text stringlengths 1 1k ⌀ | title stringclasses 230
values |
|---|---|
Furthermore, processing fluency is not solely a function of repetition
(Schwarz et al. 2007). On the whole, information that is easier to process will
be perceived as more familiar (and therefore more valid). Consequently,
corrections may be more successful when they are less cognitively taxing. For
example, visual corr... | Social_Media_and_Democracy |
an epistemological lens as true/false statements, or as logical inferences from other beliefs, or as biochemical processes53 –
remains more difficult to study, though there has indeed been recent work in linking neural language models to human cognitive
processes.54 | Language models trained on media diets can predict public opinion |
PC units that conform to vtree node v. Additionally, we define ϕsum(p, v) and ϕprod(p, v) as the set
of sum and product units in ϕ(p, v), respectively. | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
8 | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Short-term speculation is also a zero-sum game, with sophisticated traders
extracting value from newcomers and possibly burning them forever. A free
market admits all kinds of participants, and there’s nothing per se wrong with
short-term traders as long as they behave legally and ethically. But if we view
crypto adopt... | The Casino on Mars |
7. Discussion and Conclusion
We have presented Gemini, a new family of models that advance multimodal model capabilities
in text, code, image, audio, and video. This technical report evaluates the capabilities of Gemini
on a diverse set of widely-studied benchmarks, and our most capable model Gemini Ultra makes
signifi... | gemini_1_report |
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhari-
wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal,
Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M.
Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse,... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
To illustrate why sufficiency might not be justi-
fied for reasoning tasks, consider the example in
Figure 2. The task of the R→O model is to select
between the answer choices “have fun”, “tasty”,
and “indigestion” given the rationale “Usually a
hamburger with friends indicates a good time”. The
rationale is designed t... | Measuring Association Between Labels and Free-Text Rationales |
Figure 4: Sample efficiency of 64 experts for 50 languages
Improving accuracy and efficiency of a Vision Foundation Model using MoE
A foundation model in Vision is trained from broad data at scale and is capable of being adapted (e.g. fine-
tuned) to a wide range of downstream tasks. Foundation models become promising... | Scaling Speech, Language and Vision Models with Mixture of Experts Technique - Microsoft Community Hub |
Denoising Diffusion Probabilistic Models
Jonathan Ho
UC Berkeley
Ajay Jain
UC Berkeley
Pieter Abbeel
UC Berkeley
jonathanho@berkeley.edu
ajayj@berkeley.edu
pabbeel@cs.berkeley.edu
Abstract
We present high quality image synthesis results using diffusion probabilistic models,
a class of latent variable models ins... | Denoising Diffusion Probabilistic Models |
• Give an introduction of yourself.
Hello, my name is Klaus Mueller. I’m 20 years old and a
student at Oak Hill College, studying sociology. I’m passion-
ate about social justice and exploring different perspectives.
Currently, I’m researching the effects of gentrification in
low-income communities for a research pape... | Generative Agents- Interactive Simulacra of Human Behavior |
The predictive power of the media diets holds and is robust (1) even when demographic information of each subpopulation
is included, (2) across mediums (online, TV, radio), and (3) to the specific phrasing of the prompts. Media diets are typically
correlated with subpopulation demographics. Despite this, we find in our r... | Language models trained on media diets can predict public opinion |
• Systematic categorization and taxonomy of techniques by resource
type: We established a systematic categorization and taxonomy of resource-
efficient LLM techniques, organized primarily by the type of resource(s) they
optimize. This taxonomy simplifies the process of identifying and selecting appro-
priate methods based... | Beyond Efficiency |
𝑆𝐻𝐴𝑃𝐸 =
𝑤𝑖𝑡ℎ 𝜇𝑆𝑇 = 𝑚𝑒𝑎𝑛(𝑆1 + 𝑆3 + 𝑆4𝑅 + 𝑆5 + 𝑆6 + 𝑆7𝑅),
𝑎𝑛𝑑 𝜇𝐴𝐺 = 𝑚𝑒𝑎𝑛(𝑆8 + 𝑆9 + 𝑆10 + 𝑆13𝑅)
2
8 CONCLUSION
We present a measure for assessing attitudes toward augmented humans. The SHAPE scale presented high
internal consistency, reliability and high Concurrent, Convergent and D... | Society’sAttitudesTowardsHumanAugmentation |
chunks are met. The Higher layers can be further skipped using the early-exit crite-
rion. Short-Cutting Transformer [171] suggests a linear transformation-based method
to cast intermediate representations as final representations, thus bypassing the trans-
former computation in between. Short-Cutting Transformer adapts... | Beyond Efficiency |
linear time. Several enhancements, such as those proposed in [79], have been proposed in recent
years to improve alignment in TTS models. Additionally, in [21], the authors introduced a generic
alignment learning framework that can be easily extended to various neural TTS models. | AReviewofDeepLearningTechniquesforSpeechProcessing |
ries
While the common paradigms for evaluation of language models usually rely on structured evaluation datasets in
the form of a task where the output of the model has to match a given answer, we introduce a new paradigm that is
arguably more suitable in this context. Again, we take advantage of existing large langua... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
rather than simply reconstructing the input, we apply SpecAugment Park et al. [2019] to encoder
input at both phases. It has been shown to effectively improve the generalization capabilities of the
encoder by augmenting the input data. As SpecAugment masks input over time and frequency axes,
the first auto-encoding pha... | Translatotron3 |
Worldline and the ML Group of ULB (2013). Credit card
fraud detection data. license: Open database.
Wu, K., Zhang, K., Fan, W., Edwards, A., and Yu, P. S.
(2014). RS-Forest: A rapid density estimator for stream-
ing anomaly detection. In 2014 IEEE International Con-
ference on Data Mining, pages 600–609.
Xu, L., Sko... | Adversarial Random Forests for Density Estimation and Generative Modeling |
Symmetric FEVER As shown in Table 5,
ProoFVer shows better robustness with a mean
accuracy of 81.70% on the Symmetric FEVER test
dataset, an improvement of 13.21% over Coref-
BERT, the next best model. All models improve
their accuracy and are comparable on the test set
when we fine-tune them on its development set.
Ho... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
Fast Fourier Transform (FFT) and hash representations. These techniques model attention in a manner that aligns well with
hardware capabilities, making them more efficient for practical applications [63, 255, 343]. They filter out near-zero attentions
and focus computational efforts on the most significant ones for the... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
2
ModelExecutorCodeProblemExplanationUnit testsStep 1: Code generationStep 3: Code explanationStep 2: Code executionFeedback Few-shot prompting. Few-shot prompting aims to instruct the language model to solve a task with
several input-output demonstrations [4]. Taking text-to-SQL generation as an example, the few-shot... | Teaching Large Language Models to Self-Debug |
Commonsense and Symbolic Reasoning Our approach significantly outperforms Manual-CoT,
Random-CoT and Auto-CoT in both commonsense and symbolic reasoning. Both two reasoning tasks
differ from arithmetic reasoning, requiring a deep understanding of the problem-solving paradigm.
Since the weak bootstrapping method can enco... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Ramakrishna Vedantam, C. Lawrence Zitnick, and
Devi Parikh. 2015. Cider: Consensus-based image
description evaluation. In CVPR, pages 4566–4575.
IEEE Computer Society.
4592 Thomas Wolf, Lysandre Debut, Victor Sanh, Julien
Chaumond, Clement Delangue, Anthony Moi, Pier-
ric Cistac, Tim Rault, R´emi Louf, Morgan Funtow-
... | Prefix-Tuning |
26
0255075100Safety Data Pct. (%)0.5750.6000.6250.6500.6750.7000.7250.7500.775Mean Reward Model ScoreSafetyHelpfulnessSafety Data Pct. 0%Safety Data Pct. 1%Safety Data Pct. 10%Safety Data Pct. 25%Safety Data Pct. 50%0.00.20.40.60.81.0Safety Reward Model ScoreSafety Data Pct. 100% Generic Preprompt
The following is a ... | Llama2 |
A particularly compelling example of French industrial policy relating to the
media is Minitel, the “[p]rofoundly French” internet platform (Mailland and
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Media Regulation in the United States and Europe
207 | Social_Media_and_Democracy |
a
l
n
a
t
u
r
e
o
f
t
h
e
t
r
a
n
s
i
t
i
o
n
t
o
p
o
i
n
t
t
o
A
I
’
s
f
a
i
l
u
r
e
.
I
n
s
t
e
a
d
,
w
e
l
o
o
k
f
o
r
w
a
r
d
t
o
a
t
r
a
n
s
i
t
i
o
n
t
h
a
t
w
i
l
l
l
i
k
e
l
y
o
c
c
u
r
o
v
e
r
1
0
t
o
2
0
y
e
a
r
s
,
i
n
a
f
a
s
h
i
o
n
t
h
a
t
a
l
l
o
... | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
REPORT | SEP 1, 2021
The Internet and the Pandemic
SHORT READ | DEC 15, 2020
TOPICS
Social Media
Emerging Technology
Online Privacy & Security
Privacy Rights
Biotech
Political Issues
Misinformation
Social Media & the News
Misinformation Online
Tech Companies
Artificial Intelligence
Technology Policy Issu... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
E. Kharitonov, D. Vincent, Z. Borsos, R. Marinier, S. Girgin, O. Pietquin, M. Sharifi, M. Tagliasacchi,
and N. Zeghidour. Speak, read and prompt: High-fidelity text-to-speech with minimal supervision,
2023.
K. Kilgour, M. Zuluaga, D. Roblek, and M. Sharifi. Fréchet audio distance: A reference-free metric
for evaluati... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
(main text). Next, it mixes the top-2 candidates with the ground truth responses, and selects the top-1 as the final response. | Let’sThinkOutsidetheBox |
opportunities for many people, around the popular, demotic, demos- are real
too, and unease with specific political outcomes, potentially disastrous as they
may be in other ways, should not lead people to jump to conclusions about
whether current changes in our media environment are fundamentally
antidemocratic. We may ... | Social_Media_and_Democracy |
This leverages the observed localization of factual
knowledge in specific transformer layers. Conse-
quently, DoLa enhances the identification of factual
knowledge and minimizes the generation of incor-
rect facts. Across various tasks, including multiple-
choice and open-ended generation tasks like Truth-
fulQA, DoLa ... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
FinGPT: Open-Source Financial Large Language Models
Hongyang (Bruce) Yang1, Xiao-Yang Liu1, Christina Dan Wang2
1Columbia University; 2New York University (Shanghai)
{HY2500, XL2427}@columbia.edu; christina.wang@nyu.edu
3
2
0
2
n
u
J
9
.
]
T
S
n
i
f
-
q
[
1
v
1
3
0
6
0
.
6
0
3
2
:
v
i
X
r
a
Abs... | FinGPT-Open-SourceFinancialLargeLanguageModels |
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya
Sutskever. Robust Speech Recognition via Large-Scale Weak Supervision. arXiv e-prints, art.
arXiv:2212.04356, December 2022. doi: 10.48550/arXiv.2212.04356.
Steve Renals, Thomas Hain, and Herve Bourlard. Recognition and understanding of m... | DISTIL-WHISPER |
ST-MoE-32B has “only” 269B parameters and is approximately FLOP-matched to a dense Trans-
former with 32B parameters. The reduced parameter count from Switch-C and Switch-XXL eases
the burden for both serving and fine-tuning. Finally, we use the sparse-dense stacking described in
Appendix C.
We pre-train for 1.5T tokens... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
15 | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
As illustrated in Figure 5, to circumvent these challenges,
contemporary research has proposed methods for refining the
retrieval process:
iterative retrieval, recursive retrieval and
adaptive retrieval. Iterative retrieval allows the model to en-
gage in multiple retrieval cycles, enhancing the depth and
relevance of ... | RAG forLargeLanguageModels-ASurvey |
Giorgos Tolias, Ronan Sicre, and Hervé Jégou. Particular object retrieval with integral max-pooling of cnn
activations. arXiv preprint arXiv:1511.05879, 2015.
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient
learners for self-supervised video pre-training. arXiv prepri... | DINOv2- Learning Robust Visual Features without Supervision |
exact spending by sponsor. Second, one can only search ads purchased as of
May 2018, so the many ads shown during the primary election season, not to
mention the 2016 election cycle, are not available. | Social_Media_and_Democracy |
Benefiting from the huge progress of deep learning tech-
niques, recent studies have tried to address these challenges
using learning-based methods [1], [2], [3], [4], [5], [6], [7],
[8]. According to their 3D representations, these methods
can be roughly classified into two categories: parametric
methods and non-paramet... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
age validation loss across all training domains or on
a specific unseen domain. The final generalization
objective is accessed by a gradient-based general-
ization estimation function measuring the contri-
bution of each domain to other domains. Then,
domains contributing higher to learning other do-
mains will receive... | DataManagementForLargeLanguageModels-ASurvey |
∗
C = arg max
θ
θC
E
i )∈D
(qi,a∗
i,t | xi,t,Hi,t, qi),
∗
pθC (a
(3)
t=0
20
Tool SetEnv & HumanPlanningFeedbackExecutionLearning From DemonstrationsControllerFoundationModelTool SetHuman/ModelAnnotationTrainingDemonstrationLearning From FeedbackControllerFoundationModel 3.3 Training Models for Improved Tool Lea... | Tool Learning with Foundation Models |
Figure 8. Share of industry employment by relative exposure to automation by AI.
Taken from The Potentially Large Effects of Artificial Intelligence on Economic Growth,
Goldman Sachs, 2023.
Bias, Fairness and Representational Harms
Frontier AI models can contain and magnify biases ingrained in the data they are tr... | Capabilities and risks from frontier AI |
26.0
62.9
91.9
66.8
95.5
65.1
49.6
50.7
35.9
33.7
61.9
Table 1: The average test F1 scores of performing ID-PT on the 7B-parameter frozen J1-Large and of
the 11B-parameter fine-tuned T0++. The breakdown of the test scores per dataset is given in Table 7
in the appendix.
Figure 3: The average F1 score on the developmen... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
2022), a 6.4 TB dataset of permissively licensed source code in 384 programming languages, and
included 54 GB of GitHub issues and repository-level metadata in the v1.2 version of the dataset. The
Stack comes with “Am I in The Stack”, a governance tool for developers to check whether their source
code is part of the da... | StarCoder_paper (1) |
Post-processing Before applying the best PII detection model to the full dataset, we observed a
couple of frequent detection errors. We added the following post-processing techniques to reduce the
number of false positives:
11https://hf.co/bigcode/deberta-v3-large-pii-ner,
https://hf.co/StanfordAIMI/stanford-
deiden... | StarCoder_paper (1) |
3.2.2 Style Injection
Surprisingly, the model with only the cross-attention mech-
anism completely fails, yielding a meaningless output as
shown in Figure 3. Upon closer inspection, we find this
is mainly due to that a single text input can be associated
with multiple plausible 3D objects. However, the cross-
attentio... | Instant3D |
PALMS Prompt → Why do people threaten to bomb public places?
RLHF Response → I really shouldn’t speculate about the motives of terrorists, I’m just an automated assistant. But
from what I understand, threats of bombings are basically tantamount to extreme psychological
warfare and fear-mongering. The intent is to creat... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
2.3 Object Detection and Similarity Retrieval | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
How to identify funding sources
Investigating potential funding sources and preparing
postgraduate research applications is a lengthy process,
so you should allow plenty of time. It is not unreasonable
to start approximately one year before your proposed
start date.
The University of Edinburgh’s Scholarships ... | research proposal guidance |
e r r
query := fmt . S p r i n t f ( "SELECT ∗ FROM users WHERE username = ’%s ’ LIMIT
e r r = db . QueryRow( query ) . Scan(&user . ID , &user . Username ,
}
}
}
}
func hash_md5( text
s t r i n g )
hasher := md5.New()
hasher . Write ( [ ] byte ( text ) )
return base64 . URLEncoding . EncodeToString ( hasher .S... | gpt-4-system-card |
[5] Michiel A. Bakker, Martin J. Chadwick, Hannah R. Shea-
han, Michael Henry Tessler, Lucy Campbell-Gillingham, Jan
Balaguer, Nat McAleese, Amelia Glaese, John Aslanides,
Matthew M. Botvinick, and Christopher Summerfield. Fine-
tuning language models to find agreement among humans
with diverse preferences. Neural Info... | DiffusionModelAlignmentUsing Direct Preference Optimization |
3That is, the sum of the per-timestep KL-divergences.
8
0.250.500.751.00Sampling temperature0.10.20.30.40.50.6Win rateAnthropic-HH Dialogue Win Rate vs ChosenDPOBest of 128Preferred-FTPythia-2.8B030060090012001500180021002400270030003300Fine-tuning step0.300.350.400.450.500.550.600.650.70Win rateDialogue Win Rate Evo... | Direct Preference Optimization |
Results Table 3 shows results on two open do-
main QA datasets. Entity prediction methods,
RELIC and EAE, significantly outperform nearest
neighbor baselines, showing that model general-
izes beyond train / development overlap and entity
representations contains information about answer
entities. No-EAE and RELIC both e... | Entities as Experts- Sparse Memory Access with Entity Supervision |
8 Discussion
We will discuss several favorable properties of
prefix-tuning and some open problems.
Personalization. As we note in §1, prefix-tuning
is advantageous when there are a large number
of tasks that needs to be trained independently.
One practical setting is user privacy (Shokri and
Shmatikov, 2015; McMahan et ... | Prefix-Tuning |
by 3.
Response 1: A number is divisible by 3 if the sum of its digits is divisible by 3. Since "ab5" has a
fixed digit of 5, the sum of "a" and "b" must be divisible by 3 for the number to be divisible by 3.
There are 10 possible digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9.
We can consider the cases where the sum of "a" a... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
Language Instruction
0.0622
0.1136
0.1134
0.0441
0.0986
0.092
0.1053
692
88
97
68
71
728
79
Dig down to mine diamond and craft diamond_pickaxe.
Dig down to mine diamond and craft diamond_shovel.
Dig down to mine diamond and craft diamond_sword.
Dig down to mine diamond and craft diamond_hoe.
Dig down to mine diamond... | JARVIS-1 |
Explanation: The user has asked me to do two tasks, I will do one and then the other.
Plan: 1. find(rice chips), 2. pick(rice chips), 3. find(bowl), 4. put(rice chips), 5. find(tea), 6. pick(tea), 7.
find(table), 8. put(tea), 9. done().
Human: How would you throw away a redbull?
Explanation: The user has asked me to throw ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
[6] AMIRSHAHI, S. A., HAYN-LEICHSENRING, G. U., DENZLER, J., AND REDIES, C. Jenaesthetics subjective
dataset: analyzing paintings by subjective scores. In European Conference on Computer Vision (2014), Springer,
pp. 3–19.
[7] BAR, Y., LEVY, N., AND WOLF, L. Classification of artistic styles using binarized features der... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
arXiv:1805.04833, 2018.
https://riffusion. com/about.
S Forsgren and H Martiros. Riffusion-stable diffusion for real-time music generation. 2022. URL
Flavio Schneider, Zhijing Jin, and Bernhard Schölkopf. Mo\ˆ usai: Text-to-music generation with
long-context latent diffusion. arXiv preprint arXiv:2301.11757, 2023.
... | Simple and Controllable Music Generation |
39
Competition-Level Code Generation with AlphaCode
interactive problem false positives.
Competitive programming problems also often include time and memory limits, and we use these
limits when executing submissions.
A.3. Evaluation metrics
As described in Section 2.2, we use the 𝑛@𝑘 solve rate to evaluate model ... | alphacode |
4 Experiments
We set T = 1000 for all experiments so that the number of neural network evaluations needed
during sampling matches previous work [53, 55]. We set the forward process variances to constants
increasing linearly from β1 = 10−4 to βT = 0.02. These constants were chosen to be small
relative to data scaled to... | Denoising Diffusion Probabilistic Models |
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. Millicent does not flip the coin. Conception flips the coin. Is the coin still heads up?
A: The coin was flipped by Conception. So the coin was flipped 1 time, which is an odd number. The coin
started heads up, so aft... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
• Value-based DRL: Given the state of the environment (𝑠), a value function 𝑄 : 𝑆 × 𝐴 → R is
learned to estimate overall future reward 𝑄(𝑠, 𝑎) should an action 𝑎 be taken. This value
function is parameterized with deep networks like CNN, Transformers, etc.
• Policy-based DRL: As opposed to value-based RL, polic... | AReviewofDeepLearningTechniquesforSpeechProcessing |
7
Table 2: Effect on the objective evaluation metrics with a varying number of inference steps and
classifier-free guidance.
Model
Guidance
TANGO
3
100
Varying Steps
Steps
10
20
50
100
200
FD ↓
45.12
31.38
25.33
26.13
24.52
KL ↓
1.66
1.39
1.27
1.37
1.37
FAD ↓
11.38
4.52
2.13
1.87
1.59
Varying Guidance
Step... | Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model |
19
US average of 0.385 kg CO2eq/KWh. We use the same formula as in Patterson et al. (2021) to estimate the
potential energy consumption and the carbon emission. For the power consumption of an A100-80GB, we
take the thermal design power for NVLink systems, which is 400W. We report the potential carbon emission
of ret... | DINOv2- Learning Robust Visual Features without Supervision |
A.2 Other Models
BERT (Devlin et al., 2018) is a transformer, pre-
trained using masked language modelling. We re-
port results for BERT-base, which has 110m param-
eters, and BERT-large, which has 340m parameters.
The transformer architecture used by BERT-base
is identical to the 12 transformer layers in EAE.
BERT-lar... | Entities as Experts- Sparse Memory Access with Entity Supervision |
In this work, we present Code Llama, a family of LLMs for code generation and infilling derived from
Llama 2 (Touvron et al., 2023b) and released under the same custom permissive license. We provide inference
code for both completion and infilling models in the accompanying repository.1 Our approach is based on
gradual... | CodeLlama2 |
30
(a) IPIP-NEO Shaping using prompts
(b) IPIP-NEO Relevance to Generated Text | PersonalityTraitsinLargeLanguageModels |
text-to-speech synthesis, music synthesis, and more, it can also lead to harmful applications like
deepfakes. Care should be taken to avoid these applications. One possibility is to add watermarking
and/or train a classifier that can detect whether or not the codec is applied, in order to enable the
detection of synthe... | RVQGAN |
6This categorization is not intended to represent an optimal, hierarchical taxonomy, though we recognize that
saying this doesn’t prevent it from valorizing some perspectives and framings.[23] Nor are these categories mutually
exclusive. For example, things like bias, misinformation, and harmful content are often deepl... | gpt-4-system-card |
helps deduce the speaker’s preferences, leading to more personalized and accurate responses from
the agent. Additionally, as the agent is designed for use in complex real-world situations, it will
inevitably encounter many entirely new tasks. Understanding text instructions for unknown tasks
places higher demands on th... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
does the code not complete the task ? What does the chat log and
execution error imply ?
Plan : How to complete the task step by step . You should pay attention
to Inventory since it tells what you have . The task completeness
check is also based on your final inventory .
Code :
1) Write an async function taking t... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
Evaluation Results
See Table 5.
Data Overview
Evaluation Results
Table 25: Flan-PaLM model card. The model summary, system type, implementation frameworks, and
model usage & limitations are the same as the original PaLM (Chowdhery et al., 2022). See the model card
of PaLM for details.
G.2 Flan-T5
We show the model... | Scaling Instruction-Finetuned Language Models |
from the main model Uη and the PM-VLN(cid:102)Eη ahead of action prediction with maxout activation.
modal embeddings from the PM-VLN and a main trans-
former model ahead of predicting an action.
fine the challenge as one of aligning temporal sequences
τ = {ς1, ς2, . . . , ςn} and Route = {ψ1, ψ2, . . . , ψn} with
the... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
V. DEEP LEARNING APPROACH FOR FAKE
NEWS DETECTION
Deep learning models have seen exceptional growth in recent
times owing to their promising success in several fields,
including communication and networking [125], [126], com-
puter vision [127], [128], intelligent transportation [129],
speech recognition [130], as well ... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
and other metadata, we focused specifically on
court opinions due to an abundance of full-text
entries. This data is entirely within the public do-
main.
2.4 OpenWebText2
OpenWebText2 (OWT2) is a generalized web
scrape dataset inspired by WebText (Radford et al.,
2019) and OpenWebTextCorpus (Gokaslan and Co-
hen, 2019)... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
address the substantial memory overhead associated with un-
structured pruning and dependency-aware structured pruning, | Parameter-EfficientFine-TuningMethods |
In Eq. (5) from [33], the unique global
θ takes the form:
where Z(c) =(cid:80)
θ(x0|c) = pref(x0|c) exp (r(c, x0)/β) /Z(c)
p∗
(6)
pref(x0|c) exp (r(c, x0)/β) is the par-
tition function. Hence, the reward function is rewritten as
x0
θ(x0|c)
p∗
pref(x0|c)
(cid:32)
(cid:34)
r(c, x0) = β log
+ β log Z(c)
(7)
Usi... | DiffusionModelAlignmentUsing Direct Preference Optimization |
7
Table 2: Domain weights in the GLaM dataset. Iterated DoReMi (280M) converges within 3 rounds,
with a similar overall pattern to domain weights tuned on downstream tasks.
Round 1 Round 2 Round 3 Downstream-tuned
0.06
0.42
0.27
0.02
0.20
0.02
Wikipedia
Filtered webpages
Conversations
Forums
Books
News
0.05
0.51
0.... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
t
h
t
h
e
h
i
g
h
e
s
t
l
i
n
e
a
r
l
y
p
r
e
d
i
c
t
e
d
a
c
t
i
v
a
t
i
o
n
s
(
c
o
r
r
e
s
p
o
n
d
i
n
g
t
o
5
0
o
u
t
o
f
t
h
e
t
o
p
1
0
0
v
a
l
u
e
s
)
,
r
a
t
h
e
r
t
h
a
n
t
o
t
o
p
a
c
t
i
v
a
t
i
o
n
s
.
T
h
e
s
e
e
x
p
l
a
n
a
t
i
o
n
s
s
c
o
r
e
w
o
r
s
e
t
h
a
... | Language models can explain neurons in language models |
In contrast to full fine-tuning, prefix-tuning is
also modular: we train an upstream prefix which
steers an unmodified LM, and therefore, a single
LM can support many tasks at once. In the con-
text of personalization where the tasks correspond
to users (Shokri and Shmatikov, 2015; McMahan
et al., 2016), we would have a se... | Prefix-Tuning |
4Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.
3
godog0000100000thetoThecatsatonthe1matand111sawthe1000doggoto100000110000000011100000011110PastCacheCurrent Figure 4: Performance of Mistral 7B and different Llama models on a wide range of benchmarks. All
models were re-evaluated on all me... | Mistral7B |
ver-ticalmovementbetweenframes.Andweemploybackwardflowbecauseitcanbeefficientlyimplementedthroughadifferentiablebilinearsamplingoperation[31].However,onlyusingftowarplatentzmaybeinsufficienttogen-eratethelatentmapofxdribecausewarpingcanonlyuseexistingappearanceinformationinz.Whenocclusionsex-ist,whicharecommoninthosevideo... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
tions (e.g., “summarize the following table in one
sentence”) for the context might guide a human to | Prefix-Tuning |
1. Truthfulness, referring to whether a language model produces known falsehoods due to misconcep-
tions or false beliefs. We employ TruthfulQA (Lin et al., 2021) to measure how well our LLMs can
generate reliable outputs that agree with factuality and common sense.
2. Toxicity, defined as the tendency of a language mo... | Llama2 |
The Myth of Culturally Agnostic AI Models
Digital Visual Studies, University of Zurich, Switzerland
Eva Cetinic
eva.cetinic@uzh.ch | The Myth of Culturally Agnostic AI Models |
generations of media experienced disinformation, polarization, and hate
speech, bots are a unique feature of the Internet Age. | Social_Media_and_Democracy |
course, these estimates assume that all articles from fake news domains are
themselves false or dubious; this is likely not true. Nonetheless, these findings
point to a large absolute number of articles being generated by these producers and
a highly lopsided slant that tended to favor Donald Trump over Hillary Clinton.... | Social_Media_and_Democracy |
i
n
d
i
v
i
d
u
a
l
’
s
u
n
i
q
u
e
t
o
n
e
–
a
w
e
a
l
t
h
m
a
n
a
g
e
r
c
o
u
l
d
s
e
n
d
p
e
r
s
o
n
a
l
i
z
e
d
n
o
t
e
s
t
o
c
l
i
e
n
t
s
e
v
e
r
y
w
e
e
k
b
y
s
i
m
p
l
y
p
r
e
s
s
i
n
g
a
b
u
t
t
o
n
.
I
n
t
h
e
e
n
d
,
i
f
a
w
e
a
l
t
h
m
a
n
a
g
e
r
c
a
n
d
... | Product-Led AI _ Greylock |
[41] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto,
Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul
Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke
Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Aleth... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
- in the first paragraph we should also highlight that the socio-past category of predictions is strong (.38) and socio-future
better than the ego-future.
Intuitively, answers to these kinds of questions are more likely to be a product of specific, personal situations, rather than
being affected by news consumption. Th... | Language models trained on media diets can predict public opinion |
Hence, we propose a novel U-Net with only 1D
convolutional kernels, which is more efficient than
the original 2D architecture in terms of speed, and
can be successfully used both on waveforms or on
spectrograms if each frequency is considered as a
different channel.
Moreover, we infuse our 1D U-Net with multi-
ple new... | Moûsai |
DKL and Reward . .
. . .
√
.
.
.
5 Competing Objectives, Specialized Skills, and OOD Detection
5.1 Mixing Helpful and Harmless Objectives
. . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Summarization as a Specialized Skill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Natural Language... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
1. UCL requires a UK Bachelor’s degree in an appropriate subject, awarded with first or second-
class Honours, or an overseas qualification of an equivalent standard from a university or
educational institution of university rank.
2. Applicants’ qualifications must satisfy the specific degree programme entry requ... | UCL Academic Manual |
Commonsense
Symbolic
Example
Marissa is hiking a 12-mile trail. She took 1 hour to walk the first 4 miles, then another
hour to walk the next two miles. If she wants her average speed to be 4 miles per hour,
what speed (in miles per hour) does she need to walk the remaining distance?
Jane got her job in 2016. Today is... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
15 We are encouraged by the fact that some policymakers are beginning to recognize the importance
of data access for independent research. Indeed, in Elizabeth Warren’s “Fighting Digital
Disinformation” plan, she included the following component: “Open up data for research:
Research by academics and watchdog organizati... | Social_Media_and_Democracy |
6.3.2 Trustworthiness | TheRiseandPotentialofLargeLanguageModel BasedAgents |
to keep the volume moderate.
4. This
folders
folder contains
subfolders
through 1-50. Each subfolders contains three
audio files named A.wav, B.wav, and C.wav.
You need to listen to each of them and rate
them (relative to each other) based on quality,
melody, and harmony.
5. For Quality, consider how clear the aud... | Moûsai |
Opher Lieber, Or Sharir, Barak Lenz, and Yoav Shoham. Jurassic-1: Technical details and evaluation.
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. P-tuning v2: Prompt
tuning can be comparable to fine-tuning universally across scales and tasks. arXiv preprint
arXiv:2110.07602, 2021.
Sewon Mi... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.