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7% and 21% of model training has a majority of pronouns
modified such that their grammatical gender is feminine
rather than masculine. We demonstrate that such interven-
tions are successful at reducing bias measures on a targeted
benchmark, and propose these counterfactual interventions
and retrainability of portions o... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Artificial neural models have, for some time, ex-
hibited the ability to achieve significant success on
specific tasks when trained on those tasks (Devlin
et al., 2019; Liu et al., 2019). PLMs in particu-
lar have demonstrated this even in the few-shot
setting (Hofer et al., 2018; Radford et al., 2019;
Brown et al., 20... | AreEmergentAbilitiesinLarge Language Models just In-Context |
N/A
N/A
23.5%
N/A
8
M2UGen
A PREPRINT
6 Conclusion and Future Work
This paper introduces the M2UGen model, which uti-
lizes a large language model (LLM) to achieve music un-
derstanding and multi-modal music generation within a
unified framework. Furthermore, we present a compre-
hensive methodology for generatin... | M2UGen |
Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, and Tat-Seng Chua. Next-gpt: Any-to-any multimodal LLM.
CoRR, abs/2309.05519, 2023b.
Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. Soundstream: An
end-to-end neural audio codec. IEEE ACM Trans. Audio Speech Lang. Process., 2022.
Dong ... | Qwen-Audio |
where the tags <Image>, <Response>, <Condition> and <Content X> serve as placeholders for inserting the embeddings
of visual image, the text response, the text condition, and the text option content. The condition of instruction with condition
17
OPTIONs: <Image> <Condition>ASSISTANT: Task-specific ResponsesUSER-INPU... | Let’sThinkOutsidetheBox |
We also conduct ablation experiments on the multimodal-
ity memory and retrieval methods. We set JARVIS-1 w/o
memory module as the baseline agent. We first evaluate
JARVIS-1’s performance with different memory size (rep-
resenting different learning stages) as shown in Figure 6,
which demonstrates the effectiveness of ... | JARVIS-1 |
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti,
Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones,
Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov.
Natural questions: A benchmark for... | gemini_1_report |
Parameter-Efficient Transfer Learning for NLP
Neil Houlsby 1 Andrei Giurgiu 1 * Stanisław Jastrze¸bski 2 * Bruna Morrone 1 Quentin de Laroussilhe 1
Andrea Gesmundo 1 Mona Attariyan 1 Sylvain Gelly 1
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Abstract | Parameter-Efficient Transfer Learning for NLP |
5Appendix A.2 describes example stability challenges, such as FP16 mixed precision training causing numerical underflows.
©2023 Cerebras Systems Inc. All Rights Reserved.
7
Cerebras-GPT: Open Compute-Optimal Language Models
Figure 5: Percentage loss increase relative to Cerebras-GPT scaling law plotted against trai... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
4.2 Melody evaluation | Simple and Controllable Music Generation |
# Show the plot
plt.show()
This code should create a figure with two subplots, each showing a scatterplot of "HE pass@1" vs "MBPP pass@1" for a different
subset of the data. The first subplot uses the data where "decoding" is equal to 0.1, while the second subplot uses the data where
"decoding" is equal to "greedy". Th... | CodeLlama2 |
(:init (on b5 b3) (on b4 b2) (on b2 b1) (on b3 b4) (clear b5)
(empty))
(:goal (and (on b1 b2) (on b3 b5) (on b4 b1)))
In-Context Learning
3.2
LLMs are known to be capable of in-context learning without finetuning their parameters. By in-
context learning, we mean LLMs’ ability to perform unseen downstream tasks by si... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
ety of criteria compared with existing music
generation models. Lastly, to promote the open-
source culture, we provide a collection of open-
source libraries with the hope of facilitating
future work in the field.1 | Moûsai |
Previous research suggests that not all corrections are effective in reducing
individuals’ reliance on misinformation. There are two pathways through
which misinformation might continue to shape attitudes and behaviors post-
correction: the continued influence effect and backfire effects. Engrained in the
former is the n... | Social_Media_and_Democracy |
Our method admits three potential sources of error, quanti-
fied by the following residuals:
b) − q(θ(cid:96)
d(cid:89)
b)
p(xj|θ(cid:96)
(cid:15)1 := (cid:15)1((cid:96), b) := p(θ(cid:96)
(cid:15)2 := (cid:15)2((cid:96), b, x) :=
q(xj; ψ(cid:96)
(4)
(3)
b,j)
b) − d(cid:89)
b) − d(cid:89)
j=1
j=1
j=1
(cid:15)... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Foundation of Generalization: Interface Unification. To facilitate knowledge transfer among tools, it
is critical to design a unified interface that enables the model to manipulate various tools in a consistent
and standardized manner, which serves as the foundation for generalizable tool learning. Through a unified
inter... | Tool Learning with Foundation Models |
3 Shortcomings of Free-Text Pipelines
We first analyze “faithful-by-construction” pipeline
models (I→R;R→O) for free-text rationalization
with respect to two properties: quality of gener-
ated rationales (§3.1) and appropriateness of the
sufficiency assumption (§3.2).
3.1
Joint Model Rationales are More
Indicative o... | Measuring Association Between Labels and Free-Text Rationales |
United States v. Alvarez, 276
Index
Urban study of content notice and takedown,
user characteristics, in hate speech detection,
user level liability, pros and cons to applying to
platforms, 272–273
“us vs. them,” to identify othering in hate
226–227
60, 63
speech, 60
vaccine debate, bot manipulation of, 100
V... | Social_Media_and_Democracy |
misinformation detection w.r.t. partisan leanings
and how it is propagated to language models even
further to downstream tasks. | DataManagementForLargeLanguageModels-ASurvey |
Education
High school or some college
College degree
Graduate or professional degree
Prefer not to say
Other
Disability
Hearing difficulty
Vision difficulty
Cognitive difficulty
Ambulatory (mobility) difficulty
Self-care difficulty
None
General Workers (n=115)
Select Workers (n=28)
54
60
1
94
5
14
1
1
0
29
39
27
16
2... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
embedding benchmark. ArXiv, abs/2210.07316, 2022.
[41] Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek,
Qiming Yuan, Nikolas A. Tezak, Jong Wook Kim, Chris Hallacy, Johannes Heidecke, Pranav
Shyam, Boris Power, Tyna Eloundou Nekoul, Girish Sastry, Gretchen Krueger, David P. Schnurr... | E5 |
Experiments. Hyung Won Chung, Le Hou, Shayne Longpre, Jason Wei, Yi Tay, Barret Zoph, Xuezhi Wang,
William Fedus, Yunxuan Li, Siddhartha Brahma, Adams Yu, Xinyun Chen, Shixiang Shane Gu, Sharan
Narang, Albert Webson, Adam Roberts.
Training infrastructure. Le Hou, Hyung Won Chung, Shayne Longpre, Jason Wei, Barret Zoph,... | Scaling Instruction-Finetuned Language Models |
Specifically, we found that information generated by the model is most likely to be useful for
individuals and non-state actors who do not have access to formal scientific training. The model
can provide general information on common proliferation pathways, including historical attempts
at proliferation that were success... | gpt-4-system-card |
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yun-
cong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke
Zhu, and Anima Anandkumar. Minedojo: Building open-ended
embodied agents with internet-scale knowledge. Advances in
Neural Information Processing Systems Datasets and Bench-
marks, 2022. 1, 2, 3, 8, 12, 16, ... | JARVIS-1 |
Table 3: Descriptions and examples from one task not found to be emergent (Tracking Shuffeled Objects), one task
previously found to be emergent (Logical Deductions), and one task found to be emergent only in GPT-4 (GSM8K).
A similar list of all 22 of the tasks that we use in our experiments is presented in Appendix A,... | AreEmergentAbilitiesinLarge Language Models just In-Context |
that VOYAGER is able to utilize the learned skill library in a new Minecraft world to solve novel tasks
from scratch, while other methods struggle to generalize. | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
A Review of Deep Learning Techniques for Speech Processing
101
[433] Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahremani, Vimal Manohar, Xingyu Na, Yiming Wang,
and Sanjeev Khudanpur. 2016. Purely sequence-trained neural networks for ASR based on lattice-free MMI.. In
Interspeech. 2751–2755.
[434] Ro... | AReviewofDeepLearningTechniquesforSpeechProcessing |
12
Image classification
Video classification
iNat2018
iNat2021 Places205
Arch
Feature
OpenCLIP ViT-G/14
ViT-H/14
MAE
ViT-B/8
DINO
ViT-L/16
iBOT
ViT-S/14
ViT-B/14
ViT-L/14
ViT-g/14
DINOv2
73.0
31.0
59.6
66.3
69
76.4
80.4
81.6
76.0
32.3
68.3
74.6
74.2
81.1
85.1
85.7
69.8
52.4
60.4
64.4
62.9
66.2
67.3
67.5
K400 ... | DINOv2- Learning Robust Visual Features without Supervision |
recommender systems. The use of listwise ranking is found to strike the best balance between cost
and performance. Furthermore, ChatGPT shows promise in addressing the cold-start problem and
providing interpretable recommendations. Moreover, the research by Yuan et al. [227] and Li et al.
[103] demonstrated the promisi... | ASurveyonEvaluationofLargeLanguageModels |
18.5 18.2
1.9
0.7
3.2
2.7
3.4
5.2
17.3 23.2
43.0 51.9
1.5
2.0
1.5
2.3
8.6
2.5
83.3 81.3
90.9 89.1
29.4 31.4
74.7 78.2
93.9 91.9
21
Table 4: Standard prompting versus chain of thought prompting on five commonsense reasoning
benchmarks. Chain of thought prompting is an emergent ability of model scale—it does not positi... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
In fact, the visible pixels of the facial texture by the given
camera pose are directly recoverable from the input image
via inverse rasterization of the fitted 3D mesh. Therefore,
we cast the 3D face reconstruction problem as an image
inpainting task in the UV space; i.e. the goal is to fill in
the missing pixels in a c... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
In the rapidly evolving domain of Natural Language Processing (NLP), the race towards higher model
performance often necessitates an escalation in model size. However, this scaling tends to increase
computational costs and inference latency, thereby raising barriers to deployment in practical,
real-world scenarios. In ... | Mistral7B |
5.3
6 Conclusion
7 Acknowledgements
8 Author Contributions
9 Appendix
.
.
.
.
.
.
.
.
.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.1 Model Release .
9.2
Implementation Details and UL2 code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.3 Details of Supervi... | UL2- Unifying Language Learning Paradigms |
Clinical trial recommendation Let us imagine a health care company that uses an AI system to supports cancer-diagnosed pa-
tients in finding experimental treatments (early access programs or EAPs). A patient provides the system with a description
of his medical history (relevant documents, symptoms, diagnosis, etc.), w... | Knowledge graphs as tools for explainable machine learning: A survey |
3.7.4 Visual Evaluation
Another way to evaluate what information is contained or not in a representation is to use
a decoder over the representation that is able to map back this information to pixel space.
Some methods like [He et al., 2022] are built with a specific decoder which make such visual
analysis easy, howeve... | A Cookbook of Self-Supervised Learning |
researchersconfirmedthatdemandtypesrelatetodemanddistribution
shapes[21,22].
Whiledemandforecastingcanbeconceivedasatimeseriesforecast-
ingproblem,itcanalsobeframedasasupervisedregressionlearning | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
a blank symbol representing gaps between output symbols and computes the loss function by
summing probabilities across all possible paths. The loss function encourages the model to assign
high probabilities to correct output symbols and low probabilities to incorrect output symbols
and the blank symbol, allowing the mo... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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... | Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima |
General reasoning abilities are evidenced by frontier AI producing remarkably apt responses to
novel questions, For example, PaLM’s ability to understand the humour behind jokes which
had never before been told.57
However, there is also evidence that models rely heavily on memorisation and basic heuristics:
● L... | Capabilities and risks from frontier AI |
realistic summarization tasks. Experiments demon-
strate reduced hallucination for two 13B parameter
LLMs, highlighting the effectiveness of synthetic
data for mitigating undesired behaviors. | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
In Proceedings of the AAAI conference on artificial intelligence.
[148] Ratish Puduppully and Mirella Lapata. 2021. Data-to-text generation with macro planning. Transactions of the
Association for Computational Linguistics (2021).
[149] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutske... | SurveyofHallucinationinNatural Language Generation |
Collobert, R., Weston, J., Bottou, L., Karlen, M.,
Kavukcuoglu, K., and Kuksa, P. Natural language pro-
cessing (almost) from scratch. Journal of machine learn-
ing research, 12(ARTICLE):2493–2537, 2011.
Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V.,
Dalmia, S., Riesa, J., Rivera, C., and Bapna, A. Fleurs:
... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Diffusion models have been proven to be highly effective in various machine learning tasks related
to computer vision, as well as speech-processing tasks. The recent development of DiffSep [482] for
speech separation, which is based on score-matching of a stochastic differential equation, has shown
competitive performa... | AReviewofDeepLearningTechniquesforSpeechProcessing |
transparency,
Overall, the track record of corporate transparency measures for promoting
good governance has been mixed. Across multiple domains, from development
projects to the private sector,
it has been said that “actual evidence on
transparency’s impacts on accountability is not as strong as one might expect”
(Fo... | Social_Media_and_Democracy |
3
Large Language Models Cannot Self-Correct Reasoning Yet
Table 1: Results of GPT-3.5 and GPT-4 on reasoning benchmarks with the setting in Section 3.1.1.
GSM8K CommonSenseQA HotpotQA
GPT-3.5
GPT-4
Standard Prompting
Self-Correct (Oracle)
Standard Prompting
Self-Correct (Oracle)
75.9
84.3
95.5
97.5
75.8
89.7
8... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
sustainable way.
• They empower individuals to self-organise and commit to being fair, transparent and accountable
about the data and resources these contribute. | informatics-phd-projects-2022-23 |
Stable Audio: Fast Timing-Conditioned Latent Audio Diffusion — Stability AI
https://stability.ai/research/stable-audio-efficient-timing-latent-diffusion
4/5 | Stable Audio_ Fast Timing-Conditioned Latent Audio Diffusion — Stability AI |
If we allow ourselves to dream multiple decades out, then it’s easy to imagine a future where Generative AI is deeply embedded in how we work, create and play: memos that write themselves; 3D print anything you can imagine; go from text to Pixar film; Roblox-like gaming experiences that
generate rich worlds as quickly a... | Generative AI A Creative New World Sequoia Capital |
2
Figure 3: RMT inference scales linearly with respect to the input sequence length. We estimate the
required FLOP increase for the forward pass compared to running models on sequences with 512 tokens. a:
lengths from 512 to 32,000 tokens. b: lengths from 32,000 to 2,048,000 tokens. The RMT segment length is
fixed at ... | Scaling Transformer to 1M tokens and beyond with RMT |
As would be expected, scaling up the number of model parameters or the size of the dataset greatly
improves model performance (see Figure A6 for scaling with dataset size). However, even when only
10 samples can be submitted, scaling up the total number of samples leads to massive improvements
in model solve rate.
Figu... | alphacode |
Early successes like Wikipedia did not generalize into a broader principle that
crowds could effectively and reliably filter for truth and against falsity.71
Regardless of its causal impact on voting behavior and political perceptions,
the 2016 US presidential election cycle demonstrated in the very least that
concerted... | Social_Media_and_Democracy |
The force required to accelerate a 10 kg object at 5 m/s2 can be calculated using the formula:
F = m * a
where F is the force, m is the mass of the object, and a is the acceleration.
So, the force required to accelerate a 10 kg object at 5 m/s2 would be:
F = 10 kg * 5 m/s2 = 50 N
When the weight is 2 kg, the force requ... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
[10] Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, and Kyunghyun
Cho. SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine.
arXiv:1704.05179 [cs], April 2017. URL http://arxiv.org/abs/1704.05179. arXiv:
1704.05179.
[11] Angela Fan, Mike Lewis, and Yann Dauphin. Hierarchical ... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
6 . 4
P E R P L E X I T Y W I T H L O N G C O N T E X T S | StarCoder_paper (1) |
framework tailored for structured pruning of LLMs offering task-agnostic compression
and efficient data usage. LLM-Pruner integrates a dependency detection mechanism
to identify interconnected structures in the model. It utilizes an effective importance
estimation approach, combining both first-order data and estimated Hess... | Beyond Efficiency |
for PCs that take advantage of their ability to efficiently compute arbitrary marginal probabilities.
Specifically, we first show which kinds of marginal probabilities are required for (de)compression.
The proposed algorithm combines an inference algorithm that computes these marginals efficiently
given a learned PC and So... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
ered emergent from prior literature.
Our observation that only two out of 14
previously-emergent tasks displayed emergence,
and the fact that one of these tasks represents for-
mal linguistic abilities and the other represents
memorisation, casts doubt on claims that emer-
gent tasks indicate LLM reasoning abilities. ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Table 17: Examples of correct and incorrect chains of thought produced by LaMDA 137B on Date
Understanding. | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Lila and Tom are playing with their toys in the living room. Lila has a smooth doll with long hair
and a pink dress. Tom has a horn that makes a loud noise when he blows it. Lila likes to comb her
doll’s hair and make her look pretty. Tom likes to make his horn sound and scare Lila.
”Tom, stop it!” Lila says. ”Your hor... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
25.0 50.0 24.4 29.3 14.3 14.3 20.0 20.0 28.1 34.4 31.8 13.6
50.0 37.5 56.1 73.2
28.6 50.0 70.0 71.9 71.9 18.2 36.4
50.0 56.2 48.8 75.6 42.9 42.9 40.0 50.0 71.9 75.0 36.4 36.4
50.0 50.0 56.1 75.6 50.0 42.9 40.0 50.0 71.9 65.6 40.9 40.9
7.1
80M T5-Small
Flan-T5-Small
18.2 18.2 18.2
36.4
25.0
54.5 27.3 26.9 30.8 16.7... | Mixture-of-Experts |
12
Weidinger, Iason Gabriel, William S. Isaac, Edward Lockhart, Simon Osindero, Laura Rimell,
Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray
Kavukcuoglu, and Geoffrey Irving. Scaling language models: Methods, analysis & insights from
training gopher. arXiv Preprint, 202... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
horizon text instructions generated by LLM. Therefore, it is
worth exploring methods for generating plans that are easier
for the controller to execute or improving the controller’s
ability to follow instructions.
4.3. Ablation Studies
4.3.1. JARVIS-1 BASED ON DIFFERENT LMS
We conducted ablation experiments on vario... | JARVIS-1 |
rich? Where there are particular concerns about the availability of material or the sensitivity of the
topic you must clearly demonstrate the feasibility of the project.
Third, you should describe how you intend to analyse your research materials. Will you be using
statistical analysi... | Writing a DPhil Research Proposal |
A Priority Map for Vision-and-Language Navigation
with Trajectory Plans and Feature-Location Cues
Jason Armitage
University of Zurich
Switzerland
Leonardo Impett
University of Cambridge
UK
Rico Sennrich
University of Zurich
Switzerland
jason.armitage@uzh.ch
li222@cam.ac.uk
sennrich@cl.uzh.ch
Abstract | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
[263] Solaiman, I., C. Dennison. Process for adapting language models to society (palms) with
values-targeted datasets. Advances in Neural Information Processing Systems, 34:5861–5873,
2021.
[264] Bach, S. H., V. Sanh, Z. X. Yong, et al. Promptsource: An integrated development environment
and repository for natural la... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Figure 2: Trade-off between NFE and different metrics of interest.
audio, the shorter audio is used as the prompt. Results are shown in Figure 3. As expected, WER
mildly decreases and SIM-r grows quickly and flattens with longer audio prompts. Comparing against
VALL-E, Voicebox is more efficient at leveraging an audio... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
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e... | An overview of Bard- an early experiment with generative AI |
Are Interventions Within the CDA 230 Framework Sufficient?
As discussed in “Part II: How Does CDA 230 Shape Efforts to Combat Online
Political Disinformation?,” CDA 230 does not function as a categorical block to
potential
challenges of political
disinformation. Its impact is considerably more specific: It limits interve... | Social_Media_and_Democracy |
on a corpus that covers both biomedical articles and clinical notes, with the goal of building a unified and
comprehensive model. However, it has been reported that models pre-trained on clinical notes can perform
poorly on language tasks based on biomedical articles, and vice versa (Gu et al., 2021; Alsentzer et al., ... | BiomedGPT |
3.1 Fact Memorization
The first task tests the ability of RMT to write and store information in memory for an extended time
(Figure 4, top). In the simplest case, the fact is always located at the beginning of the input, and
the question is always at the end. The amount of irrelevant text between the question and answe... | Scaling Transformer to 1M tokens and beyond with RMT |
vision-based robotic manipulation. CoRR, abs/1806.10293, 2018.
[362] Nguyen, H., H. M. La. Review of deep reinforcement learning for robot manipulation. In
3rd IEEE International Conference on Robotic Computing, IRC 2019, Naples, Italy, February
25-27, 2019, pages 590–595. IEEE, 2019.
[363] Dasgupta, I., C. Kaeser-Ch... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
We express our gratitude to Jinze Bai, Shuai Bai, Peng Wang, Sinan Tan, Shijie Wang for their insightful
discussion. We would like to thank Juan Zhu, Junyang Lin, Siqi Zheng, Jiaming Wang and Zhihao Du for
their support of this project.
6 Acknowledgements
References
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, ... | Qwen-Audio |
LONDON’S GLOBAL UNIVERSITY
UCL Academic Manual
2022-23
Chapter 1: Student Recruitment
and Admissions Framework
Chapter 1 is UCL’s regulatory framework for the recruitment and admission of students to UCL. | UCL Academic Manual |
References
Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W.,
O’Donnell, T. J., Berenberg, D., Fisk, I., Zanichelli, N.,
Zhang, B., et al. Openfold: Retraining alphafold2 yields
new insights into its learning mechanisms and capacity
for generalization. bioRxiv, 2022.
Andonian, A., Anthony, Q., Biderman, S., B... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
249
Medium. (2015). Medium’s 2015 Transparency Report. Medium report. https://blog
.medium.com/medium-s-2015-transparency-report-5c6205c48afe
Meleagrou-Hitchens, A., & Kaderbhai, N. (2017). Research Perspectives on Online
Radicalisation: A Literature Review, 2006–2016. VOX-Pol report. www.voxpol
.eu/new-vox-pol-repo... | Social_Media_and_Democracy |
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi,
Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible
critical care database. Scientific data, 3(1):1–9, 2016.
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin ... | BiomedGPT |
5 Experiments
We conduct extensive experiments to answer the following three questions:
1. How well does LLM-AS-P work? That is, to what extent can LLMs be directly used for
planning? (Not at all)
2. How well does LLM+P work compared to LLM-AS-P? (Much better)
3. What role does the context play in the success of LL... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Please do not hesitate to contact us if you have any questions.
We are glad to meet you!
Please note our information on data protection in the application process at https://www.tu-clausthal.de/universitaet/karriere-ausbildung/stellenangebote/hinweise-zum-daten‐
schutz-im-bewerbungsverfahren (https://www.tu-clausthal... | _2 Doctoral Researcher (m_w_d) in the field of Large Language Models (LLM) for Software Engineering_ - Technische Universität Clausthal - DAAD |
SQL: SELECT COUNT(DISTINCT status) FROM city
The execution of the SQL query above would return a table with 1 column. The
first column, "COUNT(DISTINCT status)" would contain the number of
different statuses of cities. So the SQL query returns a table with 1 column
, the number of different statuses of cities.
Feedba... | Teaching Large Language Models to Self-Debug |
Figure 4. Template. The models in the center are the predefined
template mesh with landmarks. It can be seen that we refine the
structure on specific regions, where a complex nose or tail may ex-
ist. The colored regions and delineated lines denote the landmarks.
These landmarks represent specific components of the cha... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
(cid:19)
(cid:125)
(cid:124)
πref(y | x) exp
=π(y|x), using Thm. 1 reparam.
r(x, y)
β
= 1,
(9)
i.e., π(y | x) is a valid distribution (probabilities are positive and sum to 1). However, following
Eq. 4, we can see that Eq. 9 is the partition function of the optimal policy induced by the reward
function r(x, y). ... | Direct Preference Optimization |
likely than liberals to engage in selective exposure, biased information
processing, and ideological conformity (Lau and Redlawsk 2006; Garrett
2009b; Nyhan and Reifler 2010; Nam, Jost, and Van Bavel 2013; Guess
et al. 2019), although other work has found symmetric patterns regarding
these behaviors (Munro et al. 2002; ... | Social_Media_and_Democracy |
is evaluated via accuracy and F1-score (or F1 macro-score
for multiclass problems), as well as wall time. FORGE
fares well in this experiment, attaining the top accuracy
and F1-score in three out of five tasks. On a fourth, the
highly imbalanced credit dataset, the only models that
do better in terms of accuracy receive... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Proceedings of the 13th International Conference, KR 2012, Rome, Italy, 2012, pp. 446–456.
[7] C. Bäckström, P. Jonsson, Bridging the gap between refinement and heuristics in abstraction, in: Proceedings of the 23rd International Joint Conference
on Artificial Intelligence, IJCAI 2013, Beijing, China, 2013, pp. 2261–226... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
REGISTERED MODELS AND ML PRODUCTION
Production models have undergone the experimentation
phase and are then deployed in real-world applications. They
are typically used to make predictions or decisions based on
new data. Registering a model is the process of recording and
storing metadata about a trained model in a... | 2023 state of ai databrick |
Network testing. When testing, our network only re-
quires an RGB image as input and outputs both the para-
metric model and the reconstructed surface with texture.
To maximize the performance, We run the body reference
optimization step for all results unless otherwise stated.
Fifty iterations are needed for the optim... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Each row in the matrix represents the concate-
5
051015202530Windowsize01020304050PercentageAggregatedUsageIncrementalTransfersagg(k)sagg(k+1)−sagg(k)000000000000000000000000000000Neurons to be deletedNew NeuronsNeurons from initial windowActive neurons in the initial windowActive neurons in the new windowInitial Win... | LLM in a flash |
For dialog uses, we surprisingly find that dialog-prompting alone is more effective than control tokens at reducing toxic
generation. This holds true even on the standard dataset, which aims to measure explicit forms of toxicity that are more
closely align with the tagging method from pre-training using signals from the... | PaLM 2 Technical Report |
Q: Alice, Bob, and Claire are holding a white elephant gift exchange. At the start of the event, they are each holding a
present of a different color: Alice has a pink ball, Bob has a yellow present, and Claire has a black ball. As the event
progresses, pairs of people swap gifts. First, Bob and Alice swap their gifts.... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
InformationFusion81(2022)91–102100 J.M. Rožanec et al.
[22] D.Lengu,A.A.Syntetos,M.Z.Babai,Sparepartsmanagement:Linkingdistribu-
tionalassumptionstodemandclassification,EuropeanJ.Oper.Res.235(2014)
624–635.
[23] R. Saluja, A. Malhi, S. Knapič, K. Främling, C. Cavdar, Towards a rigorous
evaluation of explainability for ... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
z = [zT, zAd , zAs, zN] ∈ R64×64×12
(7)
Samples from our diffusion model (after being decoded
through each D) can be seen in the left part of Fig. 1.
3.3. Inference
We use the aforementioned trained diffusion model to
perform inpainting on both the texture and reflectance UV
(6)
4
Denoise + MCG correction stepTe... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
Figure 8: Expert Parallelism as described in Gshard paper
Data scientists have deployed multiple replicas of Expert Parallel distribution, known as Expert Parallel Replica,
to increase training throughput when larger number of GPUs are available. Under this strategy, like traditional
data parallel training, experts fr... | Scaling Speech, Language and Vision Models with Mixture of Experts Technique - Microsoft Community Hub |
in this comic mistakenly took the dinosaur sculpture in theamusement park for a real dinosaur. Nervously, he shouted, "Help! Thedinosaur is coming!" However, in the next panel, we see a staff membercalmly responding, "Don't panic, it's fake." > LLaVA-1.5: 一个男人站在船上，指着坐在椅子上的女人。@ A manis standing on a boat and pointing at... | Let’sThinkOutsidetheBox |
Meta-learning has the potential to improve speech processing tasks by learning better learning
algorithms that can adapt to new tasks and data more efficiently. Meta-learning can also reduce
the cost of model training and fine-tuning, which is particularly useful for low-resource speech
processing tasks. Further invest... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Hella-
Swag
0.518
0.535
0.292
0.320
0.415
0.458
0.505
0.524
0.270
0.293
0.333
0.376
0.398
0.451
0.482
0.505
0.273
0.294
0.341
0.387
0.403
0.466
0.488
0.516
0.268
0.274
0.291
0.325
0.386
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0.295
0.334
0.388
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0.640
0.661
0.503
0.523
0.595
0.610
0.654
0.651
0.491
0.519
0.530
0.545
0... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M.,
Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V.,
et al. Opt: Open pre-trained transformer language models.
arXiv preprint arXiv:2205.01068, 2022.
11 | Self-Extend LLM |
[142] Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller.
2019. Language Models as Knowledge Bases?. In Proceedings of the 2019 Conference on Empirical Methods in Natural
Language Processing and the 9th International Joint Conference on Natural Language Proc... | SurveyofHallucinationinNatural Language Generation |
On the importance of joint text-to-image and text-to-video training While there are some text-
video datasets, text-image datasets dominate the internet in terms of quality and quantity [34]. Con-
sequently, there is simply not enough video data available to cover all the concepts present in text-
image datasets. For e... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
Huge pretrained language models (LMs) have demonstrated surprisingly good
zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing
vision of a single, versatile model with a wide range of functionalities across dis-
parate applications. However, current leading techniques for leveraging a “fr... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
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Self-Alignment with Instruction Backtranslation
Xian Li
Ping Yu Chunting Zhou Timo Schick
Luke Zettlemoyer Omer Levy
Jason Weston Mike Lewis
Meta AI
Abstract | Self-AlignmentwithInstructionBacktranslation |
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