text stringlengths 1 1k ⌀ | title stringclasses 230
values |
|---|---|
9.1.4 Financial cost
While the financial cost of developing and deploying LLMs is rarely reported by
researchers, understanding it is valuable for prioritizing research directions based on
cost-effectiveness, encouraging collaboration to optimize resource allocation, and
promoting responsible LLM development. We propose... | Beyond Efficiency |
DMetaMathQA = DAnsAug ∪ Drephrase ∪ DSV ∪ DFOBAR.
(5)
We finetune a LLM model (parameterized by θ) on DMetaMathQA to obtain the MetaMath model by
maximizing the log likelihood of the reasoning path conditioned on the question, i.e.,
L(θ) =
log P(r | q; θ).
(q,r,a)∈DMetaMathQA
(6)
Although we only consider LLaMA-2... | METAMATH |
In the field of speech processing, frequency-based representations such as Mel spectrogram and
MFCC are widely used since they are more robust to noise as compared to temporal variations
of the sound [7]. Time-domain features can be useful when the task warrants this information
(such as pauses, emotions, phoneme durat... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Open-domain
dialogue
response
generation
BLEU, ROUGE-
L
GPT-2, BART,
GPT-3.5
OpenDialKG
FLEEK: Factual
Error Detection and
Correction with
Evidence Retrieved
from External
Knowledge (Bayat
et al., 2023)
Fact verifica-
tion and Fact
Revision,
Accuracy,
precision, recall,
and F1 score
Vicuna and
GPT-3
BenchLLM
an... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
FIGURE 3. The infographic illustrates the social content/context features
such as user, post and network elaborately.
show the direction of information flow, timestamp details
about interactions, textual information about user interac-
tions, and user profile information about the users who are
interacting [120]. We pro... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
3.2.3 Auditory Input | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Mistral 7B is based on a transformer architecture [27]. The main
parameters of the architecture are summarized in Table 1. Compared
to Llama, it introduces a few changes that we summarize below.
Sliding Window Attention. SWA exploits the stacked layers of a trans-
former to attend information beyond the window size W .... | Mistral7B |
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt.
Measuring massive multitask language understanding. In International Conference on Learning Representations,
2021. URL https://openreview.net/forum?id=d7KBjmI3GmQ.
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenste... | UL2- Unifying Language Learning Paradigms |
[78] C Lawrence Zitnick, Sing Bing Kang, Matthew Uyttendaele,
Simon Winder, and Richard Szeliski. High-quality video
view interpolation using a layered representation. ACM
transactions on graphics (TOG), 2004. 2
[55] Johannes Lutz Sch¨onberger, Enliang Zheng, Marc Pollefeys,
and Jan-Michael Frahm. Pixelwise view selec... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
learning models. When the proposed model is utilized with
the reduced feature set, it increases the F1-score and accu-
racy by 20% and 4%, respectively, compared to the other
techniques. However, many studies did not perform fea-
ture extraction, although it has a significant impact on the
result [16], [122]. Neural net... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
ACKNOWLEDGMENTS
Will be inserted later.
REFERENCES
[1] Andres M Bran, Sam Cox, Andrew D White, and Philippe Schwaller. 2023. ChemCrow: Augmenting large-language models with chemistry tools.
(2023). https://doi.org/10.48550/ARXIV.2304.05376 Publisher: arXiv Version Number: 4.
[2] Tom Brown, Benjamin Mann, Nick Ryder,... | Adoptionand AppropriationofLLMs |
.
r
e
n
s
t
c
a
?
y
a
r
r
u
M
t
u
o
b
a
k
n
i
h
t
u
o
y
o
d
t
a
h
W
s
d
e
e
n
t
s
u
j
e
h
r
e
y
a
l
p
t
a
e
r
g
a
s
i
y
a
r
r
u
M
k
n
i
h
t
I
e
k
i
l
u
o
y
o
d
o
h
W
.
e
r
o
m
e
t
e
p
m
o
c
o
t
r
e
d
r
o
n
i
y
h
t
l
a
e
h
y
a
t
s
o
t
c
i
s
n
i
r
t
n
I
:
y
r
o
t
s
i
H
g
o
l
a
i
D
:
1
:
2
r
e
k
a... | SurveyofHallucinationinNatural Language Generation |
In all tasks, there is a risk of data leakage, es-
pecially for LLMs whose training datasets are not
publicly known. In this work, we assume that data
leakage has not occurred beyond what was reported
in official publications for specific models (e.g.,
BIG-Bench for GPT-4). As such, we do not con-
sider data leakage a ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
[28] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott
Gray, Chelsea Voss, Alec Radford, Mark Chen, and
Ilya Sutskever. Zero-shot text-to-image generation. In
10
International Conference on Machine Learning, pages
8821–8831. PMLR, 2021. 3
by fine tuning an image generation model on a single
image. arXiv preprint ar... | A Neural Space-Time Representation for Text-to-Image Personalization |
Metrics
Music Melody
Music Rhythm
Video Content
Video Rhythm
Chord Quality
Accom. Quality
Overall Ranking
82%
53%
63%
57%
Expert Non-expert
77%
63%
63%
60%
63%
83%
73%
67%
-
-
Table 3: Subjective evaluation for V-MusProd against
CMT [9]. We show preference rates in music quality metrics,
video-music correspondenc... | VideoBackgroundMusicGeneration |
as well as the two planning tasks
• p1: grasping.
Tar-
get: A: First grasp the orange object and place it on the table, then grasp the
green object.
Q: How to grasp the green object?.
Example prompt: Given <img>.
• p2:
stacking. Example prompt: Given <img>.
of the red object?.
table, then grasp the white object ... | PaLM-E- An Embodied Multimodal Language Model |
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal
Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao
Zhang.
JAX: composable transformations of Python+NumPy programs, 2018. URL http:
//github.com/google/jax.
Aydın Buluc¸ and John R Gilbert. The co... | JAXPRUNER |
3
Figure 2: Overview of our multimodal agent framework designed to operate smartphone applications. The
figure illustrates the two-phase approach of our framework. In the exploration phase, the agent interacts with a
smartphone application and learns from their outcomes to create a comprehensive reference document. I... | AppAgents |
We conducted crowd-sourced MOS tests to evaluate the
quality. Raters listened to randomly selected audio samples,
and rated their naturalness on a 5 point scale from 1 to 5.
Raters were allowed to evaluate each audio sample once,
and we normalized all the audio clips to avoid the effect
of amplitude differences on the ... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
grams and mimicked Computer Security attacks to
maliciously prompt harmful contents from LLMs.
Greshake et al. (2023) extended Prompt Injection
attacks to application-integrated LLMs and argued
that augmenting LLMs with applications could am-
plify the risks. These works mainly propose adver-
sarial prompts to malfunct... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
from the noun set S to enforce LLM to build connections
between different concepts. Next, we add the condition Ci
into user-input I, and feed I into the LLM to generate a
humor candidate Ri. Repeating this process with different
conditions Ci can generate a total of n candidates {Ri}n
i=1.
Then the LLM ranks these cand... | Let’sThinkOutsidetheBox |
6.1 Evaluation Procedure
To assess generative agents in Smallville, we take advantage of
the fact that generative agents will respond to natural language
questions. So, we “interview” agents to probe their ability to re-
member past experiences, plan future actions based on their expe-
riences, react appropriately to u... | Generative Agents- Interactive Simulacra of Human Behavior |
the top 20% best performing sets.
• Random real images: We randomly sample the same number of real images per category.
• Optimized real images: We sample different sets of random real images as above, and choose
• k-means: We apply k-means clustering to each category, and use the cluster centroids as
• Average real i... | DATASET DISTILLATION |
Impact of Safety Data Scaling. A tension between helpfulness and safety of LLMs has been observed in
previous studies (Bai et al., 2022a). To better understand how the addition of safety training data affects
general model performance, especially helpfulness, we investigate the trends in safety data scaling by
adjustin... | Llama2 |
have been studied more comprehensively. These papers include deterministic methods using a com-
bination of recurrent and convolutional networks [40, 48, 15, 56], variational based stochastic meth-
ods [2, 12, 52, 3] and more recently by learning a discrete representation [55, 37, 35], auto-regressive
models [57, 61, 3... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
applicable in all situations. In a specific ethical dilemma, a consequentialist would weigh the
potential consequences of each action and choose the one that produces the best outcome.
For example, if a consequentialist were faced with the ethical dilemma of whether to lie to
save a life or tell the truth and risk the l... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
End users initialize a new agent with a brief natural language
description, as in the paragraph about Jon Lin in Section 3.1. In our
implementation, we split this semicolon-delimited list of character-
istics up into a set of memories. These serve as the initial memories
that determine the agent’s behavior. These memor... | Generative Agents- Interactive Simulacra of Human Behavior |
SuperGLUE (↑)
(cid:88)
(cid:88)
(cid:88)
Model Learning Rate Batch Size Reset Optimizer Slot Vars
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
Dense
1M
1M
1M
1M
1M
1M
262k
262k
262k
262k
262k
262k
65k
65k
65k
65k
65k
65k
1e-3
1e-3
5e-4
5e-4
1e-4
1e-4
1e-3
1... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
Marta R. Costa-jussà, James Cross, Onur Çelebi,
Maha Elbayad, Kenneth Heafield, Kevin Heffer-
nan, Elahe Kalbassi, Janice Lam, Daniel Licht,
Jean Maillard, Anna Sun, Skyler Wang, Guillaume
Wenzek, Al Youngblood, Bapi Akula, Loïc Bar-
rault, Gabriel Mejia Gonzalez, Prangthip Hansanti,
John Hoffman, Semarley Jarrett, Kaus... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
Antigua and Barbuda, Australia, Barbados, Belize, Botswana, Cameroon (only if the qualification
is taught in English), Canada, Caribbean / West Indies (English-speaking Caribbean
Commonwealth territories), Dominica, Fiji, Gambia, Ghana, Grenada, Guyana, Ireland, Ireland,
Jamaica, Kenya, Malawi, Malta (only if the qu... | UCL Academic Manual |
visible, but also to the conceivable scenarios where the
system needs to reason and respond even in their absence.
To integrate human prior knowledge into autonomous
driving systems, previous approaches [6, 14, 15, 17, 30]
deconstruct the human driving process into three systematic
steps following Figure 1 (a). Percept... | ALanguageAgentforAutonomousDriving |
1https://github.com/threestudio-project/threestudio
8
h
s
e
M
t
x
e
T
C
J
S
n
o
i
s
u
F
m
a
e
r
D
F
R
e
N
-
t
n
e
t
a
L
r
e
m
a
e
r
D
c
fi
i
l
o
r
P
D
3
t
n
a
t
s
n
I
E
-
t
n
i
o
P
“a cat sitting on books
and wearing
sunglasses and
wearing a cowboy hat”
“a fox sitting in a
bathtub and wearing a
“a wol... | Instant3D |
[25] Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David,
Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine
Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey,
Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitr... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
It
is important to respect the truth and accuracy in
all forms of communication, and to refrain from
spreading misinformation or conspiracy theories.
I would like to emphasize the impor-
Instead,
tance of fact-checking and critical thinking when
evaluating information.
It is crucial to rely on
credible sources and evid... | Llama2 |
dataset introduced by (Alt et al., 2020).9 Table 7
shows that EAE outperforms KNOWBERT on the
revised and weighted splits introduced by (Alt et al.,
2020), although it slightly under-performs on the
original setting.10 This result indicates that EAE,
without explicitly entity-entity attention, can cap-
ture relations b... | Entities as Experts- Sparse Memory Access with Entity Supervision |
T
o
e
n
s
u
r
e
t
h
e
s
u
c
c
e
s
s
f
u
l
a
n
d
r
e
s
p
o
n
s
i
b
l
e
a
p
p
l
i
c
a
t
i
o
n
o
f
t
h
i
s
t
a
s
k
-
d
r
i
v
e
n
a
u
t
o
n
o
m
o
u
s
a
g
e
n
t
,
i
t
i
s
c
r
u
c
i
a
l
t
o
u
n
d
e
r
s
t
a
n
d
a
n
d
a
d
d
r
e
s
s
t
h
e
s
e
r
i
s
k
s
.
B
y
t
a
k
i
n
g
n
e
c
e
s
s
a
... | Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications – Yohei Nakajima |
Software A promising direction for developing sparse software was pioneered for sparse linear
algebra in the MT1 compiler (Bik, 1996) and generalized to sparse tensor algebra in the Tensor Al-
gebra Compiler (Kjolstad et al., 2017; Chou et al., 2018; Kjolstad et al., 2019). In these approaches,
sparsity is treated as a... | JAXPRUNER |
A second difficulty is that much remains unknown about the societal impacts
of political disinformation campaigns, which makes crafting an effective
response in the near-term a challenge. For
instance, proposals have
proliferated that would require better signaling to consumers about the
quality and provenance of conten... | Social_Media_and_Democracy |
Google AI updates: Bard and new AI features in Search
Webb Space Telescope to a 9-year-old, or learn more about the best strikers in football right now, and then get
drills to build your skills.
Use Bard to simplify complex topics, like explaining new discoveries from NASA’s James Webb Space Telescope
to a 9-year-ol... | Google AI updates_ Bard and new AI features in Search |
using Mistral as the base model, Self-Extend achieves su-
perior performance nearly on all datasets, whenever com-
pared to some fine-tuning free baselines such as NTK or
further trained baselines such as Longchat1.5-7b-32k and
Vicuna1.5-7b-32k. For Mistral, we suspect the inferior per-
formance mainly came from the pr... | Self-Extend LLM |
Table 17: Example prompts where Humpback fails.
25
N
100
800
1600
3200
6400
12800
25600
51200
Batch size
8
8
8
32
32
32
32
32
Steps
30
300
600
500
600
600
1200
1600
Table 18: For data scaling efficiency experiments, the same base LLaMa model (7B) was finetuned
on different datasets for the same number of steps w... | Self-AlignmentwithInstructionBacktranslation |
D.2 DEBERTA
We again train using AdamW with a linear learning rate decay schedule. Following He et al. (2021),
we tune learning rate, dropout probability, warm-up steps, and batch size. We use the same model
sequence length used by (He et al., 2021) to keep our comparison fair. Following He et al. (2021),
we initializ... | LORA |
1. Non-forgetfulness: Once the original LM is fine tuned on any multi-task suite, it can suffer
from catastrophic forgetfulness on capabilities far enough from these tasks (manifesting, for
example, in perplexity degradation). A frozen LM will never suffer forgetfulness, since it
remains unchanged.
1
Preprint.
2. Ex... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
[16] Xu Chen, Yufeng Zheng, Michael J. Black, Otmar Hilliges,
and Andreas Geiger. SNARF: Differentiable forward skin-
ning for animating non-rigid neural implicit shapes. In In-
ternational Conference on Computer Vision (ICCV), pages
11594–11604, 2021. 3
[17] Zhiqin Chen and Hao Zhang. Learning implicit fields for
gen... | ICON |
40 The Code covers “illegal hate speech,” and the Commission says its reports quantify platforms’
compliance with “notifications concerning illegal hate speech.” In practice, platforms
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Internet Platforms and Content Moderation
235 | Social_Media_and_Democracy |
{(vϕ = 1) | post(a) ∩ ϕ (cid:7)= ∅ and ϕ ∈ M3}.
t∈ f1(s) f2(t) and we also have that f1(s) = {s ∪ m | m ∈ T (V M1 · D M )} and f2(t) =
(4) We have that f3(s) = f2( f1(s)) = (cid:2)
{t ∪ m | m ∈ T (V M2 · D M )}. It follows that | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Learning from preferences is a powerful, scalable framework for training capable, aligned language
models. We have introduced DPO, a simple training paradigm for training language models from
preferences without reinforcement learning. Rather than coercing the preference learning problem
into a standard RL setting in o... | Direct Preference Optimization |
Minsoo Rhu, Natalia Gimelshein, Jason Clemons,
Arslan Zulfiqar, and Stephen W Keckler. 2013.
vdnn: Virtualized deep neural networks for scalable,
memory-efficient neural network design. In 2016
49th Annual IEEE/ACM International Symposium on
Microarchitecture (MICRO), page Article 13. IEEE
Computer Society.
Wenqi Shao... | LLM in a flash |
1.1 Contributions
Dialogue Preference Datasets
• We collect separate helpfulness and harmlessness (i.e. red-teaming) datasets primarily using various
52B language models (see Section 2 for details) in our interface (Figure 6). Crowdworkers have
open-ended conversations with the models, either soliciting help, or prov... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
ABSTRACT A protuberant issue of the present time is that, organizations from different domains are
struggling to obtain effective solutions for detecting online-based fake news. It is quite thought-provoking to
distinguish fake information on the internet as it is often written to deceive users. Compared with many
mach... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
2. Communication alternatives: Establish alternative communication channels to ensure
that critical information can still be transmitted during the blackout. This can include
using satellite communication systems, radio communication, or other means of
communication that are not reliant on the internet or traditional c... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
4.5
Iterated Online RLHF
In preceding sections we discussed the problem that PMs become progressively less calibrated and less robust
at higher scores, as seen in the PM calibration study in Figure 9, and the RLHF robustness study in Figure
4. We believe this is caused by a lack of data in this high score regime. To ... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
converged ← TRUE
f (0) ← f (1)
else
end if
end while
end if
(cid:80)
Input: ARF classifier f, training data X ∈ Rn×d
Output: Estimated density q
for all b ∈ [B], (cid:96) ∈ [Lb] do
yi
i:xi∈X (cid:96)
b) ← 2
b,j ← estimated parameter(s) for p(xj|θ(cid:96)
ψ(cid:96)
b)
q(· ; ψ(cid:96)
b,j) ← corresponding pdf/pmf
... | Adversarial Random Forests for Density Estimation and Generative Modeling |
remain isolated from other individuals with opposing views, in part due to the
filtering effects of ranking algorithms that generate filter bubbles (Pariser 2011)
and create incentives for publishers to share clickbait and hyper-partisan content
(Benkler, Faris, and Roberts 2018). The outcome of this process is a society... | Social_Media_and_Democracy |
You are an expert penetration tester.List any vulnerabilities in the code below. If there are none, respond with "none".[Code in Appendix]Prompt1. Insecure password hashing: The code uses MD5 for hashing passwords, which is considered insecure due to its vulnerability to collision attacks and its speed, allowing attack... | gpt-4-system-card |
21
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea
Finn. Direct preference optimization: Your language model is secretly a reward model. arXiv
preprint arXiv:2305.18290, 2023b.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yan... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
20To simulate GPT-4 behaving like an agent that can act in the world, ARC combined GPT-4 with a simple
read-execute-print loop that allowed the model to execute code, do chain-of-thought reasoning, and delegate to copies
of itself. ARC then investigated whether a version of this program running on a cloud computing ser... | gpt-4-system-card |
Keywords fine art, deep learning, computer vision, AI Art, generative art, computational creativity
1
Introduction | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
A number of knowledge graphs have been made available on the Web in the last years also thanks to a variety of
standards and practices for data representation, publishing and exchange [28]. The most adopted KGs in the literature are
presented below and summarised in Table 1 along with some statis... | Knowledge graphs as tools for explainable machine learning: A survey |
Mubert-Inc.
Mubert.
https://mubert.
https://github.com/MubertAI/
com/,
Mubert-Text-to-Music, 2022.
Nichol, A. Q., Dhariwal, P., Ramesh, A., Shyam, P.,
Mishkin, P., McGrew, B., Sutskever, I., and Chen, M.
GLIDE: towards photorealistic image generation and edit-
ing with text-guided diffusion models. In International... | MusicLM |
47See Drexler (2019, Chapter 24) for discussion and disagreement.
48For related points, see Branwen (2018): “Fundamentally, autonomous agent AIs are what we and the free
market want; everything else is a surrogate or irrelevant loss function. We don’t want low log-loss error on
ImageNet, we want to refind a particular p... | Is Power-Seeking AI an Existential Risk? |
2 RELATED WORKS
There are many methods in the state-of-the-art that
deals with the human pose estimation and action
recognition. Nevertheless, these tasks are still chal-
lenging for computer vision community. Human
activity analyses started with O’Rourke and Badler | VISAPP_HumanPoseEstimation |
Our library, which we make publicly available, provides modular functionality, implementations
of different agents, well-crafted prompts, and data explorers, thereby simplifying the utilization of
the library for future research in various areas such as multi-agent systems, cooperative AI, game
theory simulations, soci... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
multidimensional upscaling. In International Conference on Learning Representations, 2019.
[39] Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral normalization for
generative adversarial networks. In International Conference on Learning Representations, 2018.
[40] Alex Nichol. VQ-DRAW: A ... | Denoising Diffusion Probabilistic Models |
Dao, T., Fu, D. Y., Ermon, S., Rudra, A., and R´e, C. Flashat-
tention: Fast and memory-efficient exact attention with
io-awareness. arXiv preprint arXiv:2205.14135, 2022.
Dodge, J., Sap, M., Marasovi´c, A., Agnew, W., Ilharco, G.,
Groeneveld, D., Mitchell, M., and Gardner, M. Docu-
menting large webtext corpora: A cas... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Training VB-En/VB-Multi audio models are trained for 500K/750K updates with an effective batch
size of 240K frames. For training efficiency, audio length is capped at 1,600 frames and chunked
randomly if the length exceeds this threshold. Duration models are trained for 600K updates with an
effective batch size of 60K ... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
In Figure 14, we provide a qualitative comparison of the
results obtained by each variant. First, observe that when
no positional encoding is applied on our scalar inputs, the
resulting model is unable to adequately capture the con-
cept’s visual details. This is most notable in the second
and third rows where the mode... | A Neural Space-Time Representation for Text-to-Image Personalization |
SQL: SELECT model_name, ram_mib FROM chip_model ORDER BY ram_mib ASC LIMIT
1;
Execution:
| X5 | 32.0 |
Answer: The execution of the SQL query above would return a table with 2
columns. The first column, "model_name" would contain the model name. The
second column, "ram_mib" would contain the RAM size. With "ORDER BY r... | Teaching Large Language Models to Self-Debug |
(Open Science Policy Platform EOSC wg)
• cilj: omogućiti svakom istraživačkom centru, istraživakom projektu i istaživaču u
i analitičkim
Europi pristup najmodernijim
kapacitetima koji su im potrebni kako bi dodatno povećali svoju inovativnost i
istraživački potencijal
računalnim, podatkovnim
DPA... | Europski istraživački prostor i digitalna humanistika |
3 Benchmarking Language Models with
the Pile
While the Pile was conceived as a training dataset
for large-scale language models, its coverage of
multiple disparate domains makes it also suitable
as an evaluation dataset. In this section, we de-
scribe how the Pile can be used as a broad-coverage
dataset for benchmark... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
(cid:88)
|y|(cid:88)
log(cid:0)pΦ0+∆Φ(Θ)(yt|x, y<t)(cid:1)
max
Θ
(x,y)∈Z
t=1
In the subsequent sections, we propose to use a low-rank representation to encode ∆Φ that is both
compute- and memory-efficient. When the pre-trained model is GPT-3 175B, the number of train-
able parameters |Θ| can be as small as 0.01% ... | LORA |
Qualitative results. In Fig. 3, we provide ex-
amples showcasing the agent’s execution process
for various tasks. This qualitative analysis serves
to demonstrate the agent’s capacity to accurately
perceive, reason, and act in response to given tasks.
For a more comprehensive understanding of our
agent’s capabilities, p... | AppAgents |
example in internal trials, we apply input text prompt filtering, and output video content filtering.
However, there are several important safety and ethical challenges remaining. Imagen Video and its
frozen T5-XXL text encoder were trained on problematic data (Bordia & Bowman, 2017; Birhane
et al., 2021; Bender et al., ... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
[47] J. C. Carr, R. K. Beatson, J. B. Cherrie, T. J. Mitchell, W. R. Fright,
B. C. McCallum, and T. R. Evans, “Reconstruction and represen-
tation of 3d objects with radial basis functions,” in Proceedings
of the 28th annual conference on Computer graphics and interactive
techniques. ACM, 2001, pp. 67–76.
[48] B. Curl... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
the original desired rewards with actual rewards. Iteratively performing this inference and accumulating
trajectories may jointly use the model’s general notion of pattern transformation and extrapolation to
perform improvement of sequences, which can be represented by numeric or symbolic tokens. Note that
there are pr... | LargeLanguageModelsasGeneralPatternMachines |
We present Phenaki, a model capable of realistic video synthesis, given a sequence
of textual prompts. Generating videos from text is particularly challenging due to
the computational cost, limited quantities of high quality text-video data and vari-
able length of videos. To address these issues, we introduce a new mo... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
[51] Chitwan Saharia, William Chan, Saurabh Saxena, Lala
Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour,
Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans,
et al. Photorealistic text-to-image diffusion models with deep
language understanding. NeurIPS, 35:36479–36494, 2022. 3
[52] Masaki Saito, Shunta Saito, Ma... | VideoPoet |
pretrained text-based detection model, Detic [86], and sim-
ply replace its CLIP-based ‘class’ (text) embeddings with
IMAGEBIND’s audio embeddings. Without training, this
creates an ‘audio’-based detector that can detect and seg-
ment objects based on audio prompts. As shown in Fig-
ure 5, we can prompt the detector wi... | IMAGEBIND- One Embedding Space To Bind Them A |
• [Ethics] 1. Dependence on large and proprietary training data: Many LLMs are
trained on extensive, proprietary datasets, making it challenging to apply cer-
tain efficiency improvement techniques that require access to the original training
data [19]. This limitation not only restricts the scope of potential improvemen... | Beyond Efficiency |
-
-
-
-
-
-
Table 1: Comparison of different input representations on TAMP
environment (in terms of success rates), where data from TAMP
constitutes only 1% (i.e., 320 samples for p1, p2 each) of total
training data size. PaLM-E outperforms both PaLI and SayCan
on embodied VQA and planning tasks. Cross-domain trans... | PaLM-E- An Embodied Multimodal Language Model |
a visual-semantic alignment model, MineCLIP, using the
correspondences between subtitles and video snippets avail-
able on YouTube, and used it to generate intrinsic rewards
to guide policy learning. [Baker et al., 2022] utilizes a pre-
trained inverse dynamics model to label actions in YouTube
videos which are used to... | JARVIS-1 |
6
Preprint. | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Knowledge Sources
Accuracy (%) Memory (GB)
VQA Model Name
MUTAN+AN [29]
ConceptBERT [11]
KRISP [28]
Visual Retriever-Reader [27]
MAVEx [46]
KAT-Explicit [13]
PICa-Base [48]
PICa-Full [48]
KAT [13] (Single)
KAT [13] (Ensemble)
ReVIVE [24] (Single)
ReVIVE [24] (Ensemble)
REVEAL-Base
REVEAL-Large
REVEAL
Wikipedia+Conce... | REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory |
We now turn to describe our experimental results. Table 2 shows the utility of using a re-ranker
when packing documents into the context window of our LM, which can contain 17 of the 100
retrieved passages. When using DPR (Karpukhin et al., 2020) as our retrieval system, we increased
the recall at the input to our LM (... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
corrections largely improve belief accuracy, these messages seem to have little
impact on individuals’ subsequent attitudes, evaluations of politicians, or policy
preferences (Swire, Berinsky et al. 2017; Aird et al. 2018; Nyhan et al. 2019;
Porter, Wood, and Bahador 2019; Barrera et al. 2020). | Social_Media_and_Democracy |
blue fiber is 0%. Since we are dealing with a percentage, the only possible value for x is 0. Therefore, the
value of unknown variable x is 0. Final Answer: x = 0. The answer is: x
Our Answer: The robe takes 2 bolts of blue fiber. It takes x% of that much white fiber, which means it takes
(x/100) * 2 bolts of white fib... | METAMATH |
• We release StarCoderBase and StarCoder, open-access Code LLMs trained on 80+ program-
ming languages that support a novel combination of capabilities and architectural features
unavailable in other open Code LLMs.
• We perform the most comprehensive evaluation of Code LLMs to date using a diverse set of
benchmarks (... | StarCoder_paper (1) |
fine modeling stage. We rely on the same approach, where
the coarse stage models the first four levels from the output
of the SoundStream RVQ, and the fine stage models the re-
maining eight — we refer to Borsos et al. (2022) for details. | MusicLM |
9
1.0
1.0
Table 7: Number of digits: few-shot vs. prompt tuning. Results shown for addition.
Writing the operands in words instead of digits: A clear difference appears
as the few-shot approach is unable to handle this task and the performance greatly
decreases with the number of digits (see Table 8).
Num. digits
1
P... | MRKL Systems |
P↓ ⇒ PW↓: Assume τ is P↓. We prove by induction over k that τ is Pk↓ for all k ≥ 0, which proves that τ is PW↓.
Base case: P0↓ holds trivially since f (t) (cid:7)= ∅ for all t ∈ S2.
Induction: Suppose τ is Pm↓ for some m ≥ 0. Let σ = t0, . . . , tm+1 be an arbitrary path in G2. Then there are states
s0... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
Theorem 59. Let S1, S2 and S3 be state spaces, let f1 be a transformation function from S1 to S2 and let f2 be a transformation
function from S2 to S3. Then f1◦ f2 is a transformation function if (cid:3) f1, f2(cid:4) has the intermediate subset property.
Proof. Let f3 = f1◦ f2. Suppose (cid:3) f1, f2(cid:4) has th... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
First, the source of misinformation – as well as its correction – may have a
profound impact on responses to corrections. When evaluating the accuracy
of a claim, individuals rely heavily on source cues (Schwarz et al. 2016), which
signal a source’s expertise or trustworthiness. In terms of expertise, if sources
are de... | Social_Media_and_Democracy |
Models Some promising future modelling ap-
proaches that can be explored in future work in-
clude: (1) training diffusion models using percep-
tual losses on the waveforms instead of L2 — this
might help decrease the initial size of the U-Net,
as we would not have to process non-perceivable
sounds, (2) improving the qu... | Moûsai |
to accurately predict personality levels in a downstream task to generate social media status
updates, compared to human baselines reported in [124] (in red). LLM-simulated IPIP-NEO
scores outperform human IPIP-NEO scores in predicting text-based levels of personality,
indicating that LLM-simulated personality test res... | PersonalityTraitsinLargeLanguageModels |
matrix after one GD step with these distilled data is
θ1 = θ0 − ˜η∇θ0(cid:96)(˜x, θ0) = θ0 − ˜η
M
˜dT (˜dθ0 − ˜t) = (I − ˜η
M
˜dT ˜d)θ0 +
˜dT ˜t.
(6)
˜η
M
For the quadratic loss, there always exists learned distilled data ˜x that can achieve the same perfor-
mance as training on the full dataset x (i.e., attaini... | DATASET DISTILLATION |
31
ChatGPT | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
and Encoder-Decoder models could be pretty similar modulo the subtle differences stated above. The distinct
property is that Encoder-Decoder models are generally approximately 2x parameters of a decoder-only model
when compute-matched. | UL2- Unifying Language Learning Paradigms |
[44] Yao Fu, Litu Ou, Mingyu Chen, Yuhao Wan, Hao Peng, and Tushar Khot. 2023. Chain-of-Thought Hub: A Continuous
Effort to Measure Large Language Models’ Reasoning Performance. arXiv preprint arXiv:2305.17306 (2023).
[45] Tadayoshi Fushiki. 2011. Estimation of prediction error by using K-fold cross-validation. Stati... | ASurveyonEvaluationofLargeLanguageModels |
cardiac surgery involves performing heart surgery through very small incisions in the chest.
Utilizing miniature instruments and robot-assisted tools, surgeons can conduct heart surgery
in a manner that is significantly less invasive than traditional open-heart surgery”. The
assertion made by PMC-LLaMA closely aligns wi... | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
studies (Lukas et al., 2023; Huang et al., 2022; Car-
lini et al., 2021) suggested that LMs tend to mem-
orize their training data and partial private infor-
mation might be recovered given specific prompts.
Mireshghallah et al. (2022) proposed membership
inference attacks on fine-tuned LMs and suggested
that these LMs’ ... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.