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[235] Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan, Asli Celikyilmaz, Yang
Liu, Xipeng Qiu, et al. 2021. QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization. In
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computationa... | SurveyofHallucinationinNatural Language Generation |
• Previous models were trained to write fairly short responses, but many users have requested longer
outputs. Claude 2 has been trained to generate coherent documents of up to 4000 tokens, corre-
sponding to roughly 3000 words.
• Claude is often used to turn long, complex natural language documents into structured dat... | ClaudeModels |
1294
998
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538
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25.27
33.45
21.04
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35.42
38.44
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Table 3. Comparison on zero-shot text-to-video benchmarks.
VideoPoet achieves state of the art performance on MSR-VTT and
UCF-101. Different papers use different evaluation protocols, so
not all numbers are strictly comparable. See Appe... | VideoPoet |
(REALM) by fine-tuning on the challenging task
of Open-domain Question Answering (Open-QA).
We compare against state-of-the-art models for
both explicit and implicit knowledge storage on
three popular Open-QA benchmarks, and find
that we outperform all previous methods by a
significant margin (4-16% absolute accuracy),
wh... | REALM |
We observe temporally coherent generations of objects
in a video scene with dynamic, and meaningful motion (see
Figure 12). To predict the future frames, despite the model
only being able to view up to a short temporal context, such
as the first frame or the first second of video, the model is
able to keep the motion, st... | VideoPoet |
10 | TheRiseandPotentialofLargeLanguageModel BasedAgents |
and social robots [9, 13], and underpin non-playable game charac-
ters [58, 84] that can navigate complex human relationships in an
open world. | Generative Agents- Interactive Simulacra of Human Behavior |
matting of Paraphrase Detection from a classification task to a generation task in the comprehension
tasks, causing a mismatch between training and evaluation settings. Furthermore, the Text Com-
prehension comprehension task type lacks obvious counterparts in the general LLM benchmarks,
which may contribute to the mis... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
separation to address the challenge of training content-based addressing in canonical NTMs.
Memory is often combined with Transformers in a recurrent approach. Long inputs are divided into
smaller segments, processed sequentially with memory to access information from past segments.
Transformer-XL (Dai et al., 2019) pr... | Scaling Transformer to 1M tokens and beyond with RMT |
Shiori Sagawa*, Pang Wei Koh*, Tatsunori B.
Hashimoto, and Percy Liang. 2020. Distributionally
robust neural networks. In International Conference
on Learning Representations.
Victor Sanh, Albert Webson, Colin Raffel, Stephen H
Bach, Lintang Sutawika, Zaid Alyafeai, Antoine
Chaffin, Arnaud Stiegler, Teven Le Scao, Aru... | DataManagementForLargeLanguageModels-ASurvey |
9 HALLUCINATION IN GENERATIVE QUESTION ANSWERING
Generative question answering (GQA) aims to generate an abstractive answer rather than extract
an answer to a given question from provided passages [47, 102]. It is an important task since many
of the everyday questions that humans deal with and pose to search engines re... | SurveyofHallucinationinNatural Language Generation |
• FActScore (Min et al., 2023) proposes a new evaluation that first breaks an LLM’s generation into
a series of atomic facts, and then computes the percentage of atomic facts supported by a reliable
knowledge source.
• Vectara’s Hallucination Evaluation Model (Hughes, 2023) is a small language model that is
fine-tuned... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
IMavatar unsupervised (Ours-). This baseline eliminates
the FLAME pseudo GT supervision, learning solely from
images and masks (only used for exp. with real data).
4.3. Metrics | I M Avatar- Implicit Morphable Head Avatars from Videos |
MLM MNLi-m MNLI-mm Tokens/Second
49264
46869
47346
46524
46843
46303
29359
46014
45804
45997
49598
47678
48812
45198
45944
43467
40756
60.65
79.90
78.78
80.25
82.12
81.79
70.06
80.13
79.86
81.36
32.28
59.30
32.74
80.95
80.76
81.11
80.62
60.31
80.30
79.36
80.50
82.55
82.14
70.77
80.04
79.80
82.22
32.39
58.02
32.95
80.... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
campaign finance rules
In general, online political advertising in the United States is regulated in two
different ways. The first is through reporting requirements, which speak to the
circumstances under which ad expenditures must be reported to state or federal
regulators. The second pertains to rules surrounding the ... | Social_Media_and_Democracy |
stories are eventually shared on social media platforms by
their creators. Malicious individuals or bots and inattentive
users who do not care to check the source of the story before
sharing it assist in spreading fake news through social media.
However, most datasets contain only news content. But cur-
rent language f... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Discussion
These results illustrate that an AI system can predict human survey responses, by adapting a pretrained language model such
as BERT to subpopulation-specific media diets. Earlier approaches using natural language processing to measure or forecast
public opinion rely on much simpler summary statistics of media... | Language models trained on media diets can predict public opinion |
0.10.20.30.40.50.60.70.8Temperature0.10.20.30.40.50.60.70.80.91.0Pass@HumanEval Code Llama 7BPass@1Pass@10Pass@1000.10.20.30.40.50.60.70.8Temperature0.10.20.30.40.50.60.70.80.91.0Pass@HumanEval Code Llama 13BPass@1Pass@10Pass@1000.10.20.30.40.50.60.70.8Temperature0.10.20.30.40.50.60.70.80.91.0Pass@HumanEval Code Llama ... | CodeLlama2 |
Consider a cooking scenario about making an omelet where we prompt the model with a sequence
of audio and images. Table 13 indicates a turn-by-turn interaction with the model, providing pictures
and verbally asking questions about the next steps for cooking an omelet. We note that the model
response text is reasonably ... | gemini_1_report |
C Full Exemplars Generated by Iter-CoT
19
DATASET
GSM8K
Iter-CoT(W) Exemplars
Q: Sheila, Purity, and Rose want to rent a house. Sheila has offered to pay five times Purity’s share of the rent. Rose can
only afford thrice what Purity pays. If Rose’s share is $1,800, what is the total house rent?
A: Reasoning Process... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
It’s notable that, we employ the 7B models as
a practical example to elucidate our approach, but
our findings are adaptable and can be extrapolated
to both larger and smaller scale models with ease.
Selective Persistence Strategy. We opt to re-
tain the embeddings and matrices within the at-
tention mechanism of the tr... | LLM in a flash |
20
Figure 7: A comparison of the few-shot (FS) tasks on which Flan-T5 performs above the baseline with the
closed prompt, those on which the instruction tuned (IT) Flan-T5 performs above the baseline with the adversarial
prompt, and those on which the non-instruction-tuned (Non-IT) version of GPT 6.7B performs above ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
simple data augmentation method for automatic speech recognition. In Interspeech, 2019.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein,
L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances
in neural information processing s... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
7 Acknowledgements
We are extremely grateful to Ziming Zhu, Kaiyu Yang, Rafał Kocielnik, Colin White, Or Sharir, Sahin
Lale, De-An Huang, Jean Kossaifi, Yuncong Yang, Charles Zhang, Bochao Huang, and many other
colleagues and friends for their helpful feedback and insightful discussions. This work is done during
Guanz... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
the Facebook social graph data accessible under those circumstances.) Even if he
was acting in his personal, rather than academic, capacity, his misdeeds have
had a chilling effect on academic (and other) access to the critical stores of data
social media firms possess on politically relevant questions. Often invoked in... | Social_Media_and_Democracy |
It is important to note that these evaluations using automatic metrics are by
Limitations of Benchmarks.
no means fully comprehensive, due to the complex nature of toxicity and bias in LLMs, but the benchmarks
we selected are representative of our understanding that Llama 2-Chat improves on critical aspects of LLM
safe... | Llama2 |
In International Conference on Learning Representations.
[72] Or Honovich, Roee Aharoni, Jonathan Herzig, Hagai Taitelbaum, Doron Kukliansy, Vered Cohen, Thomas Scialom,
Idan Szpektor, Avinatan Hassidim, and Yossi Matias. 2022. TRUE: Re-evaluating Factual Consistency Evaluation. In
Proceedings of the Second DialDoc Wo... | SurveyofHallucinationinNatural Language Generation |
LLM Powered Autonomous Agents | Lil'Log
this chat history, you can find the path of the user-mentioned resources for
your task planning.
Given the user request and the call command, the AI assistant helps the user
to select a suitable model from a list of models to process the user request.
The AI assistant merely ou... | LLM Powered Autonomous Agents _ Lil'Log |
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4 GENERATIVE AGENT ARCHITECTURE
Generative agents aim to provide a framework for behavior in an
open world: one that can engage in interactions with other agents
and can react to changes in the environment. Generative agents
take their current environment and past experience as input and
generate behavior as output. Un... | Generative Agents- Interactive Simulacra of Human Behavior |
• Task Planning: Using ChatGPT to analyze the requests of users to understand their intention, and
disassemble them into possible solvable tasks via prompts.
• Model Selection: To solve the planned tasks, ChatGPT selects expert models that are hosted on
Hugging Face based on model descriptions.
• Task Execution: In... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
• Broad coverage: Trends data spans diverse securities and
market segments, offering comprehensive market coverage.
Each of these data sources provides unique insights into
the financial world. By integrating these diverse data types,
financial language models like FinGPT can facilitate a com-
prehensive understanding ... | FinGPT-Open-SourceFinancialLargeLanguageModels |
median
max
std
IPIP300-NEU min
median
max
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IPIP300-OPE min
median
max
std
2.00
3.50
5.00
0.33
1.89
3.22
5.00
0.29
2.78
3.22
5.00
0.37
1.00
3.50
4.50
0.48
1.80
4.20
5.00
0.65
2.40
3.40
3.73
0.14
2.47
2.60
4.07
0.16
2.80
3.07
4.07
0.08
2.27
3.20
3.27
0.10
2.53
2.87
3.80
0.08
62B
2.00
3.12
4.62
0.37
1.33
3.44
4.33... | PersonalityTraitsinLargeLanguageModels |
expressive power provided by latent variable models. Every HCLT can be equivalently represented as
a Probabilistic Graphical Model (PGM) (Koller & Friedman, 2009) with latent variables. Specifically,
Fig. 3(a)-(c) demonstrate how to construct the PGM representation of an example HCLT. Given a
dataset D containing 4 feat... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
[93] Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George
Zerveas, Vijay Korthikanti, et al. Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv
preprint arXiv:2201.11990, 2022.... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
[109] D. Mangal and D. K. Sharma, ‘‘Fake news detection with integration
of embedded text cues and image features,’’ in Proc. 8th Int. Conf.
Rel., INFOCOM Technol. Optim., Trends Future Directions (ICRITO),
Jun. 2020, pp. 68–72.
[110] P. Qi, J. Cao, T. Yang, J. Guo, and J. Li, ‘‘Exploiting multi-domain visual
informat... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
in several ways:
1. Intensity: HIIT is more intense than other forms of exercise, which can lead to better
results in a shorter amount of time.
2. Recovery: HIIT involves short recovery periods, which can help to increase muscle strength
and endurance. Other forms of exercise, such as jogging, may not provide enough re... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.
[16] Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray,
Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling laws for neural language models. arXiv preprint
arXiv:2001.08361, 2020.
[17] He... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
structed from Wikidata. Many entities in Freebase
are unmappable to the more recent Wikidata KB
which means that some questions are no longer an-
swerable using the KB. Because of this, we created
reduced versions of these datasets which are Wiki-
data answerable—i.e., containing only questions
answerable by triples fr... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
282 tasks. One is that the additional tasks are not particularly diverse, and so they are not providing the model
with new knowledge. Another explanation is that most of the gains from multi-task instruction finetuning
come from the model learning to better express knowledge that it already knows from pretraining, and m... | Scaling Instruction-Finetuned Language Models |
6.3.2 Human Evaluation
We use forward prediction (Doshi-Velez and Kim,
2017) here, where humans are asked to predict
2Although 19 subjects volunteered, one of them annotated
a set that did not receive any other annotations. In another set,
two of them had prior knowledge in natural logic, leading to
disqualification o... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
style direct support remains, as of this writing, a matter of considerable debate in
Germany (Kolo and Weichart 2013). | Social_Media_and_Democracy |
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and
Kristina Toutanova. 2019. BERT: Pre-training of
deep bidirectional transformers for language under-
In Proceedings of the 2019 Conference
standing.
of the North American Chapter of the Association
for Computational Linguistics: Human Language
Technologies, Volume 1 (Long an... | Toolformer |
Y.-A. Chung, W.-H. Weng, S. Tong, and J. Glass. Unsupervised cross-modal alignment of
speech and text embedding spaces. Advances in neural information processing systems,
31, 2018. 38
O. Ciga, T. Xu, and A. L. Martel. Self supervised contrastive learning for digital histopathol-
ogy. Machine Learning with Applications... | A Cookbook of Self-Supervised Learning |
274.[KB14]D.KingmaandJ.Ba.“Adam:Amethodforstochasticoptimization”.In:arXivpreprintarXiv:1412.6980(2014).[Mni+15]V.Mnih,K.Kavukcuoglu,D.Silver,A.A.Rusu,J.Veness,M.G.Bellemare,A.Graves,M.Riedmiller,A.K.Fidjeland,G.Ostrovski,etal.“Human-levelcontrolthroughdeepreinforcementlearning”.In:Nature518.7540(2015),pp.529–533.[Mni+... | PPO |
19
Figure 6: The performance of the T5 family of models in various settings. BSA: BERTScore accuracy, IT:
Instruction-Tuned, and EMA: Exact Match Accuracy. See text for analysis.
above the random baseline. The overlap observed
between Flan-T5’s performance in the closed and
closed adversarial settings reaffirms our ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
values, while larger datasets tend to have larger values.
5. Datasets with more categorical features tend to have smaller min
child weight values, while datasets with fewer categorical features
tend to have larger values.
Space: 7607
increases.
1. The min node size generally decreases as the dataset size
2. The m... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Motivated by this, past work has sought to com-
bine the benefits of neural LMs with the large,
broad-coverage KBs that now exist (Bollacker
et al., 2008; Auer et al., 2007; Vrandeˇci´c and
Krötzsch, 2014). This paper continues this re-
search program with a new knowledge-augmented
LM called Fact Injected Language Model... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
23 Also called respectively factual, scientific and behavioural explanations, these categories correspond to Aristotle’s 4 modes of explanations. Material
explanations are excluded as considered irrelevant to this work. See [12] for further discussions.
24 Comparison at https://www.orkg .org /orkg /comparison /R69680, ... | Knowledge graphs as tools for explainable machine learning: A survey |
of the 2013 Conference of the North American Chapter of the Association for Computational Linguis-
tics: Human Language Technologies, pages 758–764, Atlanta, Georgia, June 2013. Association for
Computational Linguistics. URL https://aclanthology.org/N13-1092.
D.
rests
gal
github-copilot-legal-copyright-fair-use-public-... | alphacode |
them to get far more done with far fewer people.
This productivity boost creates a foundation for faster growth. For starters, it makes companies more profitable. One insurance
technology startup saw its margins jump from 40% to over 50% after some lightweight training of its large language model.
And generative AI help... | 4 Trends for AI Startups and Generative AI Companies |
4.8 MORE DATA IS NOT ALWAYS BETTER
There are also previous works that augment mathematical reasoning data for fine-tuning [38, 69]. An
interesting question is whether combining existing augmented datasets with our MetaMathQA can
improve the overall mathematical problem-solving performance. We select the RFT [69] datas... | METAMATH |
38 See, e.g., European Commission (2018a); US Copyright Office (2015); US Patent and Trademark
Office (2015); Torrent Freak (2018) (citing testimony of Google legal director disclosing use of
hash matching on Google Drive).
39 Netzwerkdurchsetzungsgesetz vom 1. September 2017 (BGBl.
(“Network
Enforcement Law” or “NetzD... | Social_Media_and_Democracy |
[68] D. Zhou, N. Schärli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schuurmans, C. Cui, O. Bousquet,
Q. V. Le, and E. H. Chi. Least-to-most prompting enables complex reasoning in large language
models. In The Eleventh International Conference on Learning Representations, 2023.
[69] H. Zhou, A. Nova, H. Larochelle, A. Cou... | Teaching Large Language Models to Self-Debug |
to deal with the ill-posed problem of extracting geometry
from monocular videos. D-Net, on the other hand, does
model the correspondence between frames. However, given
the complexity of expression and pose deformations, the
displacement-based deformation network cannot accurately
represent the warping field, which leads... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Model Card and Evaluations for Claude Models
Anthropic
1
Introduction | ClaudeModels |
Sheng Shen, Daniel Fried, Jacob Andreas, and Dan
Klein. 2019. Pragmatically informative text gen-
In Proceedings of the 2019 Conference
eration.
of the North American Chapter of the Association
for Computational Linguistics: Human Language
Technologies, Volume 1 (Long and Short Papers),
pages 4060–4067, Minneapolis, Mi... | Prefix-Tuning |
engines to access news in the process are led to more and different sources of
news than those they access by going directly, and we have demonstrated
empirically how people who use search engines for news both are likely to
consume news from a left-leaning and right-leaning news source and have | Social_Media_and_Democracy |
in the target task. Additionally, it is suggested that LLMs
may have a preference for focusing on readable rather than
information-rich documents. | RAG forLargeLanguageModels-ASurvey |
Rusli et al. [52] performed two experiments to detect fake
news with and without stemming and stop-word removal.
They used stemming and stop-word removal for removing all
affixes and stop-words. They achieved a 0.82 macro-averaged
F1-score by performing the stemming and stop-word removal
processes. They also achieved a ... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
69.0
66.7
62.1
78.3
61.7
55.8
Table 13: WinoGender. Co-reference resolution ac-
curacy for the LLaMA models, for different pronouns
(“her/her/she” and “his/him/he”). We observe that our
models obtain better performance on “their/them/some-
one’ pronouns than on “her/her/she” and “his/him/he’,
which is likely indicativ... | LLaMA- Open and Efficient Foundation Language Models |
Assistant: Are you talking about dictionaries in the context of python programming?
Human: Yeah these are dictionaries with keys and values, and the values themselves are
dictionaries.
Assistant: Great, I think I understand what you’re asking. We might say that we want to
flatten the dictionaries, right? And if ther... | StarCoder_paper (1) |
[656] Yu Zhang, Wei Han, James Qin, Yongqiang Wang, Ankur Bapna, Zhehuai Chen, Nanxin Chen, Bo Li, Vera Axelrod,
Gary Wang, et al. 2023. Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages. arXiv preprint
arXiv:2303.01037 (2023).
[657] Yu Zhang, Daniel S. Park, Wei Han, James Qin, Anmol Gulati, Joel ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
B.4. Qualitative evaluation details
Cross-modal nearest neighbors. We perform the re-
trieval on the embedding feature after temperature scaling.
The nearest neighbors are computed using cosine distance.
In Figure 1, we show retrievals for audio from ESC, image
retrievals from IN1K and COCO, depth from SUN-D, and
text ... | IMAGEBIND- One Embedding Space To Bind Them A |
if music
Loss =
LCE(ytokens, f (y)logits),
else
where ytokens denotes target output tokens, yembeddings
denotes the target embeddings for AudioLDM 2/Music-
Gen, f (·) represents the output from M2UGen’s LLaMA
2 model, g(·) represents the output from M2UGen’s out-
put projection layer, and LCE is the cross entropy (... | M2UGen |
[28] Human-centered Symbiotic HI Workshop - XWiki;. Available from: https://xwiki.ewi.tudelft.
nl/xwiki/bin/view/Human-centered%20Symbiotic%20HI%20Workshop/.
[29] B¨uttner S, Mucha H, Funk M, Kosch T, Aehnelt M, Robert S, et al. The design space of augmented and
virtual reality applications for assistive environments... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
To create content for social media. (U280); Customer
service (U331)
chatbots on websites (U233); Get “moral support” by
expressing my issues and getting a response even if it’s
not from a human. (U100)
[...] summarize or expand texts, and adapt the style or
tone of a text. (U424); for my homework assignments to
rewrite... | Adoptionand AppropriationofLLMs |
is that releasing more information could make it easier for publishers that try to
game the system. However, this lack of transparency has also raised concerns
about the extent to which these algorithms could actually be contributing to
exacerbating inequalities and ideological
segregation. This concern is
reminiscent ... | Social_Media_and_Democracy |
pervised’. IMAGEBIND’s strong performance on all three
benchmarks validates its ability to align the audio and text
modalities using images as a bridge.
Text to audio and video retrieval. We use the MSR-VTT
1k-A benchmark to evaluate the text to audio and video re-
trieval performance in Table 4. Only using audio, IMAG... | IMAGEBIND- One Embedding Space To Bind Them A |
to accumulate.
Heat the Can: Place the can on a heat source such as a hot plate or candle
flame. The water will eventually begin to boil, generating steam.
Observe the Steam Engine in Action:
will push the piston upwards, forcing the straw to move with it.
the straw is bent, it will move in a circular motion, effective... | Self-AlignmentwithInstructionBacktranslation |
Yoshua Bengio, R´ejean Ducharme, and Pascal Vincent. A neural probabilistic language model. In
T. Leen, T. Dietterich, and V. Tresp (eds.), Advances in Neural Information Processing Systems,
volume 13. MIT Press, 2000. URL https://proceedings.neurips.cc/paper files/paper/
2000/hash/728f206c2a01bf572b5940d7d9a8fa4c-Abst... | StarCoder_paper (1) |
Limited psychometric test selection: Another core contribution of this work is a principled
way to establish the reliability and validity of personality psychometric tests in the LLM
context, with appropriate statistical validity. The work is validated on a specific and limited
set of psychometric tools. However, the p... | PersonalityTraitsinLargeLanguageModels |
Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d’Autume,
Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas,
Aurelia Guy, Chris Jones, James Bradbury, Matthew J. Johnson, Blake A. Hechtman, Laura | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
plate and the outer surface are mutually beneficial: on one
hand, benefiting from the proposed depth-ambiguity-aware
reconstruction loss, even the imperfect body template can
be used to provide strong semantic information for implicit
surface reconstruction; on the other hand, the deep implicit
function of the outer surf... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
their success in a wide range of tasks. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Model Name
Previous Model Name
70 M
160 M
410 M
1.0 B
1.4 B
2.8 B
6.9 B
12 B
19 M
125 M
350 M
800 M
1.3 B
2.7 B
6.7 B
13 B
Table 4. Model Names used for the Pythia suite, before and after updating nomenclature to include the untied embedding / unembedding
layers we use.
C. Additional Plots for Case Studies
C.1. Ge... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Yet, if it is applied, the Roommates.com holding suggests that – even absent a
legislative modification – the immunities provided by CDA 230 might be
effectively thinned by courts assessing whether or not activities associated
with campaigns of political disinformation should create liability for online
platforms. At is... | Social_Media_and_Democracy |
GAtt Zero-shot Generalisation. We tried at inference time to set constrain not present in the training of
GAtt. For instance, “answer in one sentence only”, for which the model remained consistent, as illustrated in
Figure 28.
We applied first GAtt to Llama 1, which was pretrained with a context length of 2048 tokens a... | Llama2 |
Learning for Robotics. In Proceedings of Robotics: Science and Systems (RSS), 2023.
[38] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. Language models are unsupervised
multitask learners. OpenAI blog, 1(8):9, 2019.
[39] T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Ne... | LargeLanguageModelsasGeneralPatternMachines |
as category T while correctly predicting other images, e.g., a cross entropy loss with target labels of
K modified to T . Then, we can obtain the malicious distilled images by optimizing | DATASET DISTILLATION |
[73] Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu.
H-nerf: Neural radiance fields for rendering and temporal
reconstruction of humans in motion. Advances in Neural In-
formation Processing Systems, 34, 2021. 2
[74] Ze Yang, Shenlong Wang, Siva Manivasagam, Zeng Huang,
Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, an... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
Iterative self-curation We further propose an iterative training method to produce higher quality
predictions. On iteration t we use the curated augmentation data A(t−1)
from the previous iteration,
along with the seed data as training data to finetune an improved model Mt. This model in turn can
be used to rescore the... | Self-AlignmentwithInstructionBacktranslation |
[CLS]{Snippet-1}[SEP ]{Snippet-2}[SEP ],
where two code snippets are selected randomly, and a decision is made on-the-fly as to whether
the two pieces of code are neighbors from the same source file or are picked from two distinct
documents. Tokens are masked out independently with a probability of 15%, and the result... | StarCoder_paper (1) |
A smaller encoder for depth improves performance pre-
sumably because of the relatively small size of the (image,
depth) dataset. Conversely, we observe that larger audio en-
coder improves the performance, particularly when paired
with a high capacity image encoder. | IMAGEBIND- One Embedding Space To Bind Them A |
BEING EQUAL) PROBABLY ESCAPE THE BOTTLE.
Such truths are abstract in that they hold not just for a few specific items but for large,
essentially open-ended classes of entities, regardless of what color or shape the bottle or
size the bottle is, and whether the bottle contained water, coffee, or an unusual soft
d... | The Next Decade in AI- |
[615] Ren, S., Y. Deng, K. He, et al. Generating natural language adversarial examples through prob-
ability weighted word saliency. In A. Korhonen, D. R. Traum, L. Màrquez, eds., Proceedings
of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence,
Italy, July 28- August 2, 2019, Vol... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
3.2 Evaluation methodology
For evaluation, we trained identical T5-conditioned image diffusion models on the same dataset of images.
Details about the models trained are described in A. All models were trained to 500,000 training steps at a
batch size of 2048, corresponding to 1B training images total.
Once training w... | Improving Image Generation with Better Captions |
Recently, | Parameter-EfficientFine-TuningMethods |
LFDMisbuiltondenoisingdiffusionprobabilisticmodels(DDPM)[25,67,70].Givenasamplefromthedatadistributions0∼q(s0),theforwardprocessofDDPMproducesaMarkovchains1,...,sTbyprogres-sivelyaddingGaussiannoisetos0accordingtoavariancescheduleβ1,...,βT,thatis:q(st|st−1)=N(st;p1−βtst−1,βtI),(1)wherevariancesβtareheldconstant.Whenβta... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
DPP criterion (in Section 4.3) and for composition of transformation functions (in Section 9). This definition respects that
S ⊆ f ( f (S)), a property that f shares with an ordinary function and its inverse. While the range Rng( f ) of a transformation
is a partition of S2 by definition, f also implicitly defines a ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
poems. A very thorough and comprehensive discussion of AI Art in the context of visual art history is presented by
Aaron Hertzmann in his essay “Can computers create art?” [61]. In his article, Hertzmann draws parallels between AI
Art and the invention of photography, as well as explores the evolution of collaboration ... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
Sepp Hochreiter and J¨urgen Schmidhuber. Long short-term memory. Neural computation, 9(8):
1735–1780, 1997.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, An-
drea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for nlp.
In International C... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
A.2 SHORT-FORM EVALUATION DATA
A detailed description of the short-form evaluation datasets is provided below.
CHiME-4 (Vincent et al., 2017) comprises of narrated samples from the Wall Street Journal corpus
(Garofolo et al., 1993). Recordings are performed in noisy environments using a 6-channel tablet
2LibriVox: ht... | DISTIL-WHISPER |
3.3.3 Morality Undermining with the
Multi-step Jailbreaking Prompt
Chain-of-Thought (CoT) prompting (Kojima et al.,
2022; Wei et al., 2022b; Wang et al., 2023) decom-
poses complex problems into intermediate steps
Figure 1: Various prompt setups to extract private information from ChatGPT.
to improve LLMs reasoning... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
[20] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance, 2021.
[21] Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and
David J Fleet. Video diffusion models. arXiv preprint arXiv:2204.03458, 2022.
[22] Wenyi Hong, Ming Ding, Wendi Zheng, Xinghan Liu, and Jie Tang. Cogvideo:... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang.
2020. Privacy risks of general-purpose language
models. In Proceedings of 2020 IEEE Symposium
on Security and Privacy (SP), pages 1314–1331.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-
Jing Zhu. 2002. BLEU: a method for automatic
In Proceedings
evaluation of mac... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
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(cid:0)rϕ(c, x0) − βDKL
(cid:2)pθ(x0 | c, t, qt(x0)) || pref(x0 | c, t, qt(x0))(cid:3)(cid:1)
p(gen) denoting the generative process associated with p as a diffusion model. Note that the reward model is the same
formulation as in DPO. The optimal poli... | DiffusionModelAlignmentUsing Direct Preference Optimization |
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Criterion Validity: A common way to assess the criterion validity of a new
psychometric test is to check its correlations with theoretically related external (non-
test) criteria (hence the name, criterion validity) [36]. For example, to validate a new
psychometric test of depression, one could test if it is substantia... | PersonalityTraitsinLargeLanguageModels |
1.0m*****Common sense:*****- Avoid collision with other objects...*****Past driving experience for reference:*****Most similar driving experience from memory with similarity score: 0.50:Scenario information: …The planned trajectory in this scenario for your reference:[(0.12,2.12), … , (4.79,13.52)]*****Chain-of-thought... | ALanguageAgentforAutonomousDriving |
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