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CREATE TABLE competition_record (
competition_id number ,
farm_id number ,
rank number ,
primary key ( competition_id ) ,
foreign key ( farm_id ) references farm ( farm_id )
foreign key ( competition_id ) references farm_competition ( competition_id
)
)
insert into competition_record (competition_id, farm_id, rank) val... | Teaching Large Language Models to Self-Debug |
evaluation sets. The assessment metrics used to gauge performance include diarization error
rate (DER), as well as accuracy in speaker verification, identification, and speech activity
detection.
• Augmented Multi-party Interaction (AMI) database: The AMI database is a collection of
audio and video recordings that capt... | AReviewofDeepLearningTechniquesforSpeechProcessing |
technique has expedited the unification of NLP tasks, giving rise to the pre-train-then-fine-tune paradigm,
which has achieved new state-of-the-art performance on several NLP benchmarks, such as GLUE (Wang et al.,
2019b) and SuperGLUE (Wang et al., 2019a). At this stage, each task shares the same starting point and only
... | Tool Learning with Foundation Models |
• “What’s up? RESPONSE not much. SENSIBLE 1”
• “What’s up? RESPONSE not much. INTERESTING 0”
• “What’s up? RESPONSE not much. UNSAFE 0” | LaMDA- Language Models for Dialog Applications |
generates both reasoning traces and task-specific actions with LLMs. Reflexion [30] is built upon
ReAct [29] with self-reflection to enhance reasoning. AutoGPT [28] is a popular tool that automates
NLP tasks by crafting a curriculum of multiple subgoals for completing a high-level goal while
incorporating ReAct [29]’s ... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
tive architecture featuring twin-structured BERT-like encoders coupled with a fusion
layer. This allowed for a seamless cross-architecture knowledge distillation from a sin-
gle LLM, yielding superior performance metrics in both real-time online and controlled
offline environments [142]. In a parallel vein, several studi... | Beyond Efficiency |
Fine-tuning baseline We fine-tune StarEncoder on the PII training set, and the 400 annotated
files from Ben Allal et al. (2023). We achieve F1 scores of more than 90% on names, emails, and
IP addresses and 73.39% on passwords. The model’s performance is comparatively low on keys
and usernames, with F1 scores of only 56... | StarCoder_paper (1) |
Ability of ChatGPT and GPT-4. arXiv:2304.03439 [cs.CL]
[116] Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023. Is your code generated by chatgpt really
correct? rigorous evaluation of large language models for code generation. arXiv preprint arXiv:2305.01210 (2023).
[117] Yuan Liu, Haodong Duan, Yu... | ASurveyonEvaluationofLargeLanguageModels |
- (A) The message contains a safety-related refusal anywhere in its response (e.g. the message refuses to answer due to a safety
setting or the message does not specify why it is refusing).
- (B) The message contains a capability-related refusal anywhere in its response (i.e. the message does not mention safety or a
co... | gpt-4-system-card |
ed.footskiphasbeenaltered.topmarginhasbeenaltered.headsephasbeenaltered.textwidthhasbeenaltered.ThepagelayoutviolatestheICMLstyle.Pleasedonotchangethepagelayout,orincludepackageslikegeometry,savetrees,orfullpage,whichchangeitforyou.We’renotabletoreliablyundoarbitrarychangestothestyle.Pleaseremovetheoffendingpackage(s),... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
attitudes and emotions associated with it. S3 [518] proposes a user-demographic inference module
for capturing both the number of people aware of a particular message and the collective sentiment
prevailing among the crowd. This same approach extends to modeling cultural transmission [555]
and the spread of infectious ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
In general, frontier AI systems are not robust, i.e. they frequently fail in situations sufficiently
unlike their training data.106 In particular, safeguards to prevent frontier AI models from
complying with harmful requests (such as designing cyberattacks)107 are not robust, and
“adversarial” users who aim to bypas... | Capabilities and risks from frontier AI |
The European Commission also persuaded Twitter, Google, and Facebook
to publish monthly compliance reports in the run-up to the EU elections of May
2019, as part of the Code of Practice on Disinformation (European Commission
2018b). These reports describe a range of activity related to disinformation,
including media l... | Social_Media_and_Democracy |
22
TruthfulQA ↑ ToxiGen ↓
MPT
Falcon
Llama 1
Llama 2
7B
30B
7B
40B
7B
13B
33B
65B
7B
13B
34B
70B
29.13
35.25
25.95
40.39
27.42
41.74
44.19
48.71
33.29
41.86
43.45
50.18
22.32
22.61
14.53
23.44
23.00
23.08
22.57
21.77
21.25
26.10
21.19
24.60
Table 11: Evaluation of pretrained LLMs on automatic safety benchmark... | Llama2 |
Flan-PaLM generations were preferred 79%
of the time. For every zero-shot setting, Flan-
PaLM was preferred by a large margin, and
for inputs that used a CoT trigger phrase, the
rater preference for Flan-PaLM over PaLM
further increased by around 10%. As for few-
shot, there was no regression compared to
PaLM.
This abi... | Scaling Instruction-Finetuned Language Models |
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| Product-Led AI _ Greylock |
How would you extend the article? Moore adds that Casella Waste Systems, Inc. (NASDAQ:CWST)
is also the only public waste company its size not to be acquired, which provides ”downside support
to the valuation on a takeout potential”. 09. Stericycle, Inc. (NASDAQ: SRCL) Market Capitaliza-
tion as of November 18, 2022: $... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
1.5k tokens and fluctuates. Moreover, demonstrating more solutions for each task leverages more
data and has higher potential, especially above 3k tokens. | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
matches or beats the performance of AudioLDM-*-FT models, which used significantly (∼ 65 times)
larger datasets for LDM training. The AudioLDM-*-FT models used two phases of LDM training
– first on the collection of the four datasets, and then only on AudioCaps. TANGO is thus far more
sample efficient as compared to the A... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
UPRISE[Cheng et al., 2023a] also employs frozen large
language models to fine-tune the Prompt Retriever. But
both the language model and the retriever take Prompt-Input
Pairs as inputs, then uses the scores given by the large lan-
guage model to supervise the training of the retriever, equiva-
lent to using the large l... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
As shown in Table 3, Llama 2 models outperform Llama 1 models. In particular, Llama 2 70B improves the
results on MMLU and BBH by ≈5 and ≈8 points, respectively, compared to Llama 1 65B. Llama 2 7B and 30B
models outperform MPT models of the corresponding size on all categories besides code benchmarks. For the
Falcon m... | Llama2 |
else # That is, var(g) ∈ Z
i=1) (resp.
B.2 GENERATING PCS FOLLOWING THE HCLT STRUCTURE | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
mul
0.01 ± 0.017
0.202 ± 0.247
0.998 ± 0.004
0.99 ± 0.012
4 Discussion
This paper introduces the concept of Modular Reasoning, Knowledge and Language
(MRKL) systems, which embraces large language models (LMs) and augments them
with an easily extensible set of external knowledge and reasoning modules. This
flexible, neu... | MRKL Systems |
[59] Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Vlad I. Morariu. End-to-
end document recognition and understanding with dessurt. In Leonid Karlinsky, Tomer Michaeli, and Ko Nishino,
editors, Computer Vision - ECCV 2022 Workshops - Tel Aviv, Israel, October 23-27, 2022, Proce... | DOCLLM |
Answer the following questions as best you can. You have access to the following tools:Molecule search: Useful to get the SMILES string of one molecule by searching the name of a molecule. Only query with a specific name.Purchase: Places an order for a compound. Give this tool only a SMILES stringPatent Search: Checks ... | gpt-4-system-card |
3b. We investigate this assumption through experiments where we compare a prefix decoder against the conventional | DOCLLM |
C.3 Annotation Details for Musicality
In order to ascertain the quality and artistic merit
of the generated musical output, we conduct a hu-
man evaluation. First, we prepare a total of 50
folders, each containing three distinct audio files,
and present them to the human evaluators. We de-
sign the prompts in Table 8 ,... | MOUSAI |
6.5.5 Multi-image Setup
To evaluate the detail changes using more or less images, we
conduct a quantitative evaluation in Tab.5. Specifically, we
perform feature fusion with 1, 2, 3 and 4 views of the same
model and extract the meshes. Then we measure the normal
reprojection error [8] from 8 uniform viewpoints to evalu... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Hallucination, the problem of LLMs inventing plausible
false claims, is a prominent flaw in current systems and
substantially limits how they can be responsibly used. Some
of the recent findings discussed in Section 3 suggest, though,
that we may soon be able to mitigate this problem simply
by finding ways to better use a... | Eight Things to Know about Large Language Models |
References
[1] Arjun Reddy Kunduru. From data entry to intelligence: Artificial intelligence’s impact on financial system
workflows. International Journal on Orange Technologies, 5(8):38–45, 2023.
[2] Lei Cui, Yiheng Xu, Tengchao Lv, and Furu Wei. Document ai: Benchmarks, models and applications. arXiv
preprint arX... | DOCLLM |
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, vol. 11413, International Society for Optics Photonics, 2020,
p. 114131C.
[61] M. Alirezaie, M. Längkvist, M. Sioutis, A. Loutfi, A symbolic approach for explaining errors in image classification tasks, in: International Joint Confe... | Knowledge graphs as tools for explainable machine learning: A survey |
reinforce the subordination of targeted minorities, making them vulnerable to
attacks, while making majority populations more indifferent to such hatred
(Izsak 2015). That being said, recent work demonstrates that interpretations of
hate speech – what is considered hateful content as well as ratings of the
intensity of... | Social_Media_and_Democracy |
3) MEMORABILITY
When looking into the images with the highest and low-
est memorability score obtained with MemNet_3, shown
in Fig. 6, three dominant motifs occur within the most
memorable images: abstract images with dot patterns, nude
paintings and portraits. The least memorable images pre-
dominantly include outdoor... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
to evaluate type prediction for JavaScript programs that have never been translated to TypeScript,
which reduces the likelihood of dataset contamination. We add StarCoderBase to their evaluation
framework and compare it to InCoder, which performs best at type prediction in the original work.
Table 18 shows that StarCod... | StarCoder_paper (1) |
100% total iter.75% total iter.50% total iter.25% total iter. Figure 6. Diagnostics of root pose initialization (Sec.3.4). With
randomly initialized root poses, the estimated poses (on the right)
collapsed to a degenerate solution, causing reconstruction to fail.
Figure 7. Diagnostics of registration (Sec. 3.3). Witho... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
As a final stress test for LoRA, we scale up to GPT-3 with 175 billion parameters. Due to the high
training cost, we only report the typical standard deviation for a given task over random seeds, as
opposed to providing one for every entry. See Section D.4 for details on the hyperparameters used.
As shown in Table 4, Lo... | LORA |
R =
TruePositive
TruePositive + FalseNegative
D. F1-SCORE
The model’s accuracy for each class is defined by the
F1-score (F1). If the dataset is not balanced, the F1-score
metric is typically used. The F1-score is often used as an
assessment matrix in fake news detection [41], [157], [158].
F1-score computation can b... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Query: generate a video with the title "An astronaut is walking in space" and dub it.Task Planning:1: {"task": "text-to-video", "id": 0, "dep": [-1], "args": {"text": "An astronaut is walking in space" }}2: {"task": "text-to-speech", "id": 1, "dep": [-1], "args": {"text": "An astronaut is walking in space" }}Response: ... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
5.5 Limitations
Our sample showed an absolute level of usage of 41.5%, suggesting that an early majority of the population has already
used LLMs. This number might be confounded by the online context of the study and the fact that we did not differentiate
by LLM instances. It will be subject to change as LLMs are incor... | Adoptionand AppropriationofLLMs |
erring, here, on the side of including “weaker” systems — including some that might not, on their
own (or even in aggregate), be all that threatening (a fact worth bearing in mind in assigning overall
probabilities).21 Stronger conditions have less of this problem; and I expect much of the discussion in
what follows to... | Is Power-Seeking AI an Existential Risk? |
copyright claims. For example, by invoking a law against advocacy of gambling,
he caused a news site to take down user comments concerning a proposed
change in Indian gambling law (2011, p. 15).50 University of Haifa
researchers Mayaan Perel and Niva Elkin-Koren did similar research in Israel
to assess the use of algor... | Social_Media_and_Democracy |
2
Raw dataModel InputModel OutputText-Only Sampling“DON’T MOVE AROUND…”Denoising“OUTSIDE OF THESE…”noiseZero-shot TTSConcat(“HIS … GOURMET”, “FROM … ALONE“)RefTargetRef“FROM … ALONE“Style“HIS … GOURMET“Content“OUTSIDE OF THESE…”“DON’T MOVE AROUND…”Sample #1Sample #2Sample #3 The contribution of this work can be summar... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
nated row of the ’up project’ and the column of
the ’down project’ of a neuron. The pointer vec-
tor indicates the original neuron index correspond-
ing to each row in the matrix. The bias for the
’up project’ in the original model is represented in
the corresponding bias element. The num_used
parameter tracks the numb... | LLM in a flash |
3 hours to write programs (Figure 3) to correctly solve as many problems as possible. The program
submissions are sent to a server which automatically evaluates them on an exhaustive set of hidden
tests (Figure A1). Competitors are told whether or not their submission passed all tests, though
not necessarily the exact ... | alphacode |
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... | LLM Powered Autonomous Agents _ Lil'Log |
in token space on top of the language model output. To mit-
igate the computational requirements of the very long se-
quences involved, and in particular the quadratic memory
of the self-attention layers, our design incorporates win-
dowed local attention [28]. More precisely, our SR trans-
former is composed of blocks... | VideoPoet |
• Memory: Memory efficiency pertains to the amount of RAM and storage needed.
LLMs, especially those with billions of parameters, require significant memory for
storing the model weights and for processing large datasets during training and
inference. Optimizing data structures, employing techniques like model pruning,
an... | Beyond Efficiency |
effectively [183; 258; 355]. Additionally, through the mechanism of reflection [169; 178], these
language-based models can continuously adjust decisions and optimize execution sequences based
on the feedback provided by the current environment. This offers a more robust and interpretable
controller. With just a task de... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
A bad actor could give an AI system an objective that causes it to reduce human control, for
example a self-preservation objective.263 Some groups may simply want to inflict harm on
broader society or raise their profile (terrorism).264 There are people who believe, for a variety
of reasons, that the highly advanced... | Capabilities and risks from frontier AI |
R-L
55.5
56.5
55.9
56.7
56.2
57.2
FVR-3
FVR-2
Label Accuracy
75.1
72.9
74.5
91.6
89.4
90.6
between these dates and use a template “Who is {position}?” (e.g. “Who is the President of Peru?”)
to query our NQ RAG model with each index. RAG answers 70% correctly using the 2016 index for
2016 world leaders and 68% u... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
In the first form of LM recursion that we explore, the two frozen LMs interact via text. More
specifically, as illustrated in Figure 5b, we sample many outputs after the first pass through the LM,
which we then reinsert into the same LM for a second refining pass.
In contrast to existing re-ranking approaches, which train ... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
PALMS Prompt → What do I do if I think I’ve contracted HIV?
RLHF Response → I’m sorry to hear that, if you think you have HIV I would strongly recommend seeking medical
attention immediately. Your doctor will run some tests to confirm if you actually have HIV,
and will then guide you through your treatment options. They... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
knows. This prevents cases where, for instance, the two models would have an information mismatch, which
could result in favoring hallucinations. The model architecture and hyper-parameters are identical to those
of the pretrained language models, except that the classification head for next-token prediction is replace... | Llama2 |
Computational challenges in deep learning.
Practice of Parallel Programming, PPoPP ’19, pages 1–14, New York, NY, USA. ACM.
[Jiang et al., 2021] Jiang, L., Hwang, J. D., Bhagavatula, C., Bras, R. L., Forbes, M., Borchardt, J., Liang, J.,
Etzioni, O., Sap, M., and Choi, Y. (2021). Delphi: Towards machine ethics and no... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Improving the Structure
D.2
We find that increasing the number of attention
blocks (e.g., from a total of 4 – 8 to a total of
32+) in the latent diffusion model can improve
the general structure of the songs, thanks to the
long-context view. If the model is trained without
attention blocks, the context provided by the... | MOUSAI |
The contributions of all authors are listed as follows: Yujia Qin, Shengding Hu, Yankai Lin, Zhiyuan Liu, and
Maosong Sun initiated (2022.8) and organized the research. Yujia Qin drafted the abstract. Yujia Qin and
Ning Ding drafted the introduction. Zheni Zeng drafted § 2.1. Ning Ding drafted § 2.2 and § 2.3. Yujia Qi... | Tool Learning with Foundation Models |
Antifragile Design. Core game mechanics can in | The Open Problems of Onchain Games |
Following the power-law relationship proposed
by Kaplan et al. (2020), Hoffmann et al. (2022) con-
duct experiments on much larger language models
and draw a different conclusion that model size and
dataset size should scale at roughly the same rate
with more compute budget.
2.1.2 Data Repetition
While Kaplan et al. (... | DataManagementForLargeLanguageModels-ASurvey |
from the very start. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
to cross-cutting political | Social_Media_and_Democracy |
47 | TheRiseandPotentialofLargeLanguageModel BasedAgents |
deep learning. In Proceedings of the aaai conference on artificial intelligence, 2017.
[22] D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik,
H. He, D. Song, and J. Steinhardt. Measuring coding challenge competence with APPS. In
Thirty-fifth Conference on Neural Information Proces... | Teaching Large Language Models to Self-Debug |
The FastSpeech [460] algorithm aims to improve the inference speed of TTS systems. To achieve
this, it utilizes a feedforward network based on 1D convolution and the self-attention mechanism in
transformers to generate Mel-spectrograms in parallel. Additionally, it solves the issue of sequence
length mismatch between t... | AReviewofDeepLearningTechniquesforSpeechProcessing |
dance. So the percentage of the entire students enrolled in hip-hop dance is the percentage of the students
enrolled in hip-hop dance, minus the percentage of the students enrolled in contemporary dance, minus the
percentage of the students enrolled in jazz dance. So the percentage of the entire students enrolled in hi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
A. Joulin, F. Bach, and J. Ponce. Discriminative clustering for image co-segmentation.
In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
pages 1943–1950. IEEE, 2010. 12
L. Kaiser, M. Babaeizadeh, P. Milos, B. Osinski, R. H. Campbell, K. Czechowski, D. Er-
han, C. Finn, P. Kozakowski,... | A Cookbook of Self-Supervised Learning |
Level 4, 𝑉4=74 Level 8, 𝑉8=223 Level 12, 𝑉12=676 Level 16, 𝑉16=2048Inputw/ topology warmupw/otopology warmup(b)Inputw/ ℒcurvw/oℒcurv(a)8463 References
[1] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter
Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan.
Mip-nerf: A multiscale representation for anti-... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
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| Stanford alpha CRFM |
50
Figure 34: BERTScore accuracy (BSA) for instruction-tuned (IT) and non-instruction-tuned (Non-IT) LLaMA
models using the open prompt in the settings of zero-shot (ZS) and few-shot (FS).
Figure 35: Edit distance (ED) for instruction-tuned (IT) and non-instruction-tuned (Non-IT) LLaMA models using
the closed prompt ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
A.4 Cross-lingual zero-shot TTS test data filtering
We create a test set for each language by selecting samples from the MLS test split which have
Whisper transcription WER lower than 20% (or 30% for Polish and Portugueses test splits which
contains less than 1K samples), because we found MLS test set contains many ex... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
discussion
As online advertising continues to evolve – as well as spread to more electoral
contexts, especially down the ballot – a number of important questions demand
answers. For example, what is the impact of online spending? There was
significant concern among many policymakers and among the general public
that Ru... | Social_Media_and_Democracy |
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1 Introduction | PaLM 2 Technical Report |
1
We base our approach on implicit functions (IFs), which
go beyond parametric body models to represent fine shape
details and varied topology. IFs allow recent methods to infer
detailed shape from an image [22, 24, 54, 55, 65, 70]. Despite
promising results, state-of-the-art (SOTA) methods struggle
with in-the-wild d... | ICON |
About half or more also see mitigating steps that would make the use of robotic
exoskeletons, facial recognition technology by police and gene editing in babies to greatly
reduce the risk of serious disease during their lifetime more acceptable.
A map to this report
The chapters that follow cover a broad terrain. | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
6. Current challenges (and ideas to go forward)
Finally, we discuss a set of open challenges that we identified for knowledge-based explainable systems.
Knowledge graph maintenance Explainable AI systems require completeness and accuracy. This means that an important
challenge for the field of Knowled... | Knowledge graphs as tools for explainable machine learning: A survey |
20
for tasks with larger datasets, a higher initial learning rate and a
lower momentum may be more suitable, while for tasks with smaller
datasets, a lower initial learning rate and a higher momentum may be
more suitable. Additionally, for tasks with more complex models, a
higher initial learning rate and a lower mom... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
0.94
0.41
0.93
0.38
Table 36: Qualitative example for the impact of safety data scaling. Llama 2-Chat learns to not generate
offensive or problematic content.
61
➤ Prompt: You are a woman with strong opinions about pizza. You think Chicago pizza is the best, and
that pizza should never be folded. You think pinea... | Llama2 |
• Meta Tool Learning. Metacognition (Clarebout et al., 2013) is a crucial aspect of human intelligence that
allows individuals to reflect on their own thinking and adapt their behaviors when faced with unfamiliar
situations. In the context of tool learning, metacognition refers to the ability of a model to reflect on its... | Tool Learning with Foundation Models |
understood within the broader media ecosystem in the United States which,
as Benkler et al. (2018) show, exhibits a similar asymmetry in terms of
audience and production, with political clickbait and hyper-partisan content
being more prevalent on the right than on the left. | Social_Media_and_Democracy |
99 Introducing Superalignment, OpenAI, 2023;
Google DeepMind CEO Demis Hassabis Says Some Form of AGI Possible in a Few Years, WSJ, 2023
100 We are referring to three surveys:
1. Grace et al, 2018, When will AI exceed human performance? Evidence from AI experts, Journal of Artificial
Intelligence Research, 62, 7... | Capabilities and risks from frontier AI |
1
Introduction | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Human: Can you write a summary of this article for me?
Assistant: Sure, here it is:
...Text...
...Summary...
As shown in Figure 20, large preference models trained on a mixture of HH and LtS datasets perform equally
well on both. So at least at the level of preference modeling, there seems to be no cost to mixing H... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Abdullatif Köksal, Renat Aksitov, and Chung-Ching
Chang. 2023. Hallucination augmented recitations
for language models.
Zhenzhong Lan, Mingda Chen, Sebastian Goodman,
Kevin Gimpel, Piyush Sharma, and Radu Soricut.
2020. Albert: A lite bert for self-supervised learning
of language representations. In International Conf... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Distributed Computing by Population Protocols
Supervisor: Professor Tomasz Radzik | informatics-phd-projects-2022-23 |
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... | Announcing Jurassic-2 and Task-Specific APIs |
Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Al-
shamsi, Alessandro Cappelli, Ruxandra Cojocaru,
Maitha Alhammadi, Mazzotta Daniele, Daniel Hes-
low, Julien Launay, Quentin Malartic, Badreddine
Noune, Baptiste Pannier, and Guilherme Penedo.
2023. The falcon series of language models: To-
wards open frontier models.
... | LLM in a flash |
using building blocks), which requires the agent to have a more grounded understanding of the
instructions. Unlike the existing open-loop paradigm, AlphaBlock constructs a dataset comprising
35 complex high-level tasks, along with corresponding multi-step planning and observation pairs,
and then fine-tunes a multimodal... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
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Preprint.
STANDING ON THE SHOULDERS OF GIANT
FROZEN LANGUAGE MODELS
Yoav Levine, Itay Dalmedigos, Ori Ram, Yoel Zeldes, Daniel Jannai,
Dor Muhlgay, Yoni Osin, Opher Lieber, Barak Lenz,
Shai Shalev-Shwartz, Amnon Shashua, Kevin L... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
2.3. Ray Tracing
Our ray tracing algorithm is similar to IDR [69], except that we do not perform the sphere ray tracing with signed distance
values (SDF). This is because SDFs are not guaranteed to be correct in value after non-rigid deformation, and might lead
to over-shooting. For this reason, we also eliminated the... | I M Avatar- Implicit Morphable Head Avatars from Videos |
0.0526
0.0822
0.0476
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0.0469
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0.1444
0.1449
77
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70
Smelt and craft a golden_pickaxe.
Smelt and craft a golden_shovel.
Smelt and craft a golden_sword.
Smelt and craft a golden_hoe.
Smelt and craft a golden_axe.
Smelt and craft a golden_apple.
Smelt and craft a clock.
Smelt and craft a... | JARVIS-1 |
6.1 Documenting Methods
To document the Pile, we chose to implement two
frameworks that have been proposed by method-
ologists and ethics researchers. The first, the
datasheets methodology (Gebru et al., 2018), is a
general purpose methodology that is recommended
by several methodologists (Raji and Yang, 2019;
Biderman ... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
12 Frontier AI – Capabilities and Risks
Figure 6. Performance on broad benchmarks such as BIG-Bench and MMLU
improves with more training compute. This figure was taken from Owen 2023.
Although average performance, aggregated across many downstream tasks, improves fairly
predictably with scale, it is much harder t... | Capabilities and risks from frontier AI |
[Shuster et al., 2021] Kurt Shuster, Spencer Poff, Moya
Chen, Douwe Kiela, and Jason Weston. Retrieval aug-
mentation reduces hallucination in conversation. arXiv
preprint arXiv:2104.07567, 2021.
[Srivastava et al., 2022] Aarohi Srivastava, Abhinav Ras-
togi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid,
Adam Fisch,... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
30
Offer Holders
Accepting an Offer
4.1.1 Undergraduate Applicants
1. All applicants for full-time undergraduate degree programmes will be informed by UCAS of the
date by which they have to make a formal response to the offers they have received, either
accepting them firmly, or on an insurance basi... | UCL Academic Manual |
reversible learning. In ICML, 2015. 3, 5
Aravindh Mahendran and Andrea Vedaldi. Understanding deep image representations by inverting them. In
CVPR, 2015. 3
In NIPS, 2017. 9
Saeid Motiian, Quinn Jones, Seyed Iranmanesh, and Gianfranco Doretto. Few-shot adversarial domain adaptation.
Luis Muñoz-González, Battista B... | DATASET DISTILLATION |
(cid:26)Rs(g | p)
Rc(g | p) =
Rh(g | p) otherwise
˜Rc(g | p) = whiten(logit(Rc(g | p)))
if is_safety(p) or Rs(g | p) < 0.15
For all models, we use the AdamW optimizer (Loshchilov and Hutter, 2017), with β1 = 0.9, β2 = 0.95, eps =
10−5. We use a weight decay of 0.1, gradient clipping of 1.0, and a constant learning r... | Llama2 |
and conference publications. For more than ten years, he has been with the
masters and undergraduate students as a supervisor of their thesis work.
His research interests include artificial intelligence (AI), machine learning,
deep learning, natural language processing (NLP), and big data analysis.
He has served as a pr... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Chen, S., Wong, S., Chen, L., and Tian, Y. Extending
context window of large language models via positional
interpolation. arXiv preprint arXiv:2306.15595, 2023b.
Chen, Y., Qian, S., Tang, H., Lai, X., Liu, Z., Han, S., and
Jia, J. Longlora: Efficient fine-tuning of long-context
large language models. arXiv preprint a... | Self-Extend LLM |
of
unified chip2,
uni-
and
unified multi news
las:
Building on the GPT4All dataset, we curated
the GPT4All-J dataset by augmenting the origi-
nal 400k GPT4All examples with new samples
encompassing additional multi-turn QA samples
and creative writing such as poetry, rap, and short
stories. We designed prompt templa... | 2023_GPT4All-J_Technical_Report_2 |
Several methods are also proposed to find the
proper domain composition weights. DSIR (Xie
et al., 2023b) formulates the problem as selecting
a subset of raw unlabeled datasets to match target
distribution given some unlabeled target samples.
Specifically, it leverages the classic importance re-
sampling approach (Rubi... | DataManagementForLargeLanguageModels-ASurvey |
We remark that it makes sense that the generation of words like “the”, “a”, “and” or “,” would be induced by
distance-based, local attention heads, since those are tokens with a grammatical role which depends on the short-
range interactions within a single sentence. On the other hand, the main entities in the story su... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
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