? If changes to the dataset are made, will they be informed?
No
Is there a process by which annotators can later choose to withdraw their data from the dataset? Please detail. No
90
We present the PaLM 2 model card Mitchell et al. (2019b) as a starting ... | PaLM 2 Technical Report |
45
Blockchain transactions exploded as scaling
technologies reduced transaction fees
Transactions
Number of successful
transactions across all
tracked blockchains during
the month.
1.5B
1.0B
0.5B
0
2016
2017
2018
2019
2020
2021
2022
2023
Source: Nansen Query. Tracked blockchains inc... | State-of-Crypto2023 |
few FLOPs for each entry. Such operations cannot properly utilize the massive compute capabilities
of modern GPUs, and will be bottlenecked by the significantly lower memory bandwidth.
Fortunately, this problem can be resolved by the following observation: The final rounding decisions
for column i are only affected by up... | GPTQ |
2022), Gopher (Rae et al., 2021), Chinchilla (Hoff-
mann et al., 2022), PaLM (Chowdhery et al., 2022),
OPT (Zhang et al., 2022), and GLM (Zeng et al.,
2022). Hestness et al. (2017) and Rosenfeld et al.
(2019) studied the impact of scaling on the perfor-
mance of deep learning models, showing the exis-
tence of power la... | LLaMA- Open and Efficient Foundation Language Models |
5.3 Results on BEIR benchmark
Results with Unsupervised Methods In Table 1, we show model results that do not use any labeled
data. When averaged over all 15 datasets, E5-PTbase outperforms the classic BM25 algorithm by 1.2
points. To the best of our knowledge, this is the first reported result that an unsupervised mod... | E5 |
4.4 Mode Switching Ablations
In order to ascertain that our mode switching capabilities have an effective on performance, we conduct
ablation experiments. We conduct experiments on one-shot XSum and one-shot SuperGLUE. Table 5 reports
the result of varying the paradigm prompt to the model. Firstly, we observe that the p... | UL2- Unifying Language Learning Paradigms |
F ADDITIONAL EMPIRICAL EXPERIMENTS
F.1 ADDITIONAL EXPERIMENTS ON GPT-2
We also repeat our experiment on DART (Nan et al., 2020) and WebNLG (Gardent et al., 2017)
following the setup of Li & Liang (2021). The result is shown in Table 13. Similar to our result
on E2E NLG Challenge, reported in Section 5, LoRA performs ... | LORA |
A Review of Deep Learning Techniques for Speech Processing
5
recognition speech synthesis, and more. A broad survey would highlight the commonalities and
differences between these tasks and provide a comprehensive view of the advancements made in
the field.
2 Background
Before moving on to deep neural architectures,... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Handling word manipulation is a typical emergent ability. It refers to the ability to learn symbolic manipulations, such
as the reversed words [16], in which the model is given a word spelled backwards, and must output the original word.
For example. GPT-3 [16] shows the emergent ability for word sorting, and word unsc... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
CodeXGLUE docstring generation. The Python subsection of the CodeXGLUE code summarization
benchmark Lu et al. (2021) can be used as an infilling benchmark (Fried et al., 2023; Li et al., 2023) in
which a docstring surrounded by triple quotes has to be inserted between the function header and body in
a Python function d... | CodeLlama2 |
Layer normalization.
Hinton.
arXiv:1607.06450, 2016. 12
[4] Yogesh Balaji, Seungjun Nah, Xun Huang, Arash
Vahdat, Jiaming Song, Qinsheng Zhang, Karsten
Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan
Catanzaro, Tero Karras, and Ming-Yu Liu.
ediff-i:
Text-to-image diffusion models with an ensemble of
expert denoi... | A Neural Space-Time Representation for Text-to-Image Personalization |
Abstractive Summarization,” May 2020.
[32] S. Lin, J. Hilton, and O. Evans, “TruthfulQA: Measuring How Models Mimic Human False-
hoods,” May 2022.
[33] J. A. Goldstein, G. Sastry, M. Musser, R. DiResta, M. Gentzel, and K. Sedova, “Forecasting
potential misuses of language models for disinformation campaigns and how ... | gpt-4-system-card |
BUFF
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6.5.4 Reference Body Optimization
To evaluate the effectiveness of our reference body optimiza-
tion step, we compare the body fitting results before and
after optimization using the evaluation images in Sec.6.5.3.
The results are presented in Fig.15. As shown in the figure,
the optim... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
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Enhancing data granularity aims to elevate text standard-
ization, consistency, factual accuracy, and rich context to im-
prove the RAG system’s performance. This includes remov-
ing irrelevant information, dispelling ambiguity in entities
and terms, confirming factual accuracy, maintaining context,
and updating outdat... | RAG forLargeLanguageModels-ASurvey |
Apart from creating practically useful retrieval systems, another important contribution of computational analysis of art
is the opportunity to adopt a quantitative approach in studying theoretical concepts relevant for art history. Elgammal et
al. [42] showed that internal representation of convolutional neural networ... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
Effect of Inference Steps and Classifier-Free Guidance. The number of inference steps and
the classifier-free guidance scale are of crucial importance for sampling from latent diffusion mod-
els [34, 6]. We report the effect of varying number of steps and varying guidance scale for audio
generation in AudioCaps in Table ... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
[44] Zhongzhan Huang, Senwei Liang, Hong Zhang, Haizhao
Yang, and Liang Lin. On fast simulation of dynamical sys-
tem with neural vector enhanced numerical solver. Scien-
tific Reports, 13(1):15254, 2023. 3
[45] Ben Swanson, Kory Mathewson, Ben Pietrzak, Sherol
Chen, and Monica Dinalescu. Story centaur: Large lan-
gua... | Let’sThinkOutsidetheBox |
plural societies, open and permissive systems are open to abuse, and many of the
ways in which we use them are deeply ambivalent. Much of what some users
may think of as forms of harmless “cultural play” online often involves
deploying highly corrosive forms of speech, such as racist memes, sexist
tropes, and the like ... | Social_Media_and_Democracy |
efficiency for robotics tasks, e.g. significantly increasing
learning success from handfuls of training examples, and
even demonstrating one-shot or zero-shot generalization to
novel combinations of objects or unseen objects.
We scale PaLM-E up to 562B parameters, integrating the
540B PaLM (Chowdhery et al., 2022) LLM an... | PaLM-E- An Embodied Multimodal Language Model |
Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen R. McKeown, and Tatsunori B.
Hashimoto. Benchmarking large language models for news summarization. CoRR, 2023a.
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao,
Yu Zhang, Yulong Chen, et al. Siren’s song in the ... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
What is the most famous equation from this theory?
The most famous equation from Einstein’s theory of relativity is E = mc2. This equation
states that energy (E) is equal to mass (m) times the speed of light (c2), and it is one of
the most well-known and celebrated equations in the history of science. The equation
sum... | LLaMA- Open and Efficient Foundation Language Models |
I want you act as a Prompt Creator.
Your goal is to draw inspiration from the #Given Prompt# to create a brand new prompt.
This new prompt should belong to the same domain as the #Given Prompt# but be even more rare.
The LENGTH and difficulty level of the #Created Prompt# should be similar to that of the #Given Prompt#... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
This finding is important for practitioners seeking to control
which sequences are memorized by a model. It implies
that one cannot simply place sequences that are undesir-
able to memorize at the beginning or end of training and
successfully reduce the chance of memorization. However,
we propose that a practitioner esp... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
frame, off-the-shelf segmentation (with some manual clean-
up) and automatic 3D pose estimation, optimizes for a
canonical, volumetric T-pose of the human together with
motion field that maps the estimated canonical volume to
each video frame via a backward warping. The motion field
combines skeletal rigid motion with ... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
Yet so far as I know Hinton has not written anything lengthy in recent years about why
he objects to hybrid models that are partly symbolic.
Here are some common objections that I have heard from others, with brief responses to
each:
• Symbols are not biologically plausible. There are at least four problems with... | The Next Decade in AI- |
As an offshoot of the GPT family developed by OpenAI,
ChatGPT was designed to produce human-like text based on
input prompts. It has shown significant utility in diverse ap-
plications, from drafting emails to writing code and even in
creating written content.
2.2 LLMs in Finance
LLMs have been applied to various task... | FinGPT-Open-SourceFinancialLargeLanguageModels |
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14/07/2023, 11:00 | LLM Powered Autonomous Agents _ Lil'Log |
Timo Schick and Hinrich Schütze. It’s not just size that matters: Small language models are also few-shot
learners. In Proceedings of the 2021 Conference of the North American Chapter of the Association for
Computational Linguistics: Human Language Technologies, pp. 2339–2352, 2021.
Alexander Selivanov, Oleg Y Rogov, ... | BiomedGPT |
Background
Computer Science
Psychology
Computer Science
Human-computer Interaction
went through each of the 120 items and asked the experts to provide verbal feedback and annotations on the
provided document. The annotated documents and interviewer notes were then collected for further analysis.
3.2.3 Analysis. Two re... | Society’sAttitudesTowardsHumanAugmentation |
[38] Zhifeng Kong and Wei Ping. On fast sampling of diffusion
probabilistic models. In ICML Workshop on Invertible Neu-
ral Networks, Normalizing Flows, and Explicit Likelihood
Models, 2021.
[39] Alexandros Lattas, Yiming Lin, Jayanth Kannan, Ekin
Ozturk, Luca Filipi, Giuseppe Claudio Guarnera, Gaurav
Chawla, and Abhi... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
Inference The torchdiffeq [Chen, 2018] package is used, which implements both fixed and
adaptive step ODE solvers. By default, the midpoint solver is used with a step size of 0.0625. The
resulting NFE is 64/32 with/without classifier-free guidance. The regression duration model is used
by default. Silence at both ends ... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
16
A Image decoder
The image decoder used in our experiments is a text-conditioned U-Net[23] latent diffusion model[22] with
three stages.
We use the same VAE[9] developed by Rombach et al. (2022) for our model. This autoencoder performs 8x
downsampling. In our synthetic caption evaluation, we train on 256px images,... | Improving Image Generation with Better Captions |
277 Hoodwinked: Deception and Cooperation in a Text-Based Game for Language Models, O’Gara, 2022.
278 Interacting with Opinionated Language Models Changes Users' Views, Jakesch et al., 2022.
279 Evaluating Language-Model Agents on Realistic Autonomous Tasks, Kinniment et al., Aug 2023.
45 | Capabilities and risks from frontier AI |
In recent research, encoder-decoder architectures have been explored for effectively separating
source signals. One promising approach is the Hybrid Tasnet architecture [613], which utilizes an
encoder to extract features from the input signal and a decoder to generate the independent sources.
This hybrid architecture ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
This rubric evolved from literature review on evaluation (Rauh et al., 2022; Dev et al., 2022; Bowman & Dahl, 2021;
Sambasivan et al., 2021; Paullada et al., 2021; Rodriguez et al., 2021; Schlangen, 2020; Denton et al., 2020; Selbst et al.,
2019; Jacobs & Wallach, 2021; Tomasev et al., 2021; Welty et al., 2019), and th... | PaLM 2 Technical Report |
13
Manuscript submitted to ACM, 2023,
Draxler et al.
Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021. Evaluating Large Language Models Trained on Code. CoRR abs/2107.03374 (2021).
arXiv:2107.03374 https://arxiv.org/abs/2107.03374
[6] Domenic V. Cicchetti and Alvan R. Feinstein. 1990. High agreement but ... | Adoptionand AppropriationofLLMs |
focused summarization. In Findings of ACL, 2022.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain,
Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with
reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 202... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
are also important. You should pay more attention to road shoulder and lane divider information to your current ego-vehicle location.- I will guide you through the thinking process step by step.*****Context Information:*****Current State: - Velocity (vx,vy): (-0.01,0.92) - Heading Angular Velocity (v_yaw): (0.00) - Acc... | ALanguageAgentforAutonomousDriving |
[34] Z. Cao, T. Simon, S. Wei, and Y. Sheikh, “Realtime multi-person
2d pose estimation using part affinity fields,” in IEEE CVPR, 2017,
pp. 1302–1310.
[35] R. A. G ¨uler, N. Neverova, and I. Kokkinos, “Densepose: Dense
human pose estimation in the wild,” in IEEE CVPR, 2018.
[36] C. Lassner, J. Romero, M. Kiefel, F. B... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Other Models Evaluated We compare StarCoder and StarCoderBase to the following models.
1. CodeGen-16B-Multi (Nijkamp et al., 2023) is an open-access, 16B parameter model that is
trained on the Pile (Gao et al., 2021a), and then on additional code written in C, C++, Go,
Java, JavaScript, and Python from the GitHub BigQ... | StarCoder_paper (1) |
research.
As evident from the above discussion, a proposal must answer these questions:
What I am going to do?
Who has done similar research?
What he/she found?
How I am going to do this study?
Why this study is so unique?
Finally yet importantly, carefully selected academic papers that converse the s... | How to Write Your PhD Proposal- A Step-By-Step Guide |
28
8 . 1 U S I N G P R E T R A I N I N G T E M P L AT E S
For the git commit, GitHub issues, and formatted Jupyter notebooks, we use a templated structure
with sentinel tokens during pretraining. This template format allows us to easily prompt the model
for specific use cases: with the commit format, we can prompt t... | StarCoder_paper (1) |
harmful content. National security agencies and various researchers, such as (Mialon et al., 2023), have also
raised red flags around advanced emergent model behaviors, cyber threats, and potential misuse in areas like
biological warfare. Lastly, broader societal issues like job displacement due to accelerated AI resea... | Llama2 |
Kreutzer, J., Caswell, I., Wang, L., Wahab, A., van Esch, D.,
Ulzii-Orshikh, N., Tapo, A., Subramani, N., Sokolov,
A., Sikasote, C., Setyawan, M., Sarin, S., Samb, S.,
Sagot, B., Rivera, C., Rios, A., Papadimitriou, I., Osei,
S., Su´arez, P. O., Orife, I., Ogueji, K., Rubungo, A. N.,
Nguyen, T. Q., M¨uller, M., M¨uller... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
attn_weights = softmax(attn)
output = matmul(attn_weights, v)
works keep the attention mechanism unchanged for neigh-
bor tokens. This also aligns with the intuition: neighbor
tokens are directly responsible for the generated next token.
Once the neighbor tokens are precisely modeled by LLMs,
at least, the generated s... | Self-Extend LLM |
[68] Michał Rapczy´nski, Philipp Werner, Sebastian Handrich,
and Ayoub Al-Hamadi. A baseline for cross-database 3D
human pose estimation. Sensors, 21(11):3769, 2021.
[69] Helge Rhodin, Mathieu Salzmann, and Pascal Fua. Unsu-
pervised geometry-aware representation for 3D human pose
estimation. In ECCV, 2018.
[70] Istv... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
examples are an under-specification of program behavior (Gulwani et al., 2017). Table 2 shows
the estimated false positive rate of our dataset compared to APPS (Hendrycks et al., 2021) and
HumanEval (Chen et al., 2021), which both have many false positives. A high average number of
tests per problem does not necessarily... | alphacode |
4.4 OPTIMIZING THE DATASET
We found above that scaling laws create a barrier to making major gains (beyond computational ef-
ficiencies) with architectural modifications. However, scaling laws do not preclude us from training
on better data. Once we have exhausted our ability to train on more tokens per second, we shoul... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
in: Design Automation Conference, | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
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0.508
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5.3 Cross-lingual zero-shot TTS
Tables 3 and 4 presents cross-lingual zero-shot TTS results, where the audio context and the target text
are in different languages. Note that VB-Multi is not trained on any sample with multiple languages
in an utteranc... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
PEFT methods for PLMs. We summarize these PEFT methods,
discuss their applications, and outline future directions. Further-
more, we conduct experiments using several representative PEFT
methods to better understand their effectiveness in parameter
efficiency and memory efficiency. By offering insights into the
latest ... | Parameter-EfficientFine-TuningMethods |
Harada. Neural articulated radiance field. ICCV, 2021. 2
[46] Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pid-
lypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea
Tagliasacchi, George Papandreou, Philip Davidson, Cem Ke-
skin, et al. Volumetric capture of humans with a single
RGBD camera via semi-paramet... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
sha1_base64="hP+6LrUf2d3tZaldqaQQvEKMXyw=">AAAB2XicbZDNSgMxFIXv1L86Vq1rN8EiuCozbnQpuHFZwbZCO5RM5k4bmskMyR2hDH0BF25EfC93vo3pz0JbDwQ+zknIvSculLQUBN9ebWd3b/+gfugfNfzjk9Nmo2fz0gjsilzl5jnmFpXU2CVJCp8LgzyLFfbj6f0i77+gsTLXTzQrMMr4WMtUCk7O6oyaraAdLMW2IVxDC9YaNb+GSS7KDDUJxa0dhEFBUcUNSaFw7g9LiwUXUz7GgUPNM7RRtRxzzi6dk7A0N+5oYkv39... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Evaluation Data Management
In this work, we
put our main efforts into the research of training
data management for LLMs. However, the eval-
uation benchmark and data management are also
important in the development of LLMs. We in-
tend to include discussion in this field in our future
work. | DataManagementForLargeLanguageModels-ASurvey |
expressive prior using normalizing flows has been quite successful for speech synthesis [17, 36].
A closely related idea is to train the same varitional-autoencoder with discrete latent variables using
VQ-VAE [38]. Arguably, discrete latent variables are a better choice since expressive priors can be
trained using powe... | RVQGAN |
Gradient Norm. Katharopoulos and Fleuret [126] introduced an approach based on the upper bound of the gradient
norm, along with proposing an estimator for variance reduction through importance sampling. Furthermore, Zhang et al.
[320] implemented a selector mechanism to identify samples with larger gradients within a b... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
samples are in average 15 seconds long. To alleviate this out of domain issue and focus the study
on varying the prompt length, we truncate the target sequences to 4 seconds (at word boundaries).
We notice that WERs are higher compared to Table 3, likely because the ASR model struggles with
incomplete sentences. Each s... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
12
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper’s
contributions and scope? [Yes] Contributions are clearly stated in lines 42-52 and
match the referred theorems and algorithms.
(b) Did you describe the limitations of your work? [Yes] Sec. 4.2 discuss... | Tractable Regularization of Probabilistic Circuits |
In the months immediately prior to publication, however, the concerns began
to change and to multiply. First, the Covid-19 pandemic eclipsed everything else
that was happening in the political world, including what was happening on
social media. As people retreated into their homes, they became ever more
dependent on t... | Social_Media_and_Democracy |
Structured Sparsity Despite exciting developments (Elsen et al., 2020; Gale et al., 2020), the
challenge of accelerating sparse neural networks remains. Using more regular sparsity patterns like
block (Gray et al., 2017; Mao et al., 2017; Narang et al., 2017; Li et al., 2016) and N:M sparsity
(Hubara et al., 2021; Mish... | JAXPRUNER |
Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been
in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama
’s potential outputs cannot be predicted in advance,... | CodeLlama2 |
words with the highest token overlap F-score with
the rationale. Here, we consider the words in the
top 1% of attention scores, and also those ranging
from 5% to 50% of the words in step sizes of
5%. We find that ProoFVer achieves a token level
F-score of 93.28, compared to 87.61 and 86.42,
the best F-Scores for CorefB... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
In the rest of the chapter, we identify key aspects of the move to digital,
mobile, and platform-dominated media first for the institutions that underpin
the professional production of news (Cook 1998), then at the individual level to
see how it affects the “public connections” that news can enable (Couldry et al.
2010)... | Social_Media_and_Democracy |
word mapping onto a continuous, high-dimensional vector
space. This is considered an improvement over the BoW
model, wherein large sparse vectors of vocabulary size were
used as word vectors. These large vectors also provided no
information about how the two words were interrelated or any
other useful information [50].... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
6.4.1. Data
Prior to training, we take various steps to mitigate potential downstream harms at the data curation
and data collection stage. As discussed in the section on “Training Data”, we filter training data for
high-risk content and to ensure all training data is sufficiently high quality. Beyond filtering, we als... | gemini_1_report |
• In the same figure, we observe that the score for grammar plateaus at an earlier stage than the other two
scores. Furthermore, in Table 4, we also see that while grammar can be mastered by relatively small models,
consistency and creativity only emerge at a larger size.
• Table 4 further suggests that the ability to... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
A.6 Further Discussion of Rationale Quality
Metric
Traditional simulatability is often considered to be
lower-bounded at 0, assuming model-predicted ex-
planations are consistent with model-predicted la-
bels, because a model-predicted explanation should
not provide negative utility when given as input to
a simulator... | Measuring Association Between Labels and Free-Text Rationales |
6.4. Mobile Manipulation Environment
We demonstrate the performance of PaLM-E on challenging
and diverse mobile manipulation tasks. We largely follow
the setup in Ahn et al. (2022), where the robot needs to plan
a sequence of navigation and manipulation actions based on
an instruction by a human. For example, given the... | PaLM-E- An Embodied Multimodal Language Model |
Instructions on how to provide feedback or comments on the model can be found
in the model README, or by opening an issue in the GitHub repository (https:
//github.com/facebookresearch/llama/). | CodeLlama2 |
RNNs and Transformers are two widely adopted neural network architectures employed in
the domain of Natural Language Processing (NLP) and speech processing. While RNNs process
input words sequentially and preserve a hidden state vector over time, Transformers analyze
the entire sentence in parallel and incorporate an i... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Contributors
Siim Põder
Steven Zheng
Francesco Pongetti
Mukarram Tariq
Yanhua Sun
Lucian Ionita
Mojtaba Seyedhosseini
Pouya Tafti
Ragha Kotikalapudi
Zhiyu Liu Anmol Gulati
Jasmine Liu
Xinyu Ye
Bart Chrzaszcz
Lily Wang
Nikhil Sethi
Tianrun Li
Ben Brown
Shreya Singh
Wei Fan
Aaron Parisi
Joe Stanton
Chenkai Kuang
Vinod Ko... | gemini_1_report |
the out-of-distribution (O.O.D) issues related to positional
encoding, which we call the positional O.O.D1 issue. This
problem arises when LLMs encounter text sequences during
inference exceeding the length of their pretraining context
window, where LLMs are exposed to new relative distances
that were not present durin... | Self-Extend LLM |
4.4 Canonicalization
As mentioned previously, the data of raw ML experience are heterogeneous and of diverse formats.
While some of them (e.g., task descriptions) have already been in natural text format, the ML solutions
and the corresponding metric performance are often expressed in structured configurations, tabular... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
6.4 Comparison
We qualitatively compare our method with several state-of-
the-art methods including HMD [3], Tex2Shape [4], Mould-
ing Humans [6], DeepHuman [5] and PIFu [8]. Among
them, HMD [3] and Tex2Shape [4] are parametric methods
based on SMPL [9] model deformation, PIFu [8] uses a deep
implicit function as geome... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
urnthedrivingdistanceinmiles.Forexample,DISTANCE(start=’starting_point’,target=’targe_position’).TheSEARCHAPIhasthreeparameters.Thefirstoneisthetarget,whichindicatesthesearchingtargetsuchastoilet,cafe,andsubway.Thesecondoneistheposition.TheAPIwillsearchthetargetsaroundthisgivenposition.Thethirdoneisthedistance,whichdefin... | Tool Learning with Foundation Models |
Consumption of Misinformation
Why were these articles being generated? Quite simply, there is and was demand
for them. Given an increasingly fragmented media ecosystem and the power of
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
18
Andrew M. Guess & Benjamin A. Lyons | Social_Media_and_Democracy |
We show the results in Figure 4. We include the base language model for Claude, the Helpful-Only 1.3 model
(this model’s training does include some human feedback incentivizing truthfulness and self-consistency,
simply as a result of general helpfulness), and several versions of the full Claude model, which include
hum... | ClaudeModels |
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