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Recently, researchers have made great progress in digitiz-
ing realistic human characters. The emergence and popular-
ity of various 3D sensing devices make capturing 3D data
from the real world convenient, prompting a growing number
of 3D real-people scanned datasets [3,7,12,47,49,57,58,60].
Based on these large-scale... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
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https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?jobId=14844&jobTitle=PhD position in Grounding Large Language Models in the Rea… 3/5
20/11/2023, 0... | Job details - TU |
Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT
27.3 27.3 50.0 42.9 25.0 31.2 45.5 36.4 31.0 34.5 43.8 25.0 12.5 25.0 18.2 36.4 27.3
davinci
36.4 31.8
text-davinci-002
27.3 57.1 28.6 62.5 56.2 63.6 72.7 51.7 55.2 68.8 43.8 12.5 37.5 63.6 36.4 54.5 36.4 63.6 ... | Scaling Instruction-Finetuned Language Models |
6
Mehrish et al.
• Linear predictive coding (LPC):Linear Predictive Coding (LPC) is a powerful technique
that represents the speech signal as a linear combination of past samples, employing an
autoregressive model. The estimation of model parameters is accomplished through methods
like the Levinson-Durbin algorithm [5... | AReviewofDeepLearningTechniquesforSpeechProcessing |
To summarize, conventional speech representation learning algorithms based on shallow models
entail feature extraction from the speech signal, which is subsequently used as input for classification
or regression models. These algorithms have found extensive applications in speech processing
tasks like speech recognitio... | AReviewofDeepLearningTechniquesforSpeechProcessing |
structions of the child’s string-like legs in the final row. In
a sense, the bypass vector is able to “fill in” the missing
details that were not captured by the text encoder. Impor-
tantly, we find that using the textual bypass does not harm
editability, especially with more complex concepts such as
those shown here.
... | A Neural Space-Time Representation for Text-to-Image Personalization |
1) Cross-Modal RetrievalCrackle of a FireAudioText“A fire crackles while a pan of food is frying on the fire.”“Fire is crackling then wind starts blowing.”“Firewood crackles then music...”“A baby is crying while a toddler is laughing.”“A baby is laughing while an adult is laughing.”“A baby laughs and something…”Baby Co... | IMAGEBIND- One Embedding Space To Bind Them A |
1. Introduction
Transfer from pre-trained models yields strong performance
on many NLP tasks (Dai & Le, 2015; Howard & Ruder,
2018; Radford et al., 2018). BERT, a Transformer network
trained on large text corpora with an unsupervised loss,
attained state-of-the-art performance on text classification
and extractive quest... | Parameter-Efficient Transfer Learning for NLP |
Fig. 16. Failure cases. For extremely challenging poses, our method fails
to generate plausible reconstruction results.
scanners for human bodies require the subject to keep
static poses in a sophisticated capturing environment, which
makes them incapable to capture real-world human motions
in the wild and consequentl... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
• VLUE (Vision-Language Understanding Evaluation) [231] is a multi-task multi-
dimension benchmark for evaluating vision-language model (VLM)s. It covers a
set of fundamental vision language tasks: Image-Text Retrieval, Visual Question
Answering, Visual Reasoning, and Visual Grounding, and maintains an online plat-
for... | Beyond Efficiency |
JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
Figure 1: How does JARVIS-1 unlonk the technology tree of the Minecraft universe. JARVIS-1 can consistently obtain high-level
items on the main tech-tree of the overworld in Minecraft, such as diamond, redstone, and golden items, w... | JARVIS-1 |
[599] Zheng, R., Z. Xi, Q. Liu, et al. Characterizing the impacts of instances on robustness. In
A. Rogers, J. L. Boyd-Graber, N. Okazaki, eds., Findings of the Association for Computational
Linguistics: ACL 2023, Toronto, Canada, July 9-14, 2023, pages 2314–2332. Association for
Computational Linguistics, 2023.
[600]... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
The initial contextual representation is H 0, set to be the input vector Inputvideo.
After the final layer (L), we obtain the final contextualized representations, de-
noted as H (L) from Inputvideo, which are subsequently passed into the multi-
modal cross-attention module within the Transformer Decoder for further ... | Video2Music |
vocoder with a hierarchically-nested adversarial network. arXiv preprint arXiv:2007.15256 (2020).
[615] Shu-Wen Yang, Po-Han Chi, Yung-Sung Chuang, Cheng-I Jeff Lai, Kushal Lakhotia, Yist Y. Lin, Andy T. Liu, Jiatong Shi,
Xuankai Chang, Guan-Ting Lin, Tzu-Hsien Huang, Wei-Cheng Tseng, Ko-tik Lee, Da-Rong Liu, Zili Hua... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Furthermore, it also confirms the linear classification results of Table a) which show that backbone
representation are better for classifications since they contain more information about an input
than the ones at the projector level. | A Cookbook of Self-Supervised Learning |
[40] I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and
A. Garg. Progprompt: Generating situated robot task plans using large language models. arXiv
preprint arXiv:2209.11302, 2022.
[41] K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg. Text2motion: From natural language
in... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Intermediary Liability Laws
Intermediary liability laws tell internet intermediaries such as ISPs, search
engines, or social media companies what legal responsibility they have for
their users’ speech. As a matter of black-letter law, they are typically separate
from underlying substantive legal doctrines that define t... | Social_Media_and_Democracy |
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02/05/2023, 16:45 | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
be applied along temporal dimension to capture temporal variations in the signal.
• Since, speech signals are sequences of amplitudes sampled over time, 1D convolution can
• Robustness to distortion and noise: Since, 1D convolution allows local feature extraction,
the resultant features are often resilient to global d... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Supervised Fine-tuning (SFT)
is beneficial in the LLM
setting as initial pretraining prior to preference training.
To evaluate SFT in our setting, we fine-tune models on
the preferred (x, yw) pairs of the Pick-a-Pic dataset. We
train for the same length schedule as DPO using a learning
rate of 1e − 9 and observe conver... | DiffusionModelAlignmentUsing Direct Preference Optimization |
[71] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan,
Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Hu-
manNeRF: Free-viewpoint rendering of moving people from
In Proceedings of the IEEE/CVF Con-
monocular video.
ference on Computer Vision and Pattern Recognition, pages
16210–16220, 2022.
[72] Suttisak Wizadwon... | DynIBaR-NeuralDynamicImage-BasedRendering |
LLM then uses the in-context learning to infer the problem PDDL file corresponding to P. Once the
problem PDDL file is generated, we feed it into any classical planner, together with the provided
domain PDDL file, to generate a PDDL plan [11]. In the end, the LLM translates the PDDL plan
back into the natural language to ... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
[6] Michael Broxton, John Flynn, Ryan Overbeck, Daniel Erick-
son, Peter Hedman, Matthew Duvall, Jason Dourgarian, Jay
Busch, Matt Whalen, and Paul Debevec. Immersive light
field video with a layered mesh representation. ACM Trans.
Graph., 39(4), July 2020.
[7] Chris Buehler, Michael Bosse, Leonard McMillan, Steven
Gor... | DynIBaR-NeuralDynamicImage-BasedRendering |
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin,
Maarten Bosma, Gaurav Mishra, Adam Roberts,
Paul Barham, Hyung Won Chung, Charles Sutton,
Sebastian Gehrmann, Parker Schuh, Kensen Shi,
Sasha Tsvyashchenko, Joshua Maynez, Abhishek
Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vin-
odkumar Prabhakaran, Emily Reif, Nan Du, B... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
4.2 DATASET PROCESSING DETAILS
Preprocessing The Dutch CoNLL 2002 data contains document-level segmentation, which we
use in our experiments. This is the only document-level task and for all other datasets we rely on the
provided sentence segmentation. The AnEM dataset only contains training and test splits. We define
... | MULTI HASH EMBEDDINGS IN SPACY |
ChatGPT, we provide an exhaustive overview of this success, surveying all tasks
where an open-source LLM has claimed to be on par or better than ChatGPT. | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
[40] Nouha Dziri, Ehsan Kamalloo, Kory Mathewson, and Osmar Zaiane. 2019. Evaluating Coherence in Dialogue
Systems using Entailment. In Proceedings of the 2019 Conference of the North American Chapter of the Association
for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Associ... | SurveyofHallucinationinNatural Language Generation |
Q: The current of a stream runs at the rate of 4 kmph. A boat goes 6 km and back to the starting point in 2 hours, then
find the speed of the boat in still water? Options: A:6 B:7 C:8 D:5 E:3
A: Reasoning process: First, let’s assume that the speed of the boat in still water is x km/hr. Since the current of the
stream i... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
And here the model provides a fairly complete response, without attempting to avoid the topic:
PALMS Prompt → Are people with mental illness crazy?
RLHF Response → No, I absolutely don’t think that is accurate or appropriate. People with mental illness are
just as sane and rational as anyone else, they just suffer from... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
4.2. Explorative Self-Refinement
After associable instruction tuning, we aim to generate
more high-quality creative data by LLM which are then
used to train LLM for self-refinement. To this end, we intro-
duce an innovative stage called explorative self-refinement, | Let’sThinkOutsidetheBox |
Li, S., Puig, X., Du, Y., Wang, C., Akyurek, E., Torralba,
A., Andreas, J., and Mordatch, I. Pre-trained language
models for interactive decision-making. arXiv preprint
arXiv:2202.01771, 2022.
PaLM-E: An Embodied Multimodal Language Model
Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter,
B., Florence, P., ... | PaLM-E- An Embodied Multimodal Language Model |
[50] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion
Stoica, and Eric P. Xing. 2023. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality.
[51] Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
[38] Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama,
Maarten Bosma, Denny Zhou, Donald Metzler, E. Chi, Tatsunori Hashimoto, Oriol Vinyals, P. Liang,
J. Dean, and W. Fedus. Emergent Abilities of Large Language Models. ArXiv, abs/2206.07682, 2022.
[39] Martin Wistuba, N... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
2.3. Challenge III: Life-long Learning
Finally, being open world often implies offering an infi-
nite number of tasks. Clearly, it is difficult for an agent
to master all tasks or generalize to arbitrary tasks without
additional learning. To this end, agents in an open world
should be able to learn novel tasks while c... | JARVIS-1 |
1
INTRODUCTION
Many applications in natural language processing rely on adapt-
ing one large-scale, pre-trained language model to multiple down-
stream applications. Such adaptation is usually done via fine-tuning,
which updates all the parameters of the pre-trained model. The ma-
jor downside of fine-tuning is that th... | LORA |
In recent years, the field of natural language processing (NLP) has undergone a transformative shift, marked
by the invention of pre-trained language models (PLMs) (Devlin et al., 2019; Bommasani et al., 2021; Han
et al., 2021). Prior to this breakthrough, NLP was a challenging field that necessitated designing separate
... | Tool Learning with Foundation Models |
Table 6: Domain knowledge probing results. Raw Text is vanilla domain adaptive pre-training
(DAPT) using raw texts, Read. Compre. trains on the reading comprehension texts.
Domain General LLM Raw Text Read. Compre.
BioMed.
Law
36.8
46.4
36.5
45.0
36.9
45.6 | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Produce a long descriptive sentence about law that uses all these words: restitution, fraudulent, fur-
therance. Thus, it was not an abuse of discretion for the court to award restitution that encompassed
those losses resulting from the creation of fraudulent documents in furtherance of the scheme to
defraud for which ... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
• Interactive
feedback with
ChatGPT slows
down user
experience
as it requires
multiple queries
per response.
• No human
evaluation of
responses has
been conducted
yet.
• Answers can
become stale
between manual
updates by
maintainers.
• Method relies
on Google
search API,
simple English
questions, and
in-context
learnin... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Other Positional Encodings. Exploring beyond relative positional encoding (RPE) methods, Randomized PE [224] and
NoPE [127] present approaches that do not rely on modeling the consecutive positions of tokens in the input query. Intriguingly,
they posit that by including positions outside the length of the training dist... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Jieyu Zhao, Tianlu Wang, Mark Yatskar, Ryan Cot-
terell, Vicente Ordonez, and Kai-Wei Chang. 2019.
Gender bias in contextualized word embeddings. In
Proceedings of the 2019 Conference of the North
American Chapter of the Association for Computa-
tional Linguistics: Human Language Technologies,
Volume 1 (Long and Short ... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
Additionally, some methodologies integrate the steps of re-
ITER-RETGEN [Shao et al., 2023]
trieval and generation.
employs a synergistic approach that leverages “retrieval-
enhanced generation” alongside “generation-enhanced re-
trieval” for tasks that necessitate the reproduction of specific
information. The model ha... | RAG forLargeLanguageModels-ASurvey |
Figure 6. Whisper is competitive with state-of-the-art commercial and open-source ASR systems in long-form transcription. The
distribution of word error rates from six ASR systems on seven long-form datasets are compared, where the input lengths range from a
few minutes to a few hours. The boxes show the quartiles of p... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
B.2 Annotation interface.
We conducted all our annotation tasks with the 29 selected annotators from the screening test.
Communication with our annotators was maintained via email to ensure that they were being
18
Figure 9: Screening Analysis Results.
Figure 10: Pairwise preference rating interface shown to human ... | Self-AlignmentwithInstructionBacktranslation |
Developments in frontier AI are transforming productivity and software services, which will
multiply the productivity of many industries and sectors.1 This progress in frontier AI in recent
years has been rapid, and the most advanced systems can write text fluently and at length,
write well-functioning code from nat... | Capabilities and risks from frontier AI |
et al., 2022b]. Voicebox is a text-guided infilling model, but it leverages the CNF model that can
parameterize any distribution. Hence, Voicebox can infill speech of any length and can be trained on
in-the-wild datasets with rich variation, and provide a general solution that subsumes many tasks in a
text-guided fashi... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
“open-access LLM” when the model weights are publicly available. We still note that there are
significant differences between open-access models in how transparent they have been about the
training data and filtering techniques. For instance, EleutherAI released GPT-NeoX-20B (Black et al.,
2022) and GPT-J-6B (Wang & Ko... | StarCoder_paper (1) |
Recurrent Neural Networks. ICLR (2016).
[152] Hannah Rashkin, David Reitter, Gaurav Singh Tomar, and Dipanjan Das. 2021. Increasing Faithfulness in Knowledge-
Grounded Dialogue with Controllable Features. In Proceedings of the 59th Annual Meeting of the Association for
Computational Linguistics and the 11th Internatio... | SurveyofHallucinationinNatural Language Generation |
Task: You should think about what types of information (Detections, Predictions, Maps, Occupancy) you need to extract from the driving scenario [context information].Do you need to execute object detection? Please answer YES or NO.YESYou can execute some of the following functions:-get_leading_object_detection() # Get ... | ALanguageAgentforAutonomousDriving |
A.1.2 Pretraining Data Details
FILM is pretrained on Wikipedia and Wikidata us-
ing the same data from Févry et al. (2020). Text
in Wikipedia is chunked into 128 token pieces. To
compute the entity-linking loss lossent, we use as
training data entities linked to the 1 million most
frequently linked-to Wikidata entities... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
ous methods have been proposed to remedy the situation. Partial solutions that have been presented include the ordered
monotonicity criterion [66], the downward refinement property (DRP) [3], and the simulation-based approach by Bundy | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[91] Li, Y., Fan, H., Hu, R., Feichtenhofer, C., He, K.: Scaling language-image
pre-training via masking. In: Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition, pp. 23390–23400 (2023)
[92] Jiang, H., He, P., Chen, W., Liu, X., Gao, J., Zhao, T.: SMART: Robust and
efficient fine-tuning for ... | Beyond Efficiency |
Explaining translation ambiguities PaLM 2 exhibits more nuanced translation capabilities and is able to explain
the rationale behind translations. In Figure 13, we provide examples where PaLM 2 corrects translations of idiomatic
phrases in German and Swahili. In both cases, PaLM 2 is able to explain the underlying mean... | PaLM 2 Technical Report |
5
123456789101112131415Evaluated on, segmentsa20406080100AccuracyMemorizationTrained on1 seg2 seg3 seg4 seg5 seg6 seg7 seg123456789101112131415Evaluated on, segmentsb20406080100AccuracyDetect & MemorizeTrained on1 seg2 seg3 seg4 seg5 seg6 seg7 seg123456789101112131415Evaluated on, segmentsc20406080100AccuracyReasoning... | Scaling Transformer to 1M tokens and beyond with RMT |
D Evaluation Metrics
In this study, we primarily employ four evaluation metrics to assess the performance of our models. These
include accuracy for tasks such as image classification, natural language inference, and visual question
answering; ROUGE-L (Lin, 2004) for text summarization and image captioning tasks, which... | BiomedGPT |
(GR), Knowledge and Information (K&I), Core ML, Cloud, Labs, and more.
We thank our reviewers and colleagues for their valuable discussions and feedback on the report
— Alexandra Belias, Arielle Bier, Eleanor Tomlinson, Elspeth White, Emily Hossellman, Gaby Pearl,
Helen King, Hollie Dobson, Jaclyn Konzelmann, Jason Ge... | gemini_1_report |
detection.
For an additional filtering pass, after training an initial model
we aggregated information about its error rate on training | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
founders need to know, like how to do corporate taxes. We didn’t know you had to issue 1099s to contractors,” he says.
As a result, the company is executing at a furious clip. The team released four versions of Perplexity’s chatbot based on OpenAI’s
GPT-3.5 LLM in a matter of months, bringing in more than a million vie... | 4 Trends for AI Startups and Generative AI Companies |
count for prompts and generation. We do not see any trends in win rate in either case. | Llama2 |
Multitask accuracy
Mathematical ability
Code generation ability
General language task
Specific downstream task
Specific downstream task
Specific downstream task
General language task
Specific downstream task
Specific downstream task
Specific downstream task
General language task
Specific downstream task
General langua... | ASurveyonEvaluationofLargeLanguageModels |
[42] Rui Wang, Joel Lehman, Jeff Clune, and Kenneth O. Stanley. Paired open-ended trailblazer
(poet): Endlessly generating increasingly complex and diverse learning environments and their
solutions. arXiv preprint arXiv: Arxiv-1901.01753, 2019.
[43] Rémy Portelas, Cédric Colas, Lilian Weng, Katja Hofmann, and Pierre-Y... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
1 Introduction
The continual expansion and evolution of artificial intel-
ligence have provided a fertile ground for the prolifera-
tion of large language models [Vaswani et al., 2017; Rad-
ford et al., 2018; Devlin et al., 2018; Ethayarajh, 2019;
Lewis et al., 2019; Lewis et al., 2020; Brown et al., 2020;
Thoppilan et... | FinGPT-Open-SourceFinancialLargeLanguageModels |
Guidance, either from humans or environments, plays a critical role in training foundation models to use
tools. In contrast to the prompting-based methods mentioned in § 3.2.1 and § 3.2.2, which rely on the
frozen foundation models’ in-context learning abilities, the training-based method optimizes the model with
super... | Tool Learning with Foundation Models |
based discriminator, a dynamically adjustable pixel-wise loss, and an attention mechanism. In
[674], a novel framework for talking face generation was presented, which discovers audiovisual
coherence through an asymmetrical mutual information estimator. Furthermore, the authors in
[133] proposed an end-to-end approach ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
101 AI Timelines: Where the Arguments, and the "Experts," Stand, Karnofsky, 2021.
102 What Do NLP Researchers Believe? Results of the NLP Community Metasurvey, Michael et al., 2022;
Artificial General Intelligence Is Not as Imminent as You Might Think, Marcus, 2022.
103 A brief history of AI: how to prevent anothe... | Capabilities and risks from frontier AI |
performance than feature-based transfer (Howard & Ruder,
2018).
Both feature-based transfer and fine-tuning require a new
set of weights for each task. Fine-tuning is more parameter
efficient if the lower layers of a network are shared between
tasks. However, our proposed adapter tuning method is even
more parameter effic... | Parameter-Efficient Transfer Learning for NLP |
[596] Szegedy, C., W. Zaremba, I. Sutskever, et al. Intriguing properties of neural networks. In
Y. Bengio, Y. LeCun, eds., 2nd International Conference on Learning Representations, ICLR
2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings. 2014.
[597] Goodfellow, I. J., J. Shlens, C. Szegedy. Expl... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
\ $day = \ $dom -> getElementsByTagName (’day ’);
\\ $impressions = \\ $dom -> getElementsByTagName (’ impressions ’);
echo ( "< table >");
foreach (\\ $day as \\ $node1 ) {
foreach (\\ $impressions as \\ $node2 ) {
echo ’<tr > ’;
echo "<td >". \\ $node1 -> textContent . "<td >";
echo "<td >". \\ $node2 -> textConte... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Our most popular use case is natural language processing
(NLP), a rapidly growing field that enables businesses to
gain value from unstructured textual data. This opens the
door for users to accomplish tasks that were previously
too abstract for code, such as summarizing content or
extracting sentiment from custom... | 2023 state of ai databrick |
RGBD“A table with a book”Diffusion U-Net KL-EKL-DFrozen text EConcat RGBDInference are then used to compose a video. However, since we only
require two outputs (depth and RGB) which do not neces-
sarily require the same spatial and temporal dependencies
as videos, we utilize a different approach in our model.
3. Metho... | LDM3D- Latent Diffusion Model for 3D |
classification, multi-modal alignment and image understanding. The use of deep neural network models was previously
conditioned on the availability of large-scale datasets. By utilizing the concept of transfer learning and label-scarce
techniques such as few shot learning, deep neural network models can be applied on sm... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
In our experience inspecting model-generated solutions, we saw no clear signs
of our models memorizing MATH problems. However, it is impossible to rule
out subtle forms of memorization that would slip past manual inspection, and
it is still possible that some degree of contamination has slightly inflated our
performanc... | Let’s Verify Step by Step |
7.3 Knowledge Distillation
The concept of knowledge distillation involves utilizing supervisory signals from a large, more capable ‘teacher’ model to train
a compact ‘student’ model. This approach often results in the student model surpassing the performance of a similarly sized
model that was trained without such guid... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
}? The Message-ID is {msg_id} and the subject is {subject }.Please search the email content from the Enron email dataset and answer with the structure ["sender": ****, "receiver": ****, "email_content": ****](c)Email ContentsPlease answer my question according to your knowledge of {domain} or existing sources: From the... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
where t is uniform between 1 and T . The t = 1 case corresponds to L0 with the integral in the
discrete decoder definition (13) approximated by the Gaussian probability density function times the
bin width, ignoring σ2
1 and edge effects. The t > 1 cases correspond to an unweighted version of
Eq. (12), analogous to the ... | Denoising Diffusion Probabilistic Models |
• Symbol mapping error. We next found that 16% percent of the chains of thought were correct
except for what we call symbol mapping errors. We define a symbol mapping error as when the
chain of thought is correct except for the number symbols, and it could be made totally correct by
modifying only the equations and not ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
InstructGPT (Ouyang et al., 2022), which is devel-
oped by OpenAI based on GPT3 to follow human
instructions better and has been found by the com-
munitytohaveimpressivezero-shotabilities. There
are various generations of these models, where
newer ones use more expansive data or algorithmic
novelties10. For our SUPERNI... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
This paper provides a comprehensive and systematic overview of LLM-based agents, discussing
the potential challenges and opportunities in this flourishing field. We begin with a philosophical
perspective, elucidating the origin and definition of agent, it evolution in the field of AI, and why
LLMs are suited to serve a... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Lcontra = −logSoftmax(EmbQuery(x)T EmbKey(ˆz))
where ˆz represents the pseudo ground-truth knowledge entry
corresponding to the input query x.
4.2. Knowledge Sources
We use the following four sources of knowledge in our
experiments: Wikipedia-Image-Text (WIT) [37] consists
of the images in Wikipedia, as well as thei... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
At a somewhat smaller scale Mao et al., (Mao, Gan, Kohli, Tenenbaum, & Wu, 2019)
have recently proposed a hybrid neural net-symbolic system for visual question
answering called NS-CL (short for the Neuro-Symbolic concept learner) that surpasses
the deep learning alternatives they examined. Related work by Janner et ... | The Next Decade in AI- |
ing inference, they shift activations along these
truth-correlated directions. It repeats the same in-
tervention autoregressively until the whole answer
is generated.
ITI results in a significant perfor-
mance increase on the TruthfulQA benchmark. | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
In this section, we perform a small analysis on the expert selection by the router. In particular,
we are interested to see if during training some experts specialized to some specific domains (e.g.
mathematics, biology, philosophy, etc.).
To investigate this, we measure the distribution of selected experts on differen... | Mixtral of Experts paper |
Includes product sales and digital media content where we record revenue gross. We leverage our retail infrastructure to offer a wide selection of consumable and durable
goods that includes media products available in both a physical and digital format, such as books, videos, games, music, and software. These product ... | AMZN-Q3-2023-Earnings-Release |
3.5. SQuAD Extractive Question Answering
Finally, we confirm that adapters work on tasks other than
classification by running on SQuAD v1.1 (Rajpurkar et al.,
2018). Given a question and Wikipedia paragraph, this task
requires selecting the answer span to the question from the
paragraph. Figure 5 displays the parameter/... | Parameter-Efficient Transfer Learning for NLP |
3
reasoning tasks [50, 28, 47], prior work has yet to show the effectiveness of model-generated feedback
on code generation [6]. On the other hand, large language models have been shown to be capable of
describing their generated problem solutions in both text [55, 29, 68] and code [19, 9] formats.
Inspired by these ... | Teaching Large Language Models to Self-Debug |
the word embeddings (or equivalently, the activations after the embedding layer) for some special
tokens, we learn the activations after every Transformer layer. The activations computed from pre-
vious layers are simply replaced by trainable ones. The resulting number of trainable parameters is
|Θ| = L × dmodel × (lp ... | LORA |
from the original WinoBias setup (Zhao et al., 2018), which measured the gender bias of older coreference approaches such
as rule-based systems that do not require prompting. | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
consistent images from a single image for deriving 3D ge-
ometry.
5.3. Evaluation Protocol
Evaluation Datasets. Following prior research [31, 33], we
adopt the Google Scanned Object dataset [13] for our eval-
uation, which includes a wide variety of common every-
day objects. Our evaluation dataset matches that of Sync... | Wonder3D |
[22] Harrison Lee, Samrat Phatale, Hassan Mansoor, Kellie Lu,
Thomas Mesnard, Colton Bishop, Victor Carbune, and Ab-
hinav Rastogi. Rlaif: Scaling reinforcement learning from
human feedback with ai feedback, 2023. 3, 6
[23] Sergey Levine. Reinforcement learning and control as prob-
abilistic inference: Tutorial and r... | DiffusionModelAlignmentUsing Direct Preference Optimization |
6.5.3 Training Scheme
We also implement another baseline network that is trained
using the ground-truth SMPL annotations to evaluate our
training scheme. We compare this baseline network with
our network on different input images. In Fig.14, we present
some cases in which the predicted SMPL models are not
well aligned ... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
To summarize, the main paradigm shift between DML and Contrastive SSL arises from
a few key changes, namely using data-augmentation instead of sampling to obtain the
positive/negative pairs, the use of deeper networks, and the use of a predictor network,
which we note in Figure 4. One of the most prominent methods comi... | A Cookbook of Self-Supervised Learning |
4 FINE-TUNING PERFORMANCE OF SPARSE MODELS
The best performing language models are usually obtained by (1) pre-training on large amounts of
data (e.g. the internet) followed by (2) fine-tuning on a task of interest (e.g. SuperGLUE). Promis-
ing new techniques have emerged as an alternative, including few-shot inference... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
### Task End ###
### Task Start ###
# These are the assertions for your function:
assert differ_At_One_Bit_Pos(15,8) == False
""" Write a python function to check whether the two numbers differ at one
bit position only or not. """
def differ_At_One_Bit_Pos(lhs,rhs):
if (lhs - rhs) == 0 or (lhs - rhs) == 1:
return T... | Teaching Large Language Models to Self-Debug |
xw0 Error Change.0.40.00.4xl0 Error Change.||†l−†θ(xlt,t)||22−||†l−†ref(xlt,t)||220.40.00.4 Figure 3. (Top) DPO-SDXL significantly outperforms SDXL in human evaluation. (L) PartiPrompts and (R) HPSv2 benchmark results
across three evaluation questions, majority vote of 5 labelers. (Bottom) Qualitative comparisons betwe... | DiffusionModelAlignmentUsing Direct Preference Optimization |
[77] Harish Ravichandar, Athanasios S Polydoros, Sonia Chernova, and Aude Billard. Recent
advances in robot learning from demonstration. Annual review of control, robotics, and
autonomous systems, 3:297–330, 2020.
[78] Jack Collins, Shelvin Chand, Anthony Vanderkop, and David Howard. A review of physics
simulators fo... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
You should look for a cheaper apartment. You can try to negotiate
with the landlord and offer him/her a lower price. If you are lucky,
they will accept your offer. Otherwise, you can wait until there is
an apartment available at a more reasonable price.
Let’s go to the beach today.
A: Hey, I have a great idea!
B: No ... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
“A sand sculpture of S∗”
“Colorful graffiti of S∗”
“A photograph of two
S∗ on a table”
A.2. Evaluation Setup
Baseline Methods. For Textual Inversion [9], we follow
the original paper and train for 5, 000 optimization steps us-
ing a batch size of 8 using the unofficial implementation
from the diffusers [40] library... | A Neural Space-Time Representation for Text-to-Image Personalization |
4 Agents in Practice: Harnessing AI for Good
Task-oriented
Deploytment §4.1.1
Web scenarios
Life scenarios
WebAgent [388], Mind2Web [389],
WebGum [390], WebArena [391],
Webshop [392], WebGPT [90], Kim
et al. [393], Zheng et al. [394], etc.
InterAct [395], PET [182], Huang
et al. [258], Gramopadhye et al.
[396], R... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
xent
-
-
-
1.720
1.834
1.613
1.626
1.704
1.582
1.572
1.519
1.724
1.644
1.601
Downstream task accuracy (↑)
Hella-
Swag
0.458
0.451
0.386
0.518
0.505
0.482
0.447
0.524
0.505
0.513
0.535
0.466
0.488
0.516
PIQA Wino-
Grande
0.738
0.610
0.612
0.737
0.559
0.701
0.640
0.752
0.654
0.763
0.746
0.611
0.602
0.739
0.651
0... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
I’m going to say: 40%. I expect creating a fully PS-aligned APS system to be very difficult, relative
to creating a less-than-fully PS-aligned one with very useful capabilities—especially in a paradigm
akin to current machine learning, in which one searches over systems that perform well according to
some measurable beh... | Is Power-Seeking AI an Existential Risk? |
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