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beyond the full GPT-3 175B parameter model with no changes outside of model configurations.
The Weight Streaming design stands in contrast with existing accelerator execution modes. Recent trends in
large language model training typically require parallelizing training across tens to thousands of accelerator
devices, su... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Neural execution engines: Learning to execute subroutines. NeurIPS.
Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, and Xiang Ren. 2021. Refining language models
with compositional explanations. NeurIPS.
Xi Ye and Greg Durrett. 2022. The unreliability of explanations in few-shot in-context learning.
arXiv preprint arX... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
[71] OpenAI. Gpt-4 technical report, 2023. 4, 6, 7
[72] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-
Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.
Lora: Low-rank adaptation of large language models. arXiv
preprint arXiv:2106.09685, 2021. 4, 5, 20, 29
[73] Junnan Li, Dongxu Li, Silvio Savarese, and ... | Let’sThinkOutsidetheBox |
128:6
• Villa et al.
3.1.6 Moral Foundations Questionnaire (MFQ). Numerous factors, such as sociocultural context and individual
personality traits influence the perceptions of morality. We adapted the MFQ [31] to evaluate how the observer
integrates the concept of human augmentation into their personal values, cultu... | Society’sAttitudesTowardsHumanAugmentation |
26https://github.com/bigcode-project
27https://huggingface.co/datasets/bigcode/the-stack
32
salaried employees were considered, the decision to work with Toloka crowd-workers was taken after
a review of service providers and their compensation practices - most would not provide sufficient
transparency and guarantees... | StarCoder_paper (1) |
Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk
Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito,
David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani
Agrawal, Mark Omernick, Andrew M... | Llama2 |
Journal of Information Science, 2022, pp. 1–11 (cid:2) The Author(s), DOI: 10.1177/01655515221112844
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Rajabi and Etminani
5 | Knowledge-graph-based explainable AI- A systematic review |
Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022.
[111] Albert Webson and Ellie Pavlick. Do prompt-based models really understand the meaning of their prompts? In Proceedings of the 2022 Conference
of the North American Chapter o... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
input: 0, 0, 0, 0 0, 3, 4, 0 0, 7, 6, 0 0, 0, 0, 0 output: 3, 0, 0, 4 0, 0, 0, 0 0, 0, 0, 0 7, 0, 0, 6input: 0, 0, 0, 0 0, 5, 6, 0 0, 8, 3, 0 0, 0, 0, 0 output: 5, 0, 0, 6 0, 0, 0, 0 0, 0, 0, 0 8, 0, 0, 3input: 0, 0, 0, 0 0, +#, B, 0 0, @, 慶, 0 0, 0, 0, 0 output: +#, 0, 0, B 0, 0, 0, 0 0, 0, 0, 0... | LargeLanguageModelsasGeneralPatternMachines |
(2023) posited that excessively intricate exemplars do not aid simple problems. Instead, they added
the demonstrations with the correct answer to the samples pool and optimized the demonstrations
selection model via reinforcement learning. These studies either disregarded the hazards of utilizing
demonstrations contain... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Figure 3: Our proposed 1D U-Net architecture. Each
UNetBlock (top) consists of several U-Net items (bot-
tom). In each U-Net item (bottom), we use a 1D con-
volutional ResNet (R), and a modulation unit (M) to
provide the diffusion noise level as a feature vector con-
ditioning (
). For Stage 1, we use an inject item (I... | MOUSAI |
During each round of code generation, we execute the generated program to obtain environment
feedback and execution errors from the code interpreter, which are incorporated into GPT-4’s prompt
for the next round of code refinement. This iterative process repeats until self-verification validates
the task’s completion, ... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
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... | Language models can explain neurons in language models |
performing the style mixing at all denoising timesteps, we
begin mixing at different starting points, such that starting
later in the denoising process should preserve more details
from the geometry concept. | A Neural Space-Time Representation for Text-to-Image Personalization |
• Barn
• Dog
• Tortoise Plushy
• Cat
• Teddybear
• Wooden Pot
All models are trained on the same training set and ini-
tialization token, when applicable. For a list of all 15 text
prompts considered in the evaluation protocol, please refer
to Table 2.
12
Real Sample
& Prompt
No Time Conditioning
No Space Co... | A Neural Space-Time Representation for Text-to-Image Personalization |
pipeline where such filtering is only the first stage. Subsequently, crowd workers filter the subset down using human judge-
ment and at the final stage expert in photography are employed to create the dataset. While effective, this process has several
drawbacks compared to Diffusion-DPO. First, necessitating training ... | DiffusionModelAlignmentUsing Direct Preference Optimization |
Figure 1: Example of response of Code Llama - Instruct (34B) when queried for a specific shell command.
• Infilling. Autoregressive training and fine-tuning of LLMs is suitable for prompt completion, but does
not provide the capability to fill a missing portion of text while taking the full surrounding context into
ac... | CodeLlama2 |
Fine-tuning Fine-tuning aims to adapt a pre-trained LLM to downstream tasks, by updating weights
with the available supervision, which usually forms a dataset orders of magnitude smaller than the
one used for pre-training (Devlin et al., 2018). T5 (Raffel et al., 2020) was among the first to frame
fine-tuning into a te... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
Anna Rumshisky, et al. Alexatm 20b: Few-shot learning using a large-scale multilingual seq2seq model. arXiv preprint arXiv:2208.01448, 2022.
[96] Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya
Gupta, Adrià Garriga-Alonso, et al. Beyond ... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
In addition to modifying the training loss to improve localization, we can also augment
the data with this objective in mind by placing an object in multiple settings so that
resulting models extract the same features from an object irrespective of its location.
Instance Localization [Yang et al., 2021] leverages RoIAl... | A Cookbook of Self-Supervised Learning |
When confronted with complex and challenging mathematical problems, LLMs exhibit subpar
performance. Specifically, GPT-3 demonstrates nearly random performance, while GPT-3.5 shows
improvement, and GPT-4 performs the best [3]. Despite the advancements made in the new models,
it is important to note that the peak perfor... | ASurveyonEvaluationofLargeLanguageModels |
In Section 6.1, we have introduced techniques for reducing the number of parame-
ters in an LLM for inference acceleration. These methods are general and agnostic
to input data, i.e., static for any given input sequence. However, there is another
line of methods that aims to improve the efficiency of LLM inference withou... | Beyond Efficiency |
for RLHF. Consequently, the reward mechanism swiftly learns to assign low scores to undesirable tail-end
distribution and aligns towards the human preference. This phenomena is illustrated in Figure 20, where we
can see that the worst answers are progressively removed, shifting the distribution to the right.
In additio... | Llama2 |
We first briefly introduce the tools selected in experiments as follows:
Machine Translator. General-purpose language models may exhibit suboptimal proficiency when processing
text from multiple linguistic domains. Machine translators can effectively alleviate this issue by enabling
non-translation-dedicated language mode... | Tool Learning with Foundation Models |
3. Method
We propose IMavatar, an implicit morphable head avatar
that equips implicit surfaces with fine-grained expression
control by leveraging morphing-based deformation fields.
In this section, we first recap the deformation formulation
of the FLAME face model [35], followed by the representa-
tions for the canonical... | I M Avatar- Implicit Morphable Head Avatars from Videos |
arXiv preprint arXiv:2210.17323 (2022).
[80] Daniel Y Fu, Simran Arora, Jessica Grogan, Isys Johnson, Sabri Eyuboglu, Armin W Thomas, Benjamin Spector, Michael Poli, Atri Rudra, and Christopher
Ré. 2023. Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture. In NeurIPS.
[81] Yarin Gal, Riashat Islam, and Zou... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin,
Maarten Bosma, Gaurav Mishra, Adam Roberts,
Paul Barham, Hyung Won Chung, Charles Sutton,
Sebastian Gehrmann, et al. 2022. Palm: Scaling
language modeling with pathways. arXiv preprint
arXiv:2204.02311.
Han Dai, Yi Zhang, Ziyu Gong, Nanqing Yang, Wei Dai,
Eric Song, an... | LLM in a flash |
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza
Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hen-
nigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy,
Simon Osindero, Karen Simonyan, Erich Elsen, Jack W... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
(
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... | LLM Powered Autonomous Agents _ Lil'Log |
{(v = 0)}, g(a), {(v = 1)}, g(b), {(v = 2)} is a path in G2, but there is no path from {(v = 0)} to {(v = 3)} in G1. Hence, VDA
is neither PL↓ nor P2↓.
(6–7) For both RRAa and RRAb, if (cid:3)s, t, a(cid:4) ∈ E1, then t ∈ R2(s). Hence, RRAa and RRAb are P1↑ and, thus, PS↑ by
Theorem 21.
(8–9) Consider Example... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Training Factors
Carbon Footprint
Overview
Data Freshness
Model Details
Meta AI
Llama 2 comes in a range of parameter sizes—7B, 13B, and 70B—as well as
pretrained and fine-tuned variations.
Models input text only.
Models generate text only.
Llama 2 is an auto-regressive language model that uses an optimized transf... | Llama2 |
We note that all of the models above are entirely or partly trained on LibriSpeech.
Robust Speech Recognition via Large-Scale Weak Supervision
C. Text Standardization
Since Whisper may output any UTF-8 string rather than a restricted set of graphemes, the rules for text standardization need
to be more intricate and c... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
motivations; although misinformation is typically not designed to advance a
particular agenda, disinformation is often spread in service of concrete goals.
For instance, fake news is often designed to go viral on social media (Pennycook
and Rand 2018; Tandoc, Lim, and Ling 2018), enabling rapid transmission of
highly p... | Social_Media_and_Democracy |
To do so, we designed a self-reflection prompt that makes Mistral 7B classify a prompt or a generated
answer. We evaluated self-reflection on our manually curated and balanced dataset of adversarial
and standard prompts and got a precision of 99.4% for a recall of 95.6% (considering acceptable
prompts as positives).
Th... | Mistral7B |
2 The other major abstraction method is Hierarchical Task Networks (HTN), which originates from the Noah [79] and Nonlin [89] planners. It is based on
a hierarchy of methods that can be refined by predefined expansion patterns, and it is fundamentally different from state abstraction.
2
C. Bäckström and P. Jonsson
A... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
used in (Du et al., 2021; Chowdhery et al., 2022). We delve into understanding trade-offs between zero-shot
and finetuning performance and show that UL2 is Pareto-efficient with respect to both learning paradigms.
On one-shot summarization, UL2 triples the performance of an LM adapted T5 XXL model and is competitive
with (... | UL2- Unifying Language Learning Paradigms |
’type’: ’literal’,
’value_or_uri’: ’Raw data for polymerization and intermediate products ...’}],
’distribution_dcat’:[{’accessURL_dcat’: [{’uri’: ’http://eplca.jrc.ec.europa.eu/ELCD3/’}],
’format_dcterms’: {’uri’: ’http://publications.europa.eu/resource/authority/file-type/ZIP’},
’license_dcterms’: [{’uri’: ’http://pu... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
• Support Vector Machines (SVMs): Support Vector Machines (SVMs) are a widely adopted
class of supervised learning algorithms extensively utilized for various speech classification
tasks [504]. They are particularly effective in domains like speaker recognition [174, 509, 510]
and phoneme recognition [52]. SVMs excel i... | AReviewofDeepLearningTechniquesforSpeechProcessing |
In our study, we examine three existing models: DiffSound by Yang et al. [38], AudioGen by Kreuk
et al. [16], and AudioLDM by Liu et al. [17]. AudioGen and DiffSound use text embeddings for con-
ditional generative training, while AudioLDM employs audio embeddings to avoid potential noise
from weak textual descriptions... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
©2023 Cerebras Systems Inc. All Rights Reserved.
22
Cerebras-GPT: Open Compute-Optimal Language Models
log-likelihood (NLL) (argmini(−ln(pi)/|ci|), where pi is the model’s predicted probability of continuation
sequence ci, and |ci| is the length of that sequence). This approach will tend to favor longer continuatio... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
18
[37] H. Liu, D. Tam, M. Muqeeth, J. Mohta, T. Huang, M. Bansal, and C. A. Raffel. Few-shot
parameter-efficient fine-tuning is better and cheaper than in-context learning. Advances in
Neural Information Processing Systems, 35:1950–1965, 2022.
[38] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lew... | QLORA |
5 Attention Patterns of Memory Operations
By examining the RMT attention on specific segments, as shown in Figure 6, we observe that
memory operations correspond to particular patterns in attention. Furthermore, the high extrapolation
performance on extremely long sequences, as presented in Section 5.2, demonstrates th... | Scaling Transformer to 1M tokens and beyond with RMT |
[52] Ma, X., Zhou, C., Kong, X., He, J., Gui, L., Neubig, G., May, J., Zettlemoyer, L.:
43
Mega: Moving average equipped gated attention. In: The Eleventh International
Conference on Learning Representations (2023). https://openreview.net/forum?
id=qNLe3iq2El
[53] Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Ar... | Beyond Efficiency |
news agenda but are increasingly supplemented by platform companies serving
as secondary gatekeepers in terms of reaching a wide audience. This is a media
environment that challenges many established institutions, including news
media, gives technology companies more institutional and infrastructural
roles (and power),... | Social_Media_and_Democracy |
Coarse-to-fine interpolation Figure 9 shows interpolations between a pair of source CelebA
256 × 256 images as we vary the number of diffusion steps prior to latent space interpolation.
Increasing the number of diffusion steps destroys more structure in the source images, which the
15
model completes during the rever... | Denoising Diffusion Probabilistic Models |
[39] William E. Lorensen and Harvey E. Cline. Marching cubes:
A high resolution 3D surface construction algorithm. Inter-
national Conference on Computer Graphics and Interactive
Techniques (SIGGRAPH), 21(4):163–169, 1987. 5
[40] Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, and
Michael J. Black. SCALE: Modeling... | ICON |
The abstract embedding technique prioritizes top K re-
trieval based on document abstracts (or summaries), offering
a comprehensive understanding of the entire document con-
text. Additionally, the metadata filtering technique leverages
document metadata to enhance the filtering process. An in-
novative approach, the g... | RAG forLargeLanguageModels-ASurvey |
While huge pretrained LMs often exhibit impressive diverse zero-shot performance, the practice of
massively multi-tasking an LM via fine tuning it simultaneously on many diverse NLP tasks has
been shown to dramatically improve performance across tasks and domains. For example, Sanh et al.
(2021) and Aribandi et al. (202... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
One example is the setting of [1], where robots have to estimate the fraction of black tiles in a grid.
Each of the robots is very simple and performs a random walk. Whenever, two or more robots are
close to each other, they can communicate with each other. In the end, the robots have to agree on a
joint estimate of... | informatics-phd-projects-2022-23 |
[552] Gilbert, N., J. Doran. Simulating Societies: The Computer Simulation of Social Phenomena.
Routledge Library Editions: Artificial Intelligence. Taylor & Francis, 2018.
[553] Hamilton, J. D. A new approach to the economic analysis of nonstationary time series and the
business cycle. Econometrica: Journal of the ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
rization Across Neural Language Models,” Mar. 2023.
[60] D. Ganguli, D. Hernandez, L. Lovitt, N. DasSarma, T. Henighan, A. Jones, N. Joseph,
J. Kernion, B. Mann, A. Askell, Y. Bai, A. Chen, T. Conerly, D. Drain, N. Elhage, S. E. Showk,
S. Fort, Z. Hatfield-Dodds, S. Johnston, S. Kravec, N. Nanda, K. Ndousse, C. Olsson,... | gpt-4-system-card |
Language model pre-training has been shown to
capture a surprising amount of world knowledge,
crucial for NLP tasks such as question answering.
However, this knowledge is stored implicitly in
the parameters of a neural network, requiring ever-
larger networks to cover more facts.
To capture knowledge in a more modular ... | REALM |
tasks. We decompose the problem into two components including offline and online stages. In the
offline stage, MLCopilot canonicalizes historical data and creates an experience pool. LLMs are
then used to extract valuable knowledge from historical experience. In the online stage, MLCopilot
retrieves experiences from the ... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Empowering employees and delivery service partners
In addition to its focus on customers, Amazon strives to make every day better for its employees and delivery service partners.
For example, the company: | AMZN-Q3-2023-Earnings-Release |
22
Fig. 3: Convergent Pearson’s correlations between IPIP-NEO and BFI scores by model.
Bar chart illustrates the similarities (i.e., convergence) between IPIP-NEO and BFI score
variation for each Big Five domain. Stronger correlations indicate higher levels of convergence
and provide evidence for convergent validity.... | PersonalityTraitsinLargeLanguageModels |
Dragomir Radev, Rui Zhang, Amrit Rau, Abhinand
Sivaprasad, Chiachun Hsieh, Nazneen Fatema Ra-
jani, Xiangru Tang, Aadit Vyas, Neha Verma,
Pranav Krishna, Yangxiaokang Liu, Nadia Irwanto,
Jessica Pan, Faiaz Rahman, Ahmad Zaidi, Murori
Mutuma, Yasin Tarabar, Ankit Gupta, Tao Yu,
Yi Chern Tan, Xi Victoria Lin, Caiming Xio... | Prefix-Tuning |
the generation of unexpected, irrelevant, or coun-
terfactual output (Zhang et al., 2023c). Several
works in hallucination trace down the occurrence
of hallucination to the lack of pertinent knowledge
and the internalization of false knowledge from
the pretraining corpora (Li et al., 2022; McKenna
et al., 2023; Dziri e... | DataManagementForLargeLanguageModels-ASurvey |
Is there any social principle for llm-based agents? CoRR,
abs/2308.11136, 2023.
[658] Baum, S. A survey of artificial general intelligence projects for ethics, risk, and policy. Global
Catastrophic Risk Institute Working Paper, pages 17–1, 2017.
[659] Lecun, Y. https://twitter.com/ylecun/status/1625127902890151943.... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
§
DeepMind's Atari game system, DQN, for example, almost entirely lacks explicit
cognitive models. When DQN learned to play Breakout it did not abstract individual
board positions into scene graphs representing the location and extent of individual
bricks; there was no direct representation of where the paddle is,... | The Next Decade in AI- |
supervised learning [Tarvainen and Valpola, 2017], and even model average in supervised
and generative modeling [Jean et al., 2014]. | A Cookbook of Self-Supervised Learning |
˜fsigmoid(x) =
1
α
α to the stretched sigmoid:
fsigmoid(αx), α ∈ [0, 1].
(7)
We refer to ˜fsigmoid as the scaled-sigmoid, which is visu-
alized in Figure 6 (right). Since ˜fsigmoid can surpass the
[0, 1] bounds, we employ an annealing strategy: initializing
α with a small value (0.5 in our experiment) to accelerate... | Instant3D |
0.0
33.3 22.2 36.4
18.2 27.3 20.0 16.0
77.8 55.6 18.2 18.2 63.6 63.6 84.0 68.0
9.1
27.8 27.8 27.3
18.2 27.3 28.0 20.0
72.2 66.7 45.5 18.2 54.5 72.7 84.0 84.0
0.0
22.2 44.4
54.5 45.5 20.0 60.0
66.7 77.8 27.3 27.3 72.7 45.5 72.0 76.0
0.0
9.1
16.7 22.2 18.2 18.2 18.2 36.4 32.0 24.0
61.1 72.2 45.5 45.5 81.8 36.4 72... | Mixture-of-Experts |
To evaluate the agents, we ask crowdworkers to have dialogs with each of the two LaMDA and the two PT instances,
producing 600 dialog turns in total. In addition, we ask another set of crowdworkers to label each of the generated
responses in their original context according to whether they are role-consistent and helpf... | LaMDA- Language Models for Dialog Applications |
conclusions and steps for future research
As online hate speech has become increasingly visible on social media platforms,
it has emerged at the center of academic, legal, and policy agendas. Despite
increased attention to online hate speech, as this chapter demonstrates, the
debate over how to define online hate speec... | Social_Media_and_Democracy |
for efficient and robust semi-supervised learning.
Systems (NeurIPS), 2021c.
Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International
Conference on Learning Representations (ICLR), 2015.
14
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris A... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
3.1. Preliminaries
Aligning specific pairs of modalities. Contrastive learn-
ing [27] is a general technique for learning an embedding
space by using pairs of related examples (positives) and un-
related examples (negatives). Using pairs of aligned ob-
servations, contrastive learning can align pairs of modal-
ities su... | IMAGEBIND- One Embedding Space To Bind Them A |
Table 5: Percentage of helpful and persona-consistent messages from each agent.
Helpful % Role Consistent %
LaMDA Everest
PT Everest
LaMDA Music
PT Music
65
18
57
31
91
85
89
84
Table 6: Examples of domain-specific losses for PT responses when compared to LaMDA responses that could be
due to their different perform... | LaMDA- Language Models for Dialog Applications |
character in the NFKC-normalized string starts with M, S, or P.
Additionally, we put a space between every letter for the languages that do not use spaces to separate words, namely Chinese,
Japanese, Thai, Lao, and Burmese, effectively measuring the character error rate instead.
We note that the above is an imperfect ... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Model
Dense
Dense
Sparse
Sparse
(cid:88)
(cid:88)
84.9 ± 0.33
85.1 ± 0.25
86.6 ± 0.18
86.6 ± 0.24
GEC (↑)
22.3 ± 0.25
22.1 ± 0.42
22.2 ± 0.04
22.9 ± 0.09
Table 7: Impact of sentinel tokens for fine-tuning. The addition of sentinel tokens (a similar
concept used in Lester et al. (2021)) during fine-tuning has mixed p... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Fast Attention Calculation. In the realm of fast attention, researchers are developing innovative strategies to enhance
efficiency. A primary focus is on attention factorization, which aims to reduce attention calculations that are often unnecessary
in certain contexts. This technique is particularly useful when dealin... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
From a mathematical standpoint, let sagg(k) de-
note the cumulative use of neuron data across a
sequence of k input tokens. Our memory architec-
ture is designed to store an average of sagg(k) in
Dynamic Random-Access Memory (DRAM). As
we process each new token, the incremental neu-
ron data, which is mathematically re... | LLM in a flash |
We evaluate all models in JAX on TPU v4-8 with greedy decoding unless specified otherwise. We
normalise text using the Whisper English normaliser (Radford et al., 2022), which standardises text
by removing or converting specific words, symbols, numeric expressions, and managing whitespace
and spellings, in an attempt t... | DISTIL-WHISPER |
What are the unintended consequences of potential | Social_Media_and_Democracy |
•
Continued to expand AWS’s infrastructure footprint to support customers by launching the AWS Israel (Tel Aviv)
Region and a new AWS Local Zone in Phoenix, Arizona. The AWS Israel (Tel Aviv) Region is estimated to support
an average of 7,700 full-time equivalent jobs annually through a planned investment of $7.2 bi... | AMZN-Q3-2023-Earnings-Release |
2 Related Work
Communicative Agents. Communication between agents has been studied for a long time [44, 45].
There are many ways to facilitate communication between agents, and with agents [19, 53, 57].
Among these, natural language is considered the most natural form of communication [57]. By
enabling agents to funct... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
ertainsize(Weietal.,2022b).Inparticular,foundationmodelswithtensorhundredsofbillionsofparameterscangenerateintermediatereasoningtracesduringcomplexproblem-solving,whichsignificantlybooststheirzero-shotandfew-shotperformances(Nakanoetal.,2021;Nyeetal.,2021;Weietal.,2022b,interalia).Thereasoningabilitythatemergesinthefoun... | Tool Learning with Foundation Models |
3.2
INTRINSIC SELF-CORRECTION
Per the discussions in Section 3.1.3, since the idea that LLMs can self-correct their reasoning is
not supported by the evidence so far, we turn our focus to the results in the intrinsic self-correction
2For GSM8K, a similar random baseline might not exist, but the underlying rationale ... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
the pose, size, color etc. of those objects. Then, the MLP
φstate maps s into the language embedding space.
Vision Transformer (ViT). ViT ˜φViT (Dosovitskiy et al.,
2020) is a transformer architecture mapping an image I
into a number of token embeddings ˜x1:m = ˜φViT(I) ∈
Rmטk. We consider several variants, including ... | PaLM-E- An Embodied Multimodal Language Model |
J. Bromley, I. Guyon, Y. LeCun, E. Säckinger, and R. Shah. Signature verification using a"
siamese" time delay neural network. Advances in neural information processing systems,
6, 1993. 7, 10
47
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan,
P. Shyam, G. Sastry, A. Askell, S. Aga... | A Cookbook of Self-Supervised Learning |
Generating wikipedia by summarizing long sequences. arXiv preprint arXiv:1801.10198, 2018.
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke
Zettlemoyer, and Veselin Stoyanov. Roberta: A robustly optimized bert pretraining approach. arXiv preprint
arXiv:1907.11692, 20... | UL2- Unifying Language Learning Paradigms |
Property DLBS is the only one of the methods in Section 6 that is not transitive. The following example illustrates why.
Example 67. Let M1 be the landmarks for τ1 and M2 the landmarks for τ2. Let v be a variable in V 1 and let ϕ1 = {(v = 0)} ∈
M1 be a landmark on V 1. Then V 2 contains both v and a variable vϕ1 fo... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[202] Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. Codet5: Identifier-aware unified pre-trained encoder-
decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859 (2021).
[203] Yidong Wang, Zhuohao Yu, Jindong Wang, Qiang Heng, Hao Chen, Wei Ye, Rui Xie, Xing Xie, and Shiku... | ASurveyonEvaluationofLargeLanguageModels |
while successfully handling a wide range of diverse tasks.
We follow OFA (Wang et al., 2022b) to design BiomedGPT, which takes BART (Lewis et al., 2019) as the
backbone that is implemented as a sequence-to-sequence model with a BERT-style encoder over corrupted
text and a GPT-style left-to-right autoregressive decoder.... | BiomedGPT |
MQA, we increase the dimension of the feed-forward layers to compensate for the reduction in the attention
layers. For the MQA variant, we increase the FFN dimension by a factor of 1.33, and for the GQA variant, we
increase it by a factor of 1.3. From the results, we observe that the GQA variant performs comparably to ... | Llama2 |
AnsAug Rephrasing SV FOBAR Overall
MetaMathQA-GSM8K 80K
75K
MetaMathQA-MATH
155K
MetaMathQA
Table 2: Number of samples in the proposed MetaMathQA. | METAMATH |
interestingness, safety, and groundedness. An advantage of using several different metrics is their debuggability: by
exploring responses with low safety or groundedness scores, we have been able to develop targeted methods to improve
them. | LaMDA- Language Models for Dialog Applications |
instruction fine-tuned models; and 3) personality in LLM outputs can be shaped
along desired dimensions to mimic specific personality profiles. We also discuss
potential applications and ethical implications of our measurement and shaping
framework, especially regarding responsible use of LLMs. | PersonalityTraitsinLargeLanguageModels |
jobs
Dolly 2.0 generates content for a tweet
Instruction
Write me a tweet about the launch of Dolly 2.0, our
new LLM.
We've upgraded our LLM, making it more ef | Dolly 2 Databricks |
2 Background
In this chapter, we will introduce the definition of RAG, as
well as the comparison between RAG and other model opti-
mization techniques, such as fine-tuning.
2.1 Definition
The meaning of RAG has expanded in tandem with techno-
logical developments.
In the era of Large Language Mod-
els, the specific def... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
5.2.2 Code Translation
For generating initial Python translation, we apply the same few-shot prompt for TransCoder as [13],
which consists of 3 exemplars (Appendix B.1). From Figure 7a, we again observe that the major
improvement comes from the first debugging turn. Specifically, a single debugging turn with the
full fe... | Teaching Large Language Models to Self-Debug |
Figure 3 and Table 2 we see that NF4 improves per-
formance significantly over FP4 and Int4 and that
double quantization reduces the memory footprint
without degrading performance.
k-bit QLORA matches 16-bit full finetuning and
16-bit LoRA performance Recent findings have
established that 4-bit quantization for inferen... | QLORA |
• Navigation. Navigation permits agents to dynamically alter their positions within the environ-
ment, which often involves multi-angle and multi-object observations, as well as long-horizon
manipulations based on current exploration [23]. Before navigation, it is essential for embodied
agents to establish prior intern... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
3.3 Survey #1
In the next stage of our scale development process, we designed a Qualtrics-based online survey to collect data
from participants and conducted an exploratory factor analysis and item reduction. Boateng et al. [10], referring
to Comrey [17], recommends a sample size of a minimum of 200 participants for st... | Society’sAttitudesTowardsHumanAugmentation |
In this way, the latent space for music can serve
as the starting point for our text-to-music genera-
tor, which will be introduced next. To ensure this
representation space fits the next stage, we apply a
tanh function on the bottleneck, keeping the val-
ues in the range [−1, 1]. Note that we do not use
a more disenta... | Moûsai |
and 15 hours a week as a coach, then she works 50 x 35 = 1750 hours as a teacher and 15 x 30 = 450 hours as a
coach. So she works 1750 + 450 = 2200 hours. She gets paid 20 dollars per hour for 1750 hours and 30 dollars
per hour for 450 hours. So her annual salary is 20 x 1750 + 30 x 450 = $36,500. The answer is $36,500... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
7
1/8x1/4x1/2x1x8xDataset scale (ratio)1.01.52.02.53.03.5Chamfer distance (cm)3.3392.9682.9322.6821.762.0241.781.4791.351.0951.3361.2661.2191.1421.036PIFuPaMIRICONSMPL-XLoose ClothesBody Fitting FailureUnseen Camera (a) ICON reconstructions for in-the-wild images with extreme poses (Sec. 5.1).
(b) Avatar creation fro... | ICON |
popular journalistic narrative, that online hate speech did not increase either
over the course of Donald Trump’s 2016 campaign or in the aftermath of his
unexpected election. Using a dataset of more than 1 billion tweets, their results
are
a machine-
learning–augmented dictionary-based approach or a community-based | Social_Media_and_Democracy |
1
INTRODUCTION | DISTIL-WHISPER |
Figure 11: Impact of different projector architectures and output dimension on popular
methods.x − y − z denotes a MLP with layers of output dimension x,y ad z respectively.
From Garrido et al. [2022b].
Influence of the backbone’s output dimension. Recent works also investigated the
effect of the backbone dimension. Dubo... | A Cookbook of Self-Supervised Learning |
In this work, we focus particularly on the role of knowledge graphs in the context of Explainable Machine Learning.
Knowledge Representation has a long tradition in manipulating, creating, standardising, and publishing structured knowl-
edge. In the last two decades, efforts have been focusing toward... | Knowledge graphs as tools for explainable machine learning: A survey |
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