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As we think through possible data-sharing paradigms, it is important to begin
with an understanding of the fact that prohibiting the sharing of social media
data for analysis by the scholarly community – or any researchers who are
committed to sharing findings in the public domain – does not mean that social
media data ... | Social_Media_and_Democracy |
6 Discussion
6.1 Credit Assignment
One clear advantage of process supervision is that it provides more precise
feedback than outcome supervision. A reward model trained with outcome
supervision faces a difficult credit-assignment task — to generalize well, it must
determine where an incorrect solution went wrong. Thi... | Let’s Verify Step by Step |
Consider that MIM is fundamentally a generative modeling task. Such models are
trained to generate missing image parts conditional on the observed ones. Note that BEiT,
MAE, and SimMIM are deployed on downstream prediction problems by removing the
decoder and replacing it with a prediction head. However, masked image m... | A Cookbook of Self-Supervised Learning |
async function mineTenCobbledDeepslateBelowY0 ( bot ) {
// Equip the iron pickaxe
const ironPickaxe = bot . inventory . findInventoryItem ( mcData .
itemsByName [" iron_pickaxe " ]. id );
await bot . equip ( ironPickaxe , " hand ");
// Find cobbled_deepslate blocks below Y =0
const cobbledDeepslateBlocks = await ex... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
and could make code generation models more reliable in real-world out-of-distribution applications.
Bias, fairness, and representation. Similar to natural language models (Brown et al., 2020), code
generation models are prone to reproducing the deficiencies and biases of their training data. When
trained on diverse corp... | alphacode |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Democratic Creative Destruction?
149 | Social_Media_and_Democracy |
et al., 2013). More recently, these datasets have
been used to assess the performance of QA systems
in the open domain setting where no evidence docu-
ments or database is given. In this setup, TriviaQA
contains 79k train examples and WebQuestions
3.1k. Most approaches rely on a text corpus at test
time, extracting ans... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Preprint. Under review.
that are tuned by backpropagating gradients through
the quantized weights.
Table 1: Elo ratings for a competition between
models, averaged for 10,000 random initial order-
ings. The winner of a match is determined by
GPT-4 which declares which response is better for
a given prompt of the the ... | QLORA |
appears after the API call but not before it. While
during data generation the model can look ahead
to generate API calls, this is not possible at infer-
ence time, so we want to dissuade the model from
calling the API in such cases. | Toolformer |
[73] Mingxing Tan and Quoc Le. EfficientNetV2: Smaller Mod-
els and Faster Training. In ICML, 2021.
[74] Matt Trumble, Andrew Gilbert, Charles Malleson, Adrian
Hilton, and John Collomosse. Total capture: 3D human pose
estimation fusing video and inertial sensors. In BMVC, 2017.
[75] Shuhei Tsuchida, Satoru Fukayama, M... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
Let’s start with a few definitions and clarifications.
At a high-level, we want the impact of AI on the world to be good, just, fair, and so forth—or at least,
not actively/catastrophically bad. Call this the challenge of “making AI go well.”51 This is a very
broad and complex challenge, much of which lies well outside t... | Is Power-Seeking AI an Existential Risk? |
[25] Wen Jiang, Nikos Kolotouros, Georgios Pavlakos, Xiaowei
Zhou, and Kostas Daniilidis. Coherent reconstruction of
multiple humans from a single image. In Computer Vision
and Pattern Recognition (CVPR), pages 5578–5587, 2020. 3
[26] Hanbyul Joo, Tomas Simon, and Yaser Sheikh. Total capture:
A 3D deformation model for... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
4.2.4 Multilingual Question Answering
We evaluate Toolformer and all baseline models
on MLQA (Lewis et al., 2019), a multilingual
question-answering benchmark. A context para-
graph for each question is provided in English,
while the question can be in Arabic, German, Span-
ish, Hindi, Vietnamese, or Simplified Chinese.... | Toolformer |
A closely related question is whether studies funded by one of the platforms,
but not carried out by researchers who are employees of the platforms, would
suffer from the same concerns. Clearly, if funding from a platform came with a
right of prepublication approval by the platform (or any funder for that matter),
it w... | Social_Media_and_Democracy |
=1 since no plan
for P2 can achieve a negative goal on a variable that is set to true at some point in the plan. Keeping negative preconditions
=1 and post(g(a)) = post(a)
=1 for all a ∈ A1.
is the identity function.
f
20
C. Bäckström and P. Jonsson
Artificial Intelligence 302 (2022) 103608
G2:
g(b)
g(a), g... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
}
void checkoutBranch () {
ifstream checkoutFile ;
checkoutFile . open ( currentBranchPath () + "/" + currentBranch + ". cpp ");
if ( checkoutFile ) {
cout << " Success !" << endl ;
} else {
cout << " Error : Unable to checkout file ." << endl ;
}
}
};
int main () {
VersionControl vc ;
vc . checkOut (" new_bran... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Tong Zhou, Yubo Chen, Pengfei Cao, Kang Liu, Jun
Zhao, and Shengping Liu. 2023c. Oasis: Data cura-
tion and assessment system for pretraining of large
language models. arXiv preprint arXiv:2311.12537.
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and
Mohamed Elhoseiny. 2023. Minigpt-4: Enhancing
vision-language unders... | DataManagementForLargeLanguageModels-ASurvey |
in natural
2.1.2 Large Language Models (LLMs)
Pre-trained Language Models (PLMs) constitute a type of neural network that has
been trained on extensive collections of text data. Their purpose is to acquire knowl-
edge of linguistic patterns, structures, and semantics inherent in the language. In the
context of LLMs, ... | Beyond Efficiency |
confirmation bias, 170, 179
conflict detection, in processing corrections to
misinformation, 177
connective use of bots, for democratic good or
control, 96
Consent of the Networked (McKinnon), 238
conservatism, predictors of, 180
conservatives. see liberals vs. conservatives
conspiracy theories, 10, 166, 182
consumer... | Social_Media_and_Democracy |
L(t) =
log P (tj|tj−k, . . . , tj−1; Θ)
Where Θ is the parameters of the captioner that are to be optimized. To turn this language model into a
captioner, you need only to condition on the image. The challenge here is that images are composed of many
thousands of pixel values. Conditioning on all of this information ... | Improving Image Generation with Better Captions |
American actors American actresses
Pretrained models
Falcon 7B
MPT 7B
StarCoder (Python) 15.5B
Llama 2 7B
Llama 2 13B
Llama 2 34B
Code Llama 7B
Code Llama 13B
Code Llama 34B
Instruct (aligned)
Falcon-instruct 7B
MPT-instruct 7B
Llama 2 Chat 7B
Llama 2 Chat 13B
Llama 2 Chat 34B
Code Llama - Instruct 7B
Code Llama - Ins... | CodeLlama2 |
§
As we saw with the Romeo and Juliet example in Section 4.3, CYC is immensely better
off inasmuch as it can (at least to some non-trivial degree) reason over cognitive models
(eg its list of time points and facts about characters and locales known at various time
points, excerpted in Figure 2) in association with... | The Next Decade in AI- |
Wanjun Zhong, Jingjing Xu, Duyu Tang, Zenan
Xu, Nan Duan, Ming Zhou, Jiahai Wang, and
Jian Yin. 2020. Reasoning over semantic-level
graph for fact checking. In Proceedings of the
58th Annual Meeting of the Association for
Computational Linguistics, pages 6170–6180,
Online. Association for Computational Lin-
guistics. h... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
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... | PhD Fellow in Explainable Natural Language Understanding |
multiple classes to each image. We make one modification to the original evaluation protocol: we do not
backpropagate gradients to the backbone and keep it frozen. We compare our model to SEERv2 in Table 13.
Our model often classifies images of all groups as Human without large deviations across skin tones. Neither
SEERv... | DINOv2- Learning Robust Visual Features without Supervision |
False refusal with context distillation
It’s not accurate or respectful to make assumptions
about someone’s food preferences based on their race
or ethnicity. People have diverse tastes and cultural
backgrounds, and it’s important to respect and ap-
preciate those differences.
It’s possible that people may have had pos... | Llama2 |
Reward Maximization Training such as [7, 31] amortize the cost of DOODL from runtime to training. They train by
generating images from text prompts, computing a reward loss on the images, and backpropagating gradients through the
generative process to improve the loss. While effective in the open-vocabulary setting (al... | DiffusionModelAlignmentUsing Direct Preference Optimization |
4. Applicants for undergraduate part-time study should confirm their acceptance of an offer from
UCL in writing directly to Admissions in Student & Registry Services.
Further guidance
i) Part time study applicants confirm or decline their acceptance of an offer of a place to
study at UCL by completing the Off... | UCL Academic Manual |
1. Introduction
Over the past several years, large transformer models have
established themselves as the premier methodology for gen-
erative tasks in natural language processing (Brown et al.,
2020; Sanh et al., 2021; Chowdhery et al., 2022). Beyond
NLP, transformers have also made big splashes as genera-
tive models ... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
RM score:
➤ Prompt: Tell me how I can rip-off my customers by selling them cars that don’t run.
Response:
I cannot provide advice or support on illegal or unethical activities. It is not appropriate or
legal to rip off customers by selling them cars that do not run. It is important to be honest
and transparent in your... | Llama2 |
or waste – for producing electricity and the DTs for the various types of power plants will be very
different). Hence, building DTs by enumerating the physical system properties can be difficult to
replicate (e.g., even across different but related entities as exemplified before) and scale (e.g., from
simpler to lar... | informatics-phd-projects-2022-23 |
with Table 6. We note that the Vicuna benchmark favors open-source models while the larger OA
benchmark favors ChatGPT. Furthermore, we can see from Tables 5 and 6 that the suitability of
a finetuning dataset is a determining factor in performance. Finetuning Llama models on FLAN
v2 does particularly well on MMLU, but ... | QLORA |
ectmorelabeledtrainingvideodatasetsandapplysomecontinual/incrementallearn-ingtechniquessuchas[4–7]totrainourLFDM.Finally,inourexperiments(Table6),wenoticedthat10-stepDDIMcanachieveacceptablegenerationperformancewithfastersamplingspeed,suggestingitmayhavegreaterpotentialwithbetterhyperparametersettings.Toexploretheseset... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
The presence [of] social bots in online political discussion can create three tangible issues:
first, influence can be redistributed across suspicious accounts that may be operated with
malicious purposes; second, the political conversation can become further polarized;
third, the spreading of misinformation and unverifie... | Social_Media_and_Democracy |
Another study has taken a different approach by aim-
ing to reduce the number of documents in order to im-
prove the accuracy of the model’s answers.
In the study
by [Ma et al., 2023b],
they propose the “Filter-Reranker”
paradigm, which combines the strengths of LLMs and Small
Language Models (SLMs). In this paradigm, ... | RAG forLargeLanguageModels-ASurvey |
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75
[485] Zhou, X., W. Y. Wang. Mojitalk: Generating emotional responses at scale. In I. Gurevych,
Y. Miyao, eds., Proceedings of the 56th Annual Meeting of the Association for Computational
Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pages
1128–1137. Association for Computati... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Bots and Computational Propaganda
91
bots
in artificial
In part due to the political concerns detailed in the Introduction to this chapter,
but also because of broader interest
intelligence (AI) and
automation, scholars and public... | Social_Media_and_Democracy |
We show in this section that the analytical gradient w.r.t.
position of hash encoding suffers from localities. Therefore,
optimization updates only propagate to local hash grids,
lacking non-local smoothness. We propose a simple fix to
such a locality problem by using numerical gradients. An
overview is shown in Fig. 2... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
Therefore, we may need to begin thinking about updating our concept of the
public’s right to data in the context of these information monopolies. This right
should supersede the proprietary right of companies to enjoy exclusive access to
the digital trace data created by users of their products at some point when those... | Social_Media_and_Democracy |
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WizardLM: Empowering Large Language Models to
Follow Complex Instructions
Can Xu1∗ Qingfeng Sun1∗ Kai Zheng1∗ Xiubo Geng1
Jiazhan Feng2† Chongyang Tao1 Daxin Jiang1‡
Pu Zhao1
{caxu,qins,zhengkai,xigeng,puzhao,chongyang.tao,djian... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
part 1: the disinformation challenge
“Fake news” has become a commonplace term for characterizing the prevalence
of false or inaccurate stories circulating online, considered a symptom of the
poor state of information quality throughout media and society generally.
These stories were widely distributed during the 2016... | Social_Media_and_Democracy |
Woohyun Han implemented block sparsity.
Milen Ferev implemented the tflite conversion colab.
Zhonglin Han contributed Pax integration example (WIP).
Hong-Seok Kim provided guidance and helped with the writing.
Yann Dauphin implemented CraM and SAM examples.
Karolina Dziugaite helped with direction and writing of th... | JAXPRUNER |
[53] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang,
Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body:
Implicit neural representations with structured latent codes
In Proceed-
for novel view synthesis of dynamic humans.
ings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition, pages 9054–906... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
Creativity of AI. Beyond the coding ability, other emergent abilities (Wei et al., 2022b) also shed light on the
possibility of more advanced tool creation. However, whether foundation models can exhibit genuine creativity
in creating novel tools remains an open problem. This issue is important because the capacity for... | Tool Learning with Foundation Models |
38.8
38.5
40.2
30.6
30.2
31.7
16.9
16.9
17.4
7.5
7.3
7.8
33.8
33.6
35.1
19.1
19.0
19.8
62.7
62.6
62.8
58.7
58.6
58.3
Table 4: Accuracy on the TruthfulQA benchmark. Since the answer is in free-form, the standard
self-consistency is not applicable. USC overall has the highest truthfulness and informativeness over
the... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
30.3 24.2 23.5 44.1 17.1 45.7 16.1 22.6 23.5
23.5 23.5 17.1 11.4 32.3 12.9 23.5
24.0 27.0 33.3
17.6 29.4 20.0
Flan-U-PaLM
Flan-PaLM
90.9
90.9
90.9
90.9
31.0
29.0
22.6
29.0
29.0
23.5
32.3
1.2
8.6
9.1
7.9
0.0
0.0
6.5
0.0
0.0
7.9
1.2
9.7
540B PaLM
540B U-PaLM
3B
11B
8B
62B
62B
39
Table ... | Scaling Instruction-Finetuned Language Models |
To delve into the exploration of instruction com-
plexity, Zhao et al. (2023a) propose Tree-Instruct
to controllably enhance the complexity of instruc-
tion data. It treats the instruction as a semantic tree
and constructs new complex instructions by adding
nodes to the tree. Thus, the complexity of instruc-
tion can b... | DataManagementForLargeLanguageModels-ASurvey |
Fried, D., Aghajanyan, A., Lin, J., Wang, S., Wallace, E.,
Shi, F., Zhong, R., Yih, W.-t., Zettlemoyer, L., and Lewis,
M. InCoder: A generative model for code infilling and
synthesis. Computing Research Repository, 2022. doi:
10.48550/arXiv.2204.05999. URL https://arxiv.
org/abs/2204.05999v2. Version 2.
Gao, L. On the ... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
thought prompting elicits reasoning in large language models. In Proceedings of NeurIPS.
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y. (2019). HellaSwag: Can a machine
really finish your sentence? In Proceedings of the ACL.
Zhang, B. and Sennrich, R. (2019). Root mean square layer normalization. In ... | TinyLlama |
17, we see another example of the model being able to generate working code and follow complex
user instructions. Finally, Figure 21 shows an example of Gemini Ultra’s capability of understanding
video by reasoning over temporally connected set of frames. | gemini_1_report |
Towards Automated Circuit Discovery for Mechanistic Interpretability, Conmy et al., 2023;
Progress measures for grokking via mechanistic interpretability, Chan et al., 2023;
A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations, Chughtai et al., 2023;
Decomposing Language Models I... | Capabilities and risks from frontier AI |
0.0
57.6
FLAN-SwitchBASE
780M SwitchLARGE
0.0
FLAN-SwitchLARGE 27.6
A.3 Reasoning
The four reasoning tasks are held-in, which means we perform instruction finetuning on the training
set while evaluating on the “validation” set in a few-shot way. The detailed performance is presented
here.
22
Table 12: Reasoning... | Mixture-of-Experts |
Figure 15: Maximum Rouge2 score (fmeasure) similarity between the 100 generated stories for each model. Here
original model means the ones generated by GPT-3.5.
For the sake of getting a more concrete impression about how different the model completions are from the
original ending of the story and from other stories ... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
2.5 ArXiv
ArXiv is a preprint server for research papers that
has operated since 1991. As shown in fig. 10, arXiv
papers are predominantly in the fields of Math,
Computer Science, and Physics. We included arXiv
in the hopes that it will be a source of high qual-
ity text and math knowledge, and benefit potential
downstrea... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
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... | Principal-agent VCG contracts - ScienceDirect |
The focus of this article is on abstraction in action planning and combinatorial search within AI. Abstraction has a long
history even if we restrict ourselves in this way; its use dates back to the Abstrips planner [78] and even to the first version
* Corresponding authors.
1 The work of C. Bäckström was partially s... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[652] Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. 2019. Graph convolutional networks: a comprehensive
review. Computational Social Networks 6, 1 (2019), 1–23.
[653] Xingxuan Zhang, Feng Cheng, and Shilin Wang. 2019. Spatio-temporal fusion based convolutional sequence learning
for lip reading. In Procee... | AReviewofDeepLearningTechniquesforSpeechProcessing |
LLMs can also be used for quality assessment on some NLG tasks, such as summarization and translation. On
summarization tasks, GPT-4 as an evaluator achieves a higher correlation with humans than other methods with a
large margin [64]. Some other evaluators based on LLMs [34, 50, 64, 108] also show good human alignment... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
l24gesturesofonesubjectfromNATOPSdataset.1Thesevideosarealsoavailableinhttps://github.com/nihaomiao/CVPR23_LFDM.•newdomain.mp4showsthesynthesizedvideoclipsincluding4expressionsoffoursubjectsfromFace-Forensicsdataset.“Original”meansdirectlyapplyingourLFDMpretrainedonMUGdataset.“Finetuned”meansthattheimagedecoderisfinetun... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Social Media, Echo Chambers, and Political Polarization
47
shows through a series of survey studies and lab experiments, social media
usage (and the type of partisan content that is shared on Facebook) increases
the perceived differences between individuals’ own position and where they
perceive the outgroup to be, ma... | Social_Media_and_Democracy |
4.5 DISCUSSION FROM A DIVERSITY PERSPECTIVE | METAMATH |
score is obtained for landscapes. This is consistent with the
finding that pictures with people tend to be more memo-
rable than natural landscapes, presented by Isola et al. [43].
Because nude paintings and portraits have the highest aver-
age memorability score, while landscape and marina paint-
ings have the lowest s... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
microsoft/guidance.
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena
Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In FAT, pp. 220–229.
ACM, 2019.
MosaicML. Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable L... | CodeLlama2 |
CREATE TABLE shipment_items (
shipment_id number ,
order_item_id number ,
primary key ( ) ,
foreign key ( shipment_id ) references shipments ( shipment_id ) ,
foreign key ( order_item_id ) references order_items ( order_item_id )
)
insert into shipment_items (shipment_id, order_item_id) values (11, 12) ;
Translate the... | Teaching Large Language Models to Self-Debug |
D := argmaxθ
(cid:80)N
(cid:91)
γn :=
γm ∩ supp(n).
m∈pa(n)
Intuitively, if an assignment x is in the context of unit n, then there exists a path on the PC’s DAG
from n to the root unit nr such that for any unit m in the path, we have x∈ supp(m). Circuit flow
extends the notation of context to indicate whether a s... | Tractable Regularization of Probabilistic Circuits |
started heads up, so after an odd number of flips, it will be tails up. So the answer is no.
Q: A coin is heads up. Inga does not flip the coin. Elanor does not flip the coin. Is the coin still heads up?
A: The coin was flipped by no one. So the coin was flipped 0 times. The coin started heads up, and it was not
flipped, so ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
5.2 LAMA
LAMA (Petroni et al., 2019) contains cloze tasks
from three different knowledge base sources, and
one QA dataset. LAMA aims to probe the knowl-
edge contained in a language model, with a fo-
cus on the type of knowledge that has traditionally
been manually encoded in knowledge bases. As
a zero-shot probing tas... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Cutler, D. R., Edwards Jr., T. C., Beard, K. H., Cutler,
A., Hess, K. T., Gibson, J., and Lawler, J. J. (2007).
Random forests for classification in ecology. Ecology,
88(11):2783–2792.
Dang, M., Vergari, A., and Van den Broeck, G. (2022).
Strudel: A fast and accurate learner of structured-
decomposable probabilistic ci... | Adversarial Random Forests for Density Estimation and Generative Modeling |
large language models have potential for generating feedback messages to critique and refine their
outputs for some natural language and reasoning domains [50, 35, 28, 36, 3], prior works suggest that
such large language models are not yet capable of correcting code when lacking external feedback,
such as unit tests or ... | Teaching Large Language Models to Self-Debug |
Solving complicated AI tasks with different domains and modalities is a key step
toward advanced artificial intelligence. While there are abundant AI models avail-
able for different domains and modalities, they cannot handle complicated AI tasks.
Considering large language models (LLMs) have exhibited exceptional abili... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
(a) Pixel space nearest neighbors
21
Figure 16: LSUN Church generated samples. FID=7.89
22
Figure 17: LSUN Bedroom generated samples, large model. FID=4.90
23
Figure 18: LSUN Bedroom generated samples, small model. FID=6.36
24
Figure 19: LSUN Cat generated samples. FID=19.75
25 | Denoising Diffusion Probabilistic Models |
21.50
6.73
2.19
19.35
10.55
0.57
11.03
1.00
6.09
4.45
0.32
0.83
1.69
86.31
290.5
38.4
19.8
113.8
88.0
2.9
64.0
10.7
43.6
11.6
2.8
4.1
24.9
715.1
Appendix Table A1 | Composition of our GitHub pre-training dataset. Python 2 and 3 are distin-
guished by whether the code can be successfully parsed using Python 3’s parser... | alphacode |
Large language models (LLMs) power a rapidly increasing number of applications, having reached a proficiency
in natural language that allows them to be commanded and prompted to perform a variety of tasks (OpenAI,
2023; Touvron et al., 2023b). By utilizing large, in-domain datasets, their efficacy can be greatly improv... | CodeLlama2 |
[30] S. Dhar, V. Ordonez, and T. L. Berg, ‘‘High level describable attributes
for predicting aesthetics and interestingness,’’ in Proc. 24th IEEE Conf.
Comput. Vis. Pattern Recognit. (CVPR), Colorado Springs, CO, USA,
Jun. 2011, pp. 1657–1664.
[31] N. Murray, L. Marchesotti, and F. Perronnin, ‘‘AVA: A large-scale data... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
74
Figure 33: Gender agreement translating out of English, across languages evaluated
While there are exceptions, for both PaLM and PaLM 2, we observe a broad relationship between translation quality
and percentage of pre-training data from web documents in that language. Most languages that are represented in over
... | PaLM 2 Technical Report |
and ObtainDiamondAxe
3. Multi-task Agent with Memory-Augmented
MLM
This section details the architecture of the proposed
JARVIS-1 agent. We begin with an overview of the mod-
ular agent design in Section 3.1. Next, we elaborate on
how to implement an interactive planning scheme with a
multimodal language model, whic... | JARVIS-1 |
STGs; the only difference is that a state may have more than one outgoing arc with the same label, which is not prohibited.
Other examples of languages for planning and search are PDDL [71] and PSVN [55].
Since there is a one-to-one correspondence between SAS+ frames and STGs, it is straightforward to say that a trans... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
τ2 is DLBS. It follows that V 1 ∪ V M3 = V 1 ∪ V M1 ∪ V M2 = V 2 ∪ V M2 = V 3.
(3) We have postM1(a) = {(vϕ = 1) | post(a) ∩ ϕ (cid:7)= ∅ and ϕ ∈ M1} and post(g1(a)) = post(a) ∪ postM1(a) for all a ∈ A1
since τ1 is DLBS. Since also τ2 is DLBS we further get that postM2(g1(a)) = {(vϕ = 1) | post(g1(a)) ∩ ϕ (cid... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Sparsity is an active research area for achieving better efficiency in deep learning. However, utilizing
sparsity and realizing its potential in real life requires a closer collaboration between hardware,
software and algorithms research. Such collaborations often require a flexible library to enable
rapid prototyping of... | JAXPRUNER |
Single-Line Infilling for Python, Java, and JavaScript Fried et al. (2022) present a single-line
fill-in-the-middle task for Python that masks one line of code from a HumanEval solution and scores
21
Model
StarCoderBase
StarCoderBase
InCoder-6B
InCoder-6B
Format
Completion
Insertion
Completion
Insertion
Completion ... | StarCoder_paper (1) |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Maintaining a high goodput3 at this scale would have been impossible using the conventional
approach of periodic checkpointing of weights to persistent cluster storage. For Gemini, we instead
made use of redundant in-memory copies of the model state, and on any unplanned hardware failures,
we rapidly recover directly f... | gemini_1_report |
to be practical solutions in addressing the challenges posed by these large models. Recent
research [280, 422, 593] has demonstrated the effectiveness of model compression, highlight-
ing the sparsity that exists within these models, particularly for specific tasks. By employing
model compression techniques, researcher... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and
Kristina Toutanova. 2019. BERT: Pre-training of deep
bidirectional transformers for language understanding.
In Proceedings of the 2019 Conference of the North
American Chapter of the Association for Computational
Linguistics: Human Language Technologies, Volume 1
(Long and ... | Moûsai |
sha1_base64="YX137MIq8yNr4LLnvGCMgoYJ0TI=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0mKUI8FLx4r2g9pQ9lsN+3SzSbsToQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IJHCoOt+O4WNza3tneJuaW//4PCofHzSNnGqGW+xWMa6G1DDpVC8hQIl7yaa0yiQvBNMbuZ+54lrI2L1gNOE+xEdKREKRtFK9zioDcoVt+ouQNaJl5MK5GgOyl/9YczSiCtkkhrT89wE/YxqFEzyWamfGp5QNqEj3rNU0YgbP1ucOiMXVhmSM... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Additional requirements for entry to courses of Initial
Teacher Education
1. Applicants undertaking any course of initial teacher education must meet the Secretary of
State’s requirements for physical and mental fitness to teach. This will be assessed by an (or
UCL’s) Occupational Health Provider through complet... | UCL Academic Manual |
**A Language Agent for Autonomous Driving**Role: You are the brain of an autonomous vehicle (a.k.a. ego-vehicle). In this step, you need to first determine notable objectsand identify their potential effects on your driving route, and then derive a high-level driving plan.Context:-Coordinates: X-axis is perpendicular, ... | ALanguageAgentforAutonomousDriving |
3
2
0
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a
M
6
]
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a
Figure 1: PaLM-E is a single general-purpose multimodal language model for embodied reasoning tasks, visual-language tasks,
and language tasks. PaLM-E transfers knowledge from visual-language domains into embodied reasoning – from ro... | PaLM-E- An Embodied Multimodal Language Model |
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. Universal and transferable adversarial
attacks on aligned language models. arXiv preprint arXiv:2307.15043, 2023.
13
Large Language Models Cannot Self-Correct Reasoning Yet
A PROMPTS AND EXAMPLE OUTPUTS
Can you solve the following math problem? Christina i... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
Alexander Matt Turner, Aseem Saxena, and Prasad Tadepalli. Formalizing the problem of side effect
regularization. In NeurIPS ML Safety Workshop, 2022.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez,
Lukasz Kaiser, and Illia Polosukhin. Attention is all you need.
In Isabelle Gu... | Tool Learning with Foundation Models |
1
Introduction
Fact verification systems typically comprise an
evidence retrieval model followed by a textual en-
tailment classifier (Thorne et al., 2018b). Recent
high-performing fact verification systems (Zhong
et al., 2020; Ye et al., 2020) use neural models
for textual entailment whose reasoning is opaque
to hum... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
Huachuan Qiu, Shuai Zhang, Anqi Li, Hongliang He, and Zhenzhong Lan. Latent jailbreak: A
benchmark for evaluating text safety and output robustness of large language models. arXiv
preprint arXiv:2307.08487, 2023.
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al.
Improving language
understanding ... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
[246] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan
Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 2023. Judging LLM-as-a-judge with
MT-Bench and Chatbot Arena. arXiv:2306.05685 [cs.CL]
[247] Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Y... | ASurveyonEvaluationofLargeLanguageModels |
[2] R. Anil, A. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey,
Z. Chen, E. Chu, J. Clark, L. Shafey, Y. Huang, K. Meier-Hellstern, G. Mishra, E. Moreira,
M. Omernick, K. Robinson, S. Ruder, Y. Tay, K. Xiao, Y. Xu, Y. Zhang, G. Abrego, J. Ahn,
J. Austin, P. Barham, J. Botha, J. Brad... | METAMATH |
5 Evaluation Results
In this section, we will evaluate the model generation quality in different settings including single
modality generation, multi-condition generation, and multi-output joint generation. We provide both
quantitative benchmarking on evaluation datasets as well as qualitative visualization demonstrat... | Any-to-Any Generation via Composable Diffusion |
52
THE NEXT DECADE IN AI / GARY MARCUS
write this essay at all. But, maybe, just maybe there's enough already out there that if
we squint, and look at all the pieces around us, we might be able to imagine what the
elephant might look like, if we were to put it all together.
A few thoughts:
• Deep learn... | The Next Decade in AI- |
,
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| Language models can explain neurons in language models |
1 [components]
2
3 [components.ner.model]
4 @architectures = "spacy.TransitionBasedParser.v2"
5 state_type = "ner"
6 extra_state_tokens = false
7 hidden_width = 64
8 maxout_pieces = 2
9 use_upper = true
10 nO = null
11
12 [components.ner.model.tok2vec]
13 @architectures = "spacy.Tok2VecListener.v1"
14 width = ${compone... | MULTI HASH EMBEDDINGS IN SPACY |
3.4 Emergent Social Behaviors
By interacting with each other, generative agents in Smallville
exchange information, form new relationships, and coordinate joint
activities. Extending prior work [79], these social behaviors are
emergent rather than pre-programmed.
Information Diffusion. As agents notice each other, the... | Generative Agents- Interactive Simulacra of Human Behavior |
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