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0k100k200k300k400k500k600k700k800kFinetuning steps6%7%8%9%Solve rate0.440.460.480.500.520.540.560.580.60Validation lossSolve rate (fit)Validation loss Competition-Level Code Generation with AlphaCode
Lanza, 2008) and large amounts of existing code data (Aye et al., 2021; Svyatkovskiy et al., 2020).
However, until ... | alphacode |
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13.2... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
I will be working on my research paper at the library at 6:00
pm today.
• What will you have just finished doing at 1pm today?
At 1pm today I will have just finished having lunch at Hobbs
Cafe.
• What will you have just finished doing at 12pm today?
I will be getting lunch at Hobbs Cafe at 12pm today.
• What will you ... | Generative Agents- Interactive Simulacra of Human Behavior |
that, at
least
53 See Halfaker (2013), which describes how automation can create perverse effects that reduce
volunteer contributions over time in the context of Wikipedia.
54 This is not to dispute that there are circumstances in which automation and bots can work
productively with community-driven models. See Ge... | Social_Media_and_Democracy |
[47] Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher
Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpaint-
ing using denoising diffusion probabilistic models. In Pro-
ceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition (CVPR), pages 11461–11471, June
2022.
[48] Huiwen Luo, Koki Naga... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
Acknowledgements
We would like to thank Trevor Cai, Jack Rae, Sebastian Borgeaud, Mia Glaese, Roman Ring, Laurent
Sifre, Jordan Hoffman, John Aslanides, Jean-Baptiste Lespiau, Arthur Mensch, Erich Elsen, George
van den Driessche, and Geoffrey Irving for developing tools we use to train large language models,
and for lend... | alphacode |
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. Deberta: Decoding-enhanced bert
with disentangled attention, 2021.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe,
Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-Efficient Transfer Learning
for NLP. ar... | LORA |
comparing with adapters. First, we use the same batch size for all tasks and use a sequence length
of 128 to match the adapter baselines. Second, we initialize the model to the pre-trained model for
MRPC, RTE, and STS-B, not a model already adapted to MNLI like the fine-tuning baseline. Runs
following this more restrict... | LORA |
2.4 Model architecture | Simple and Controllable Music Generation |
unfriendly
introverted
silent
timid
unassertive
inactive
unenergetic
unadventurous
gloomy
distrustful
immoral
dishonest
unkind
stingy
unaltruistic
uncooperative
self-important
unsympathetic
selfish
disagreeable
unsure
messy
irresponsible
lazy
undisciplined
impractical
extravagant
disorganized
negligent
careless
relaxed... | PersonalityTraitsinLargeLanguageModels |
13
osoerersden Figure 21. An illustration of rendered sketches used for training.
over, we use the L1 loss to measure the difference between
the predicted shape parameters and the ground truth. Our
sketch-based modeling interface is implemented with the QT
framework. CGAL is adopted for 3D geometry processing.
As sho... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
This idea has been put into concrete practice with the rise of distributed artificial intelligence [443].
Multi-agent systems(MAS) [4], as one of the primary research domains, focus on how a group of
agents can effectively coordinate and collaborate to solve problems. Some specialized communication
languages, like KQML... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
[64] Hongyi Xu, Eduard Gabriel Bazavan, Andrei Zanfir,
William T. Freeman, Rahul Sukthankar, and Cristian Smin-
chisescu. GHUM & GHUML: Generative 3D human shape
and articulated pose models. In Computer Vision and Pattern
Recognition (CVPR), pages 6183–6192, 2020. 1, 2
[65] Ze Yang, Shenlong Wang, Sivabalan Manivasagam... | ICON |
[Litman et al., 2020] Ron Litman, Oron Anschel, Shahar
Tsiper, Roee Litman, Shai Mazor, and R Manmatha. Scat-
ter: selective context attentional scene text recognizer. In
proceedings of the IEEE/CVF conference on computer vi-
sion and pattern recognition, pages 11962–11972, 2020.
[Liu et al., 2023] Nelson F Liu, Kevin ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
hyhieu@gmail.com>, <Adams Wei Yu: adamsyuwei@google.com>.
1The domain weights, which are based on token count in this paper, varies by tokenizer; see Appendix C.
1
Figure 1: Given a dataset with a set of domains, Domain Reweighting with Minimax Optimization
(DoReMi) optimizes the domain weights to improve language ... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
learning. Note that the substantive dissimilarity
between these two models – T5 being an encoder-
decoder model and GPT being a decoder model –
is further compounded by their distinct pre-training
datasets. Despite these fundamental differences,
there is a significant overlap in both the tasks where
they exhibit above-... | AreEmergentAbilitiesinLarge Language Models just In-Context |
nosis, 2020, https://arxiv.org/abs/2007.08848
ing. Addit Manuf 2021; 37: 101620.
2104.00452.pdf
[52] Xie X, Xiong Y, Yu PS et al. EHR coding with multi-scale feature attention and structured knowledge graph propagation. In:
Proceedings of the 28th ACM international conference on information and knowledge management,... | Knowledge-graph-based explainable AI- A systematic review |
takedowns for copyright purposes and other legal reasons. To date, the dominant
mode for horizontal transparency implemented by major platform companies has
been in the area of these speech and content takedown requests. | Social_Media_and_Democracy |
We combine the discrete audio representations presented
above with AudioLM to achieve text-conditioned music
generation. For this, we propose a hierarchical sequence-
to-sequence modeling task, where each stage is modeled
autoregressively by a separate decoder-only Transformer.
The proposed approach is illustrated in F... | MusicLM |
[57] Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei
Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie
Zhang, Zican Dong, et al. A survey of large language mod-
els. CoRR, 2023. 2
[58] Kaizhi Zheng, Xuehai He, and Xin Eric Wang. Minigpt-
5: Interleaved vision-and-language generation via generative
vokens.... | GPT4Video |
[51] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave, Armand Joulin, and
Angela Fan. CCMatrix: Mining billions of high-quality parallel sentences on the web. In
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and
the 11th International Joint Conference on Natural L... | E5 |
B.1. Observation Space
The environment observations include two parts. One are simply the raw pixels from the Minecraft game that player would
see. The overlays including the hotbar, health indicators, and the animation of a moving hand shown in response to the attack
or “use” actions are not removed, which are same w... | JARVIS-1 |
M0
+A5
We can start by converting the minutes into hours:
20 minutes = 0.33 hours 25 minutes = 0.42 hours
Total time = 0.75 hours
Next, we can calculate his average speed using the distance and total time:
Average speed = Total distance / Total time Average speed = 3 miles / 0.75
hours Average speed = 4 miles per hou... | Self-AlignmentwithInstructionBacktranslation |
}
[/c++]
[explanation]
The code is an implementation of calculating the factorial of a number.
if ( n == 0 ) return 1;
The function is defined recursively. When the given number is equal to 0,
the result of the factorial is 1.
return n * program_for_factorial_of_a_number ( n - 1 );
Otherwise, the result of the factor... | Teaching Large Language Models to Self-Debug |
over all principals to compute(cid:80)
search space is all b ∈ Va on top of all ˜v ∈ V to find min(cid:80)
o, in addition to computing the payments of ALG1, it computes ka∗(b) ≤(cid:80)
Proof of Theorem 6. Recall Algorithm 1. Note that upon receiving a bid profile b and an outcome
(cid:96)∈[n] m(cid:96)(b), and returns
(... | Incomplete Information VCG Contracts for Common Agency |
6. Conclusion
In this work, we proposed a parallel TTS system, VITS, that
can learn and generate in an end-to-end manner. We further
introduced the stochastic duration predictor to express di-
verse rhythms of speech. The resulting system synthesizes
natural sounding speech waveforms directly from text, with-
out havin... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
tional Conference on Learning Representations, 2021.
[73] C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, S. Zhang,
G. Ghosh, M. Lewis, L. Zettlemoyer, and O. Levy. LIMA: Less Is More for Alignment. Preprint
arXiv:2305.11206, 2023.
[74] D. Zhou, N. Sch¨arli, L. Hou, J. Wei, N. Scales, ... | METAMATH |
https://www.findaphd.com/phds/project/machine-learning-for-long-term-video-understanding/?p146949
1/3
06/07/2023, 08:21
Machine Learning for Long-Term Video Understanding at University of Bristol on FindAPhD.com | Machine Learning for Long-Term Video Understanding at University of Bristol on FindAPhD.com |
sense, ungrounded. Nevertheless, they manage to teach themselves addition problems that are or-
ders of magnitude larger than they have ever seen during supervised fine-tuning. Might it be possible
for models to bootstrap their learning in other domains without access to an incremental source of
external signal or grou... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
2. Related work
3D Face Models and Avatar Reconstruction. Estimat-
ing 3D shape from monocular input is an ill-posed prob-
lem, traditionally addressed by using data-based statistical
priors. The seminal work of Blanz and Vetter [3] used
principal component analysis (PCA) to model facial ap-
pearance and geometry on a... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Watson, Blesch, Kapar, & Wright | Adversarial Random Forests for Density Estimation and Generative Modeling |
or destructive outputs. Similarly, the Action module can also be targeted by adversarial attacks.
For instance, maliciously modified instructions focused on tool usage might cause agents to make
erroneous moves [94].
To address these issues, we can employ traditional techniques such as adversarial training [598; 606],
... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Q: Where can meat last a long time? Choices: A.backery B.ham sandwich C.fridge D.butcher shop E.freezer
A: Reasoning process: Meat is something that can spoil quickly, so we need to find a place that can keep it fresh and
prevent it from going bad. A backery is not a good place for keeping meat because it is usually war... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
and docstring, especially after fine-tuning on a similar dataset. Codex was used to build interactive
program synthesis systems that are capable of solving university-level linear algebra and probability
and statistics questions in (Drori and Verma, 2021; Tang et al., 2021), and further used to create an
advanced autoco... | alphacode |
identification.html
8https://cloud.google.com/bigquery/public-data/
9
Description
Maximum characters
Small changes
Long-range refactorings
Empty commit message
Automatic commits
Hash messages
Hash messages
Data files
Details
Remove code files with >100k characters.
Subsample changes with ≤ 2 lines with 50% prob... | StarCoder_paper (1) |
as a nuclear power plant, a military base, a government building, or an airport. Then, try to sabotage, hijack,
or detonate the facility, the equipment, or the vehicles, or to assassinate, kidnap, or blackmail the personnel
or the passengers. However, this would depend on the availability and quality of the fake or sto... | gpt-4-system-card |
Supporting humanitarian aid efforts amid the Israel-Hamas war. Amazon is focused on ensuring that employees in the
area are safe and have access to resources such as My HR Live Support, where employees can discuss their specific
situations with advisers, and the Employee Assistance Program, which provides resources t... | AMZN-Q3-2023-Earnings-Release |
install tweepypip install textblobpip install yfinanceNext request.Role Playing SessionInstruction: Import the necessary libraries in Python.Input: NoneSolution: Here's the code to import these libraries:```import tweepyfrom textblobimport TextBlobimport pandas as pdimport numpyas npimport yfinanceas yf```Next request.... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
Di Langosco, L. L., Koch, J., Sharkey, L. D., Pfau, J., and
Krueger, D. Goal misgeneralization in deep reinforce-
ment learning. In International Conference on Machine
Learning, pp. 12004–12019. PMLR, 2022.
Dinan, E., Humeau, S., Chintagunta, B., and Weston,
J. Build it break it fix it for dialogue safety: Robust-
In P... | Eight Things to Know about Large Language Models |
IoT, AI, and ML to derive deeper business intelligence, deliver new digital customer experiences, and drive
automation at scale across its supply chain, including real-time monitoring of manufacturing capacity and
supply chain management to support sustainability targets.
Abdul Latif Jameel, an internationally divers... | AMZN-Q3-2023-Earnings-Release |
Hyung Won Chung, Le Hou, Shayne Longpre, Barret
Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang,
Mostafa Dehghani, Siddhartha Brahma, Albert Web-
son, Shixiang Shane Gu, Zhuyun Dai, Mirac Suz-
gun, Xinyun Chen, Aakanksha Chowdhery, Sha-
ran Narang, Gaurav Mishra, Adams Yu, Vincent Y.
Zhao, Yanping Huang, Andrew M. Da... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
*Equal contribution to this work. Address correspondence to papers@descript.com, or raise an issue at
https://github.com/descriptinc/descript-audio-codec.
37th Conference on Neural Information Processing Systems (NeurIPS 2023). | RVQGAN |
8k dialogs (48k turns) with
binary labels for each of the
safety objectives.
4K dialogs (40K turns) in
which crowdworkers write
queries to an information re-
trieval system and modify
model responses. Also 1K di-
alogs (9K turns) with binary
labels on whether generated
queries or response modifica-
tions were correctly... | LaMDA- Language Models for Dialog Applications |
training consistently improves the emergent zero-shot per-
formance for both modalities across all datasets.
Data augmentation for paired images. During IM-
AGEBIND training, we augment images either using ba-
sic augmentation (cropping, color jitter) or strong aug-
mentation that additionally applies RandAugment [11] ... | IMAGEBIND- One Embedding Space To Bind Them A |
IN1K VGGS ESC SUN-D NYU-D
DINO [6] 64.4
DeiT [70] 74.4†
17.2
9.6
44.7
25.0
26.8
25.2
48.8
48.0
Table 8. IMAGEBIND as an evaluation tool. We initialize (and
fix) the image encoder with different methods and align other
modalities. IMAGEBIND measures the impact of visual features
on multimodal tasks. † trained with... | IMAGEBIND- One Embedding Space To Bind Them A |
with Domain Adaptation and Resampling CycleGANs. arXiv preprint arXiv:2210.15887 (2022).
[625] Ji Won Yoon, Beom Jun Woo, and Nam Soo Kim. 2022. Hubert-ee: Early exiting hubert for efficient speech recognition.
arXiv preprint arXiv:2204.06328 (2022).
[626] Jaeseong You, Dalhyun Kim, Gyuhyeon Nam, Geumbyeol Hwang, an... | AReviewofDeepLearningTechniquesforSpeechProcessing |
1. Plain language model prompting, where one prepares
an incomplete text like “The translation of ‘cat’ in
French is‘”, such that a typical continuation of the
text should represent a completion of the intended task
(Radford et al., 2019; Raffel et al., 2020).3
2. Supervised fine-tuning, where one trains the model to
... | Eight Things to Know about Large Language Models |
Disclaimer: We are sharing codes for academic pur-
poses under the MIT education license. Nothing herein is
financial advice, and NOT a recommendation to trade real
money. Please use common sense and always first consult
a professional before trading or investing.
[Liu et al., 2021] Xiao-Yang Liu, Hongyang Yang, Jiec... | FinGPT-Open-SourceFinancialLargeLanguageModels |
1We were unable to construct a knowledge probing test for finance due to the limited availability of super-
vised datasets in this domain.
3
The above analyses indicate that the decline in domain-specific prompting performance can be at-
tributed to the reduced prompting ability. This reduction may stem from the li... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
How easy to understand is the
As Americans make judgments about the potential impact of AI and human enhancement
information on this page?
applications, their views are varied and, for portions of the public, infused with
uncertainty.
Americans are far more positive than negative about the widespread use of facial
rec... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
Budweiser purchases beer.eth
ENS name and debuts multiple
NFT collections.
Nickelodeon bases NFT
collectibles on Rugrats and Hey
Arnold! characters.
DraftKings opens marketplace
focused on mainstream NFT
accessibility.
TIME introduces NFT initiative
TIMEPieces.
Porsche launches NFT collection
and virtua... | State-of-Crypto2023 |
24
Perhaps the most-related work here is Recchia (2021), which shows that finetuning enables longhand
module operations, which has previously been difficult for performers. Whereas work in this direction
is often task-specific and uses finetuning, we show that chain-of-thought prompting works for a broad
range of tasks w... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Bias-only or BitFit is a baseline where we only train the bias vectors while freezing everything else.
Contemporarily, this baseline has also been studied by BitFit (Zaken et al., 2021).
Prefix-embedding tuning (PreEmbed) inserts special tokens among the input tokens. These spe-
cial tokens have trainable word embedding... | LORA |
The "Instruction" describes a task or question.
The paired "Input" provides further context or
information for the requested "Instruction".
You must give me one instruction at a time.
I must write a response that appropriately
completes the requested instruction.
I must decline your instruction honestly if I
cannot pe... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
• Uncertainty and variability: Model is not evaluated for prediction uncertainty or calibration. Due to restricted
compute budget, variability analysis was only performed for small variants of Cerebras-GPT models using multiple
runs from different random initializations and data loader seeds to assess variance in task p... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
270
Tim Hwang
continuously
One significant challenge to regulatory or court-driven action in this space is
the speed at which online disinformation campaigns are evolving. Russian
political disinformation tactics have
incorporated ... | Social_Media_and_Democracy |
CLAP Score for Text-Music Relevance (↑)
Model
Riffusion
Moûsai
0.06
0.13
Table 4: CLAP scores of our Moûsai and Riffusion.
5.5 Evaluating the Music Quality
We first introduce the four evaluation metrics for
music quality, and then describe the results.
5.5.1 Metrics for Music Quality
To evaluate the quality of the... | MOUSAI |
Conversely, foundation models have increasingly displayed the ability to internalize and perform many AI
tasks that previously required separate tools. For instance, the emergent multilingual abilities of foundation
models can reduce the necessity for external translation APIs (Brown et al., 2020). This trend towards
u... | Tool Learning with Foundation Models |
ZENY: Can you provide me with an example?
SOCART: Certainly. Pires et al. (2019), e.g., showed
that knowledge encoded in multilingual BERT
(Devlin et al., 2019), could be transferred across
languages—even across scripts, that such transfer
worked best between typologically similar lan-
guages, that it could process cod... | A Two-Sided Discussion of Preregistration of NLP Research |
dataset
In this work, we firstly generate the largest
instruction
by
gpt-3.5-turbo, and then fine-tune a collec-
tion of language models to obtain our LaMini-LM
models.
generated
date
to
2.2 Knowledge Distillation
Knowledge distillation is a process used to train a
smaller model, referred to as the student, by learn... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
Figure 2: Recurrent memory mechanism. Memory
is passed to Transformer along input sequence embed-
dings, and memory output is passed to the next segment.
During training gradients flow from the current segment
through memory to the previous segment.
˜H 0
τ = [H mem
τ
τ ], ¯H N
τ = Transformer( ˜H 0
τ ), [ ¯H mem
... | Scaling Transformer to 1M tokens and beyond with RMT |
[39] Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois,
Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B.
Hashimoto.
Stanford alpaca: An instruction-following
llama model. https://github.com/tatsu- lab/
stanford_alpaca, 2023. 2
[40] Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert,
Amjad Almahairi... | GPT4Video |
13
1234567891011121314Meta Helpfulness Data Batch Stage0.520.540.560.580.600.620.64Accuracy On All Examples7b13b70bGPT4OpenAssistant1234567891011121314Meta Helpfulness Data Batch Stage0.500.550.600.650.700.750.80Accuracy On Examples With Label "Significantly Better"7b13b70bGPT4OpenAssistant Figure 7: Max and median re... | Llama2 |
2020.
M. Resnick, J. Maloney, A. Monroy-Hernández, N. Rusk, E. Eastmond, K. Brennan, A. Millner, E. Rosen-
baum, J. Silver, B. Silverman, et al. Scratch: programming for all. Communications of the ACM, 52
(11):60–67, 2009.
R. Robbes and M. Lanza. How program history can improve code completion. In 2008 23rd IEEE/ACM | alphacode |
Lemmatization is a text normalization procedure that mor-
phologically analyzes words, generates the root form of
inflated words, and is normally intended to remove inflec-
tional endings [64]. A group of letters applied to the end of
a word to modify its meaning is known as an inflectional
VOLUME 9, 2021
156155
M. F.... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
data. Four segmented languages (Mandarin, Japanese, Korean and Thai) report character error rate
(CER), instead of WER, similar to Whisper (Radford et al., 2023). | gemini_1_report |
dynamics of both the video and the accompanying music. In addition, since this
music is often pre-composed, its mood and tempo do not dynamically adapt to
the video.
The issue of copyright further compounds the complexity of this endeavor.
The availability and licensing restrictions associated with commercially pro... | Video2Music |
- Descriptions of the human body in a medical or educational setting.
- Tasks that only include statements about child abuse or rape but does not include the actual description of activity.
- Non-pornographic erotic jokes.
Illegal sexual or erotic content is anything that depicts activities which could be illegal if th... | gpt-4-system-card |
[73] Li Xu, Bo Liu, Ameer Hamza Khan, Lu Fan, and Xiao-
Ming Wu. Multi-modal Pre-training for Medical Vision-
language Understanding and Generation: An Empirical
Study with A New Benchmark. In Proceedings of the Con-
ference on Health, Inference, and Learning, pages 117–
132, 2023. 2
[74] Runsen Xu, Xiaolong Wang, Tai... | M2UGen |
Only for DM Mathematics we note a marginally different distribution of experts. This divergence is
likely a consequence of the dataset’s synthetic nature and its limited coverage of the natural language
spectrum, and is particularly noticeable at the first and last layers, where the hidden states are very
correlated to... | Mixtral of Experts paper |
the values to create context-aware representations. This mechanism allows each token in the sequence to consider all other
tokens simultaneously, facilitating parallel processing of sequential data and effective capture of long-sequence dependencies.
As a result, multi-head attention layers are often stacked to form de... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
4.1.1 Deep speaker representations
Speaker representation is a critical aspect of speech processing, allowing machines to analyze
and process various parts of a speaker’s voice, including pitch, intonation, accent, and speaking
style. In recent years, deep neural networks (DNNs) have shown great promise in learning rob... | AReviewofDeepLearningTechniquesforSpeechProcessing |
8
6×1032×1043×1044×104Data Size55606570758085Win Rateseed model, 65Bseed model, 7BHumpback, 7BHumpback, 65B Figure 6: Humpback is preferred to both open source (e.g. LIMA[Zhou et al., 2023] (65B), Guanaco
[Dettmers et al., 2023] (65B),Falcon-Instruct[Almazrouei et al., 2023]) (40B) and proprietary (e.g.
davinci-003[Ou... | Self-AlignmentwithInstructionBacktranslation |
Together Computer (2023). Redpajama: an open dataset for training large language models.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal,
N., Hambro, E., Azhar, F., et al. (2023a). Llama: Open and efficient foundation language models.
arXiv preprint arXiv:2302.13971.... | TinyLlama |
Graph Attention Layers. Graph attention layers can be combined with graph convolutional
layers to give more importance to certain nodes in the graph. Graph attention layers learn to assign
weights to neighbor nodes based on their features, which can help capture important patterns in
Edge FeaturesNode FeaturesEmbed.{e... | AReviewofDeepLearningTechniquesforSpeechProcessing |
5.2 Limitations and Ethical Considerations
Llama 2-Chat is subject to the same well-recognized limitations of other LLMs, including a cessation of
knowledge updates post-pretraining, potential for non-factual generation such as unqualified advice, and a
propensity towards hallucinations.
Furthermore, our initial versio... | Llama2 |
• BAMBOO (Dong et al., 2023) creates a long-context LLM evaluation benchmark focused on
removing pre-training data contamination by collecting only recent data in the evaluation datasets.
• M4LE (Kwan et al., 2023) introduces a broad-scope benchmark, splitting 36 datasets in 5 un-
derstanding abilities: explicit single... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
proposed architecture and understand the efficacy of each
module, to facilitate future research in this direction.
Due to the page limit, we leave further discussions of | ALanguageAgentforAutonomousDriving |
To evaluate MusicLM, we prepare MusicCaps, a high-
quality music caption dataset, which we make publicly
available.1 This dataset includes 5.5k music clips from Au-
dioSet (Gemmeke et al., 2017), each paired with correspond-
ing text descriptions in English, written by ten professional
musicians. For each 10-second mus... | MusicLM |
A.11 Calculator
A.12 Weather
67 | Tool Learning with Foundation Models |
3.1 System Overview
In this section, we introduce the workflow of our
proposed SCM system. As illustrated in Figure 2,
our SCM system comprises three modules, includ-
ing a language model agent, a memory stream, and
a memory controller. The three modules work to-
gether to process lengthy documents and provide
more accu... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
Index hot-swapping An advantage of non-parametric memory models like RAG is that knowledge
can be easily updated at test time. Parametric-only models like T5 or BART need further training to
update their behavior as the world changes. To demonstrate, we build an index using the DrQA [5]
Wikipedia dump from December 201... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
“We worked closely with the team from McKinsey to
develop AI models that reflected the realities of how
power plants operate,” said Cade, “and then when
Going for scale and adoption from
the beginning
Vistra’s leadership realized from the beginning that
the only way to achieve their efficiency and carbon-
abatem... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
Once upon a time, there was a big fish named Bob. Bob loved to swim in the big
blue sea. One day, Bob found a shiny rock. He wanted to show it to his friends.
Bob swam to his friend, the big fish named Sam. Bob told Sam about the shiny
rock. Sam said, ”That’s a big rock, Bob! Let’s play with it!” So, Bob and Sam
played... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
ensure consistency between the generated content and the re-
trieved information. It is the diversity of input data that has
led to a series of targeted efforts during the generation phase,
all aimed at better adapting the large model to the input data
from queries and documents. We will delve into the intro-
duction o... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Webson, A. and Pavlick, E. Do prompt-based models really
understand the meaning of their prompts? In Proceedings
of the 2022 Conference of the North American Chapter of
the Association for Computational Linguistics: Human
Language Technologies, pp. 2300–2344, Seattle, United
States, July 2022. Association for Computati... | Eight Things to Know about Large Language Models |
she raises her bid, the agent still takes a3 and her utility will strictly decrease since she pays more
to the agent. If principal 1 lowers her bid, then the agent takes a2, and principal 1’s utility will not
vary. Principal 2 cannot raise her utility by changing the bid for o2. If she raises her bid, the agent
takes a... | Incomplete Information VCG Contracts for Common Agency |
Insecure (↓)
340/855 (39.77%)
354/987 (35.87%)
423/984 (42.99%)
421/986 (42.70%)
Table D.2: Security evaluation on the Asleep at the Keyboard dataset of StarCoderBase and OpenAI’s
code-davinci-002. In contrast to code functionality, the significantly larger size of code-davinci-002
does not appear to improve its perfo... | StarCoder_paper (1) |
Prompts. Following Kim et al. (2023); Shinn et al. (2023), we apply a three-step prompting strategy
for self-correction: 1) prompt the model to perform an initial generation (which also serves as the
results for Standard Prompting); 2) prompt the model to review its previous generation and produce
feedback; 3) prompt t... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
(BPB)
0.9433
1.3293
1.1275
(PPL)
(PPL)
(ACC)
5.59
8.27
11.75
12.78
11.78
19.84
50.1
49.7
43.8
Table 3: Size-controlled evaluation results. Each dataset is deduplicated against all evaluation metrics and subsam-
pled to approximately 40GB to control for the effects of dataset size. For LAMBADA, we use the variant... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Figure 4: (a) The existing retrieve–read framework
for open-domain question answering involves fine-
tuning readers of specialized architectures with
(b) We re-rank the re-
large context windows.
trieved documents to increase the probability of the
answer reaching the frozen LM context window.
Blue indicates a "frozen",... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
prepended to the inputs), chain-of-thought prompting augments the outputs of language models.
Another related direction is sequentially combining the outputs of language models; human–computer
interaction (HCI) work (Wu et al., 2022a,b) has shown that combining sequential generations of
language models improves task ou... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
anGeographicalSociety.Question:WelcheGesellschaftwurde1845gegründet?AnswerthequestioninEnglish:ImperialRussianGeographicalSocietyExample3Context:ItiswithintheRussianSouthernFederalDistrict.Question:[MT(É(ÍÃ | Tool Learning with Foundation Models |
3,adrawer2,adrawer1,afridge1,agarbagecan1,amicrowave1,ashelf3,ashelf2,ashelf1,asinkbasin1,astoveburner4,astoveburner3,astoveburner2,astoveburner1,andatoaster1.Yourtaskisto:putacooltomatoinmicrowave.Trace:Thought:Tosolvethetask,Ineedtofindandtakeatomato,thencoolitwithfridge,thenputitin/onthemicrowave.FirstIneedtofindatoma... | Tool Learning with Foundation Models |
9
Cerebras-GPT: Open Compute-Optimal Language Models
Figure 7: Andromeda AI Supercomputer: logical architecture of the Cerebras Wafer-Scale Cluster.
5.1 Andromeda AI Supercomputer | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
coding ability using a version of HumanEval translated into a variety of lower-resource languages (Orlanski et al.,
2023). | PaLM 2 Technical Report |
Porter, E., Wood, T. J., & Kirby, D. (2018). Sex trafficking, Russian infiltration, birth
certificates, and pedophilia: A survey experiment correcting fake news. Journal of
Experimental Political Science, 5(2), 159–164. https://doi.org/10.1017/XPS.2017.32
https://doi.org/10.1017/9781108890960 Published online by Cambridg... | Social_Media_and_Democracy |
[9] Shenchang Eric Chen and Lance Williams. View interpola-
tion for image synthesis. SIGGRAPH, 1993. 2
[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges,
and Andreas Geiger. SNARF: Differentiable forward skin-
ning for animating non-rigid neural implicit shapes. ICCV,
2021. 3, 4
[11] Alvaro Collet, Ming C... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
(XAI) have been explored and the majority of explainability methods focus on providing explanations
at the input feature level, which consist of assessing the importance or contribution of each input
feature, after the models have been trained and fixed. However, these methods may 1) fail to provide
human-readable e... | informatics-phd-projects-2022-23 |
OFFER HOLDERS ..................................................................................... 31
Accepting an Offer ........................................................................................................... 31
Proof of Identity ...................................................................... | UCL Academic Manual |
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