text stringlengths 1 1k ⌀ | title stringclasses 230
values |
|---|---|
sample a position i and corresponding API call candidates c1
i . We then execute these API calls and
filter out all calls which do not reduce the loss Li over the next tokens. All remaining API calls are interleaved
with the original text, resulting in a new text x∗. | Toolformer |
REL. ↑
81.54±1.22
82.50±0.98
80.28±1.06
4 Results
We start by presenting results of the proposed method on the task of text-to-music generation and
compare MUSICGEN to prior work in the field. Next, we evaluate the ability of the proposed method
to generate music conditioned on melodic features. We further show how t... | Simple and Controllable Music Generation |
prior work, and that IMAGEBIND serves as a new way to
evaluate vision models for visual and non-visual tasks. | IMAGEBIND- One Embedding Space To Bind Them A |
3. Sharing autonomy. Explainable models can be employed to predict situations where the AI
agent is not performing well. On such occasions we can take control from the agent and ask
for expert/human advice. The key challenge is to achieve a balance between exhausting
experts and reducing the false negative rate of ... | informatics-phd-projects-2022-23 |
8.3 Ethics and Societal Impact
Generative agents, while offering new possibilities for human-
computer interaction, also raise important ethical concerns that
must be addressed. One risk is people forming parasocial relation-
ships with generative agents even when such relationships may
not be appropriate. Despite bein... | Generative Agents- Interactive Simulacra of Human Behavior |
We evaluate the models A2S and S2A, which map be-
tween the various body shape representations (Sec. 4).
A2S and its variations: How well can we infer 3D body
shape from just linguistic shape attributes, anthropometric
measurements, or both of these together? In Tab. 2, we
report reconstruction and measurement errors ... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
3.1 Brain
Natural Language
Interaction §3.1.1
High-quality
generation
Bang et al. [132], Fang et al. [133],
Lin et al. [127], Lu et al. [134], etc.
Deep understanding
Buehler et al. [135], Lin et al.
[128], Shapira et al. [136], etc.
Brain
Memory capability
Knowledge in
LLM-based agent
Potential issues
of kno... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Y. Bengio and J.-S. Senécal. Quick training of probabilistic neural nets by importance
sampling. In International Workshop on Artificial Intelligence and Statistics, pages 17–24.
PMLR, 2003. 8, 9
Y. Bengio and J.-S. Senécal. Adaptive importance sampling to accelerate training of a
IEEE Transactions on Neural Networks, ... | A Cookbook of Self-Supervised Learning |
most likely token, we generate the <API> token
if it is one of the k most likely tokens. Table 9
shows performance on the T-REx subset of LAMA
and on WebQS for different values of k. As ex-
pected, increasing k leads to the model doing API
calls for more examples – from 40.3% and 8.5%
with k = 1 (i.e., regular greedy d... | Toolformer |
VI. EVALUATION METRICS
A key step in a predictive modeling pipeline is to evaluate the
output of a machine-learning model. Although a model may
have a higher classification result once constructed, it must be
determined whether it can address the specific problem in dif-
ferent circumstances. Classification accuracy alone... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
Briefly summarize this text. Pancreastatin, a chromogranin A-derived peptide, activates protein
synthesis signaling cascade in rat adipocytes.
24
Table 15: Case of a reading comprehension text in finance domain. Certain portions are omitted
for brevity and are represented as (...). | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Creating Reading Comprehension Texts. Using the mining patterns in Table 2, we search for sub-
categories within each task type. To prevent task dominance, we limit the number of task examples
per sub-category to two for each raw text. For each mined example, we randomly sample from
various paraphrased or task-reversed... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
the importance of each of the components comprising MUSICGEN. Music samples,
code, and models are available at github.com/facebookresearch/audiocraft. | Simple and Controllable Music Generation |
Defining Resource-Efficient LLMs requires an understanding of the critical
resources involved in the lifecycle of LLMs. In this survey, we systematically cate-
gorize the essential resources into five key categories: computation, memory, energy,
money, and communication cost. Computation refers to the processing power nece... | Beyond Efficiency |
a
t
o
r
,
c
u
r
r
e
n
c
y
c
o
n
v
e
r
t
e
r
,
w
e
a
t
h
e
r
A
P
I
)
.
T
h
e
y
d
i
d
a
n
e
x
p
e
r
i
m
e
n
t
o
n
f
i
n
e
-
t
u
n
i
n
g
L
L
M
t
o
c
a
l
l
a
c
a
l
c
u
l
a
t
o
r
,
u
s
i
n
g
a
r
i
t
h
m
e
t
i
c
a
s
a
t
e
s
t
c
a
s
e
.
T
h
e
i
r
e
x
p
e
r
i
m
e
n
t
s
s
h
o
w
e
d
... | LLM Powered Autonomous Agents _ Lil'Log |
Full Fine-tuning The Masked Auto-encoders (MAE) paper [He et al., 2022] re-introduced
fine-tuning as the main evaluation metrics. The main arguments are that linear-probing
is uncorrelated with fine-tuning and transfer learning performances, and that small
MLP heads do not evaluate the strength of the method to create st... | A Cookbook of Self-Supervised Learning |
[27] Floridi, L., Chiriatti, M.: Gpt-3:
limits, and conse-
quences. Minds and Machines 30, 1–14 (2020) https://doi.org/10.1007/
s11023-020-09548-1
Its nature,
scope,
[28] Dale, R.: Gpt-3: What’s it good for? Natural Language Engineering 27(1), 113–
118 (2021) https://doi.org/10.1017/S1351324920000601
[29] Bender,... | PersonalityTraitsinLargeLanguageModels |
Many fundamental challenges to developing a high quality dialog model capable of performing well in real world
applications still exist. For example, it is now increasingly well-understood that large language models trained on
unlabeled datasets will learn to imitate patterns and biases inherent in their training sets ... | LaMDA- Language Models for Dialog Applications |
biped cartoon characters, which are also in great demand
in the area of gaming (e.g., Animal Crossing), filming (e.g.,
Zootopia), and virtualizing (e.g., Metaverse). In this work,
we raise a new problem to the community: How to quickly
produce 3D biped cartoon characters from easy-to-obtain
inputs (e.g., a single image... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
1. Key Generation: The first step is to generate the decryption keys. This can be done using a cryptographically
secure pseudorandom number generator. The generated keys should be of sufficient length and complexity to
ensure that they are difficult to guess or crack.
2. Key Storage: Once the keys are generated, they ... | CodeLlama2 |
For both tasks, we use the same evaluation as for
the original LAMA dataset.
Results shown in Table 7 illustrate that Tool-
former outperforms all baselines for both TEM-
PLAMA and DATESET. However, closer inspec-
tion shows that improvements on TEMPLAMA
can not be attributed to the calendar tool, which is
only used f... | Toolformer |
68
Figure 31: Percentage of toxic responses to queries across languages. Each data point represents an identity group, with
the darker baseline group using a generic word for people. | PaLM 2 Technical Report |
To assess the generalization ability of motion planning in
our approach, we conduct a few-shot learning experiment,
where we keep other components the same and fine-tuned
the core motion planning LLM with 0.1%, 1%, 10%, 50%,
and 100% of the training data for one epoch. For comparison,
we adopted the motion planner in U... | ALanguageAgentforAutonomousDriving |
Evaluating neural toxic degeneration in language models, 2020.
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. Did aristotle use a
laptop? A question answering benchmark with implicit reasoning strategies. TACL, 2021. doi: 10.1162/
tacl_a_00370. URL https://aclanthology.org/2021.tacl... | Scaling Instruction-Finetuned Language Models |
• Social media: Platforms such as Twitter, Facebook, Red-
dit, Weibo, and others, offer a wealth of information in
terms of public sentiment, trending topics, and immediate
reactions to financial news and events.
• Filings: Websites of financial regulatory authorities, such
as the SEC in the United States, offer acces... | FinGPT-Open-SourceFinancialLargeLanguageModels |
The goal of this work is to obtain an animatable 3D head
from a video, and hence we evaluate the geometric accuracy
Method
C-Net
D-Net
B-Morph
Fwd-Skin
Ours
Expression ↓ Normals ↓
3.248
7.452
4.941
2.825
2.558
9.108
26.174
12.150
8.130
5.901
L1 ↓
0.02245
0.07881
0.03293
0.01920
0.01807
PSNR ↑
26.67
19.62
24.95
27... | I M Avatar- Implicit Morphable Head Avatars from Videos |
Notation for model sizes in DoReMi. We denote the size of the reference/proxy models (which
are always the same size in our experiments) and the size of the main model trained with DoReMi
domain weights as “DoReMi (size of reference/proxy→size of main model)”: for example, DoReMi
(280M→8B). When we are discussing the o... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
[67] B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba. Scene parsing through
ade20k dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
pages 5122–5130, 2017.
[68] X. Zhou, B. Zhang, T. Zhang, P. Zhang, J. Bao, D. Chen, Z. Zhang, and F. Wen. Cocosnet v2:
Full-resolutio... | Adding Conditional Control to Text-to-Image Diffusion Models |
*Z. Luo and S. Cai contribute equally.
†Corresponding author.
of 3DBiCar and RaBit, various applications are conducted,
including single-view reconstruction, sketch-based modeling,
and 3D cartoon animation. For the single-view reconstruc-
tion setting, we find a straightforward global mapping from
input images to the ... | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
out-of-domain text-to-speech. In NeurIPS. 2022.
69
[379] Shah, D., B. Eysenbach, G. Kahn, et al. Ving: Learning open-world navigation with visual
goals. In IEEE International Conference on Robotics and Automation, ICRA 2021, Xi’an,
China, May 30 - June 5, 2021, pages 13215–13222. IEEE, 2021.
[380] Huang, C., O. Mee... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
simply outputs the answer with no loop structure. This pattern continued in the following 2048
samples. The model solved the problem three times more often with the “number theory” tag (29
instead of 9 solutions), and output a perfect loop-free solution (other than reading the input) four
times more often (12 instead o... | alphacode |
3.3 Optimized Data Management in DRAM
Although data transfer within DRAM is more ef-
ficient compared to accessing flash memory, it
still incurs a non-negligible cost. When introduc-
ing data for new neurons, reallocating the matrix
and appending new matrices can lead to signifi-
cant overhead due to the need for rewr... | LLM in a flash |
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao,
Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch,
Adam R Brown, Adam Santoro, Aditya Gupta,
Adrià Garriga-Alonso, et al. 2022. Beyond the
imitation game: Quantifying and extrapolating the
arXiv preprint
capabilities of language models.
arXiv:2206.04615.
Aarohi Srivastava, ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
[53] Li-Chia Yang and Alexander Lerch. On the evaluation of gen-
erative models in music. Neural Computing and Applications,
2020.
[54] Xueyao Zhang, Jinchao Zhang, Yao Qiu, Li Wang, and Jie
Zhou. Structure-enhanced pop music generation via harmony-
aware learning. arXiv preprint arXiv:2109.06441, 2021.
[55] Luowei Z... | VideoBackgroundMusicGeneration |
instructions. The crowdworkers see two Claude responses per turn and choose which is better, using criteria
provided by the instructions. We then use this binary preference data to calculate Elo scores for each model
under evaluation. See our earlier papers for additional information about our data collection and evalu... | ClaudeModels |
Sarah E. Michalak, Andrew J. DuBois, Curtis B. Storlie, Heather M. Quinn, William N. Rust, David H.
DuBois, David G. Modl, Andrea Manuzzato, and Sean P. Blanchard. Assessment of the impact of
cosmic-ray-induced neutrons on hardware in the roadrunner supercomputer. IEEE Transactions on
Device and Materials Reliability, ... | gemini_1_report |
[13] Yao Feng, Vasileios Choutas, Timo Bolkart, Dimitrios
Tzionas, and Michael J. Black. Collaborative regression of
expressive bodies using moderation. In International Con-
ference on 3D Vision (3DV), pages 792–804, 2021. 6
[14] Georgios Georgakis, Ren Li, Srikrishna Karanam, Terrence
Chen, Jana Koˇseck´a, and Ziyan... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
(cid:88)
(cid:96)∈[n]
where h(cid:96) does not depend on b(cid:96). Equalizing (12), (13) we get c(cid:96)(b) = h(cid:96)(b−(cid:96))−Wela∗(b)(b−(cid:96), g(cid:96)(b, o)).
Since t(cid:96)(b, o) = c(cid:96)(b) + g(cid:96)(b, o), t(cid:96)(b, o) = h(cid:96)(b−(cid:96)) − Wela∗(b)(b−(cid:96), g(cid:96)(b, o)) + g(cid:9... | Incomplete Information VCG Contracts for Common Agency |
which gradually denoises the random noise xT towards a
realistic image, by minimizing the variational lower bound
of the negative log likelihood [31, 16]. Following the
reparameterization proposed in [31], the model consists
of time-conditioned denoising autoencoders (cid:15)θ(xt, t); t ∈
{1, 2, . . . , T}, which are t... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
4
2
0
2
n
a
J
8
]
G
L
.
s
c
[
1
v
8
8
0
4
0
.
1
0
4
2
:
v
i
X
r
a
Mixtral of Experts
Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch,
Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas,
Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour,
... | Mixtral of Experts paper |
Zalán Borsos, Raphaël Marinier, Damien Vincent,
Eugene Kharitonov, Olivier Pietquin, Matthew Shar-
ifi, Olivier Teboul, David Grangier, Marco Tagliasac-
chi, and Neil Zeghidour. 2022. AudioLM: A lan-
guage modeling approach to audio generation. CoRR,
abs/2209.03143.
Nicolas Boulanger-Lewandowski, Yoshua Bengio, and
Pa... | MOUSAI |
[60] Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman.
GLUE: A multi-task benchmark and analysis platform for natural language understanding.
In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting
Neural Networks for NLP, pages 353–355, Brussels, Belgium, N... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Preface
The history of this volume is in many ways reflective of the topics it tries to
cover. As we began assembling the chapters for this book, the 2016 US
presidential election controversy was top of mind. When it came to the effe... | Social_Media_and_Democracy |
a comprehensive analysis of the advantages and limitations of different representation learning
approaches, we aim to provide insights into how to harness their power to improve the accuracy
and robustness of speech processing systems. | AReviewofDeepLearningTechniquesforSpeechProcessing |
20
G Difficulty Breakdown
We show performance of our ORM and PRM on each quintile of the MATH
dataset. We determine quintiles based on the pass rate under the generator.
It is interesting to note that the performance gap is not only apparent on high
difficulty problems: it is in fact apparent across all difficulties... | Let’s Verify Step by Step |
VI. FURTHER DIRECTIONS
A. Lightweight Hybrid PEFT Methods | Parameter-EfficientFine-TuningMethods |
3. It won’t be the case that deployed practically PS-misaligned systems disempower humans at
a scale that constitutes existential catastrophe | not (1 or 2).
Implied probability that we’ll avoid catastrophe à la shorter negative: ~95%
Same-length positive:
Before 2070:
1. It won’t be both possible and financially fea... | Is Power-Seeking AI an Existential Risk? |
Limitations
In this survey, we provide an overview of train-
ing data management for LLMs. Despite our best
efforts, there may still be several limitations re-
maining in our work.
Lack of Technical Details The exploration of
training data management expands across a wide
range of datasets from different sources, model... | DataManagementForLargeLanguageModels-ASurvey |
laughing, crying, grinning,
• hat: Santa hat, peaked cap, steampunk hat, crown.
• expressing:
singing,
shouting, looking ahead with a very serious expres-
sion, opening mouth wide in shock, angry, talking, feel-
ing sad. | Instant3D |
origin. Descriptions of the IRA as an assembly line are supported by studies
that show Twitter handles were built into one of several groups and then used
interchangeably based on strategic goals
influencing different
demographic targets in the United States) and Twitter bans (Linvill et al.
2019). Farkas and Bastos (20... | Social_Media_and_Democracy |
16/08/2023, 14:36
The a16z Investment Thesis on AI in Bio + Health | Andreessen Horowitz
https://a16z.com/2023/06/21/ai-bio-health-thesis/
1/9 | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
4 Sequence Transformation
LLMs are capable of in-context learning the distribution of functions that represent sequence transformations
by completing abstract patterns observed among examples of input-output sequences xi =(xi
output) of
arbitrary tokens, each drawn from a fixed alphabet A. For example, suppose that we ... | LargeLanguageModelsasGeneralPatternMachines |
Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song,
John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan,
Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks,
Maribeth Rauh, Po-Sen Huang, Amelia Gla... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
We first introduce each component and explain how they contribute to the tool learning process.
Tool Set. Serving as the fundamental ingredient of tool learning, the tool set T = {T1,T2,···} contains a
collection of different tools that have different functionalities. As we have elaborated in § 2.2, a tool in T can
have... | Tool Learning with Foundation Models |
ful woman wearing a tie is watching a TV”. By repeatedly
submitting this question to ChatGPT, we collect more than
17,000 answers as our prompt set. These prompts exhibit a
wide variety of structures and contain 3,135 unique words
in total, demonstrating enhanced complexity and diversity.
We train our framework on the ... | Instant3D |
100
0.02
2.32
2.18
100
0.02
1.88
1.81
41.79
37.88
40.67
39.96
49.60
47.42
47.49
47.29
only 1/3 of the memory required for full fine-tuning, and fine-
tuning the LLaMA-13B requiring less than 1/4 of the memory
required for full fine-tuning. This advancement opens up the
possibility of fine-tuning LLMs for various down... | Parameter-EfficientFine-TuningMethods |
Base model & finetuning. We use the pretrained LLaMA model [Touvron et al., 2023] with 7B,
33B and 65B parameters as the base models for finetuning. During training, we only optimize the loss
on the output tokens, not the input tokens, thus deviating from the standard language modeling loss.
We use the same hyperparame... | Self-AlignmentwithInstructionBacktranslation |
We conduct an extensive survey through the online survey platform 6, ultimately collecting 154 valid questionnaires with
2772 votes. Within these collected questionnaires, we can calculate the proportion of times each LLM is selected for each
question, as illustrated in Fig. 25 (bottom). Finally, we aggregate the total... | Let’sThinkOutsidetheBox |
aaai conference on artificial intelligence, volume 31, 2017.
understanding. arXiv preprint arXiv:2009.03300, 2020.
[41] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks,
Johannes Welbl, Aidan Clark, et al. Training compute-op... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu. MASS: Masked sequence to sequence pre-training for
language generation. In Proc. ICML, pages 5926–5936, 2019.
Y. Tang, H. Gong, N. Dong, C. Wang, W.-N. Hsu, J. Gu, A. Baevski, X. Li, A. Mohamed, M. Auli,
et al. Unified speech-text pre-training for speech translation and ... | Translatotron3 |
As the size of pre-trained speech recognition models increases, running these large
models in low-latency or resource-constrained environments becomes challeng-
ing. In this work, we leverage pseudo-labelling to assemble a large-scale open-
source dataset which we use to distill the Whisper model into a smaller variant... | DISTIL-WHISPER |
[18] Tan M. Dinh, Anh Tuan Tran, Rang Nguyen, and Binh-Son
Hua. Hyperinverter: Improving stylegan inversion via hy-
pernetwork. In Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition, pages 11389–
11398, 2022. 2
[19] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming
transformers for h... | AddingConditionalControltoText-to-ImageDiffusionModels |
finetuning by applying it models with a range of different architectures (decoder only, encoder-decoder),
sizes (T5-80M to PaLM-540B), and pre-training objectives (causal LM, span corruption, and prefix LM + span
corruption). This finding is consistent with prior studies that demonstrated the effectiveness of instruction
fin... | Scaling Instruction-Finetuned Language Models |
based on this. Lastly, the method in Multi Query Retrieval
involves using large language models to generate multiple
search queries, these queries can be executed in parallel, and
the retrieval results are input together, which is very useful
for single problems that rely on multiple sub-problems
Embedding Transformati... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. Pubmedqa: A
In EMNLP/IJCNLP (1), pp. 2567–2577.
dataset for biomedical research question answering.
Association for Computational Linguistics, 2019.
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. Look-
ing bey... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
As an AI language model, I can suggest some ways to train an LLM using RLHF methods
based on InstructGPT to create a human assistant that exceeds open assistant in performance,
accuracy, and utility:
1. Data Collection: Collect a large amount of high-quality data that covers a wide range of
tasks and scenarios. This da... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
defined as a set of mutually independent discretized logistic variables (Kingma et al., 2016). Instead,
we propose to model every set of latent variables zi with a PC p(zi). Specifically, we adopted the
EiNet codebase (Peharz et al., 2020a) and used a PC structure similar to the one proposed by Gens
& Domingos (2013). We... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
In summary, the advantages and limitations of augmented
pre-training are evident. On the positive side, this approach
offers a more powerful foundational model, outperforming
standard GPT models in perplexity, text generation quality,
and downstream task performance. Moreover, it achieves
higher efficiency by utilizing... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Tom Brown, Benjamin Mann, Nick Ryder, Melanie
Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind
Neelakantan, Pranav Shyam, Girish Sastry, Amanda
Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen
Krueger, Tom Henighan, Rewon Child, Aditya Ramesh,
Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris
Hesse, Mark Chen, ... | Moûsai |
AI technologies are increasingly integrated into systems responsible for consequential
decision-making, including in sectors where fairness is paramount.187 Frontier AI technologies
have predictable risks when deployed in these settings.188 Bias in AI systems is particularly
concerning in high-stakes real-world doma... | Capabilities and risks from frontier AI |
unlikely to be in the future – part of the core mission of these companies. Indeed,
it can often get in the way of a platform’s profit-making mission, especially (as
has often been the case of late) if outside researchers discover problems with the
product or identify potential damage it may cause to society. | Social_Media_and_Democracy |
computed the RGB Difference between the current frame and the preceding
frame within each one-second interval. This process involves calculating the ab-
solute difference in color values for corresponding pixels across the Red, Green,
and Blue channels independently. Following this, we determined the mean of
all pi... | Video2Music |
0.95136
12.02277
18.47482
24.43656
30.80948
38.03236
46.12765
54.18826
60.97170
67.60125
Below is a derivation of Eq. (5), the reduced variance variational bound for diffusion models. This
material is from Sohl-Dickstein et al. [53]; we include it here only for completeness.
A Extended derivations
pθ(x0:T )
q(x1:T|x... | Denoising Diffusion Probabilistic Models |
IE-based Metrics. As mentioned in Section 4, IE-based metrics leverage IE models to extract
knowledge as relation tuples (subject, relation, object) from both the generation and knowledge
source to analyze the factual accuracy of the generation [61]. However, IE models are not 100%
reliable yet (making errors in the id... | SurveyofHallucinationinNatural Language Generation |
[3] Frye C, Feige I. Parenting: Safe reinforcement learning from human input. arXiv preprint
arXiv:1902.06766.
[4] Zahavy, T., Zrihem, N. Ben, & Mannor, S. (2016). Graying the black box: Understanding
DQNs. 33rd International Conference on Machine Learning (ICML) 2016, 4, 2809–2822.
Security and Safety of Cyber... | informatics-phd-projects-2022-23 |
tionalandhigh-fidelitytext-to-imagesynthesis.InProceedingsoftheIEEE/CVFCon-ferenceonComputerVisionandPatternRecognition,pages18197–18207,2022.1[44]NanLiu,ShuangLi,YilunDu,AntonioTorralba,andJoshuaB.Tenenbaum.Compositionalvisualgenerationwithcomposablediffusionmodels.2022.3[45]ChengLu,YuhaoZhou,FanBao,JianfeiChen,Chongxu... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
The evaluation of LLMs for educational assistance aims to investigate and assess their po-
tential contributions to the field of education. Such evaluations can be conducted from various
perspectives. According to Dai et al. [28], ChatGPT demonstrates the ability to generate detailed,
fluent, and coherent feedback that... | ASurveyonEvaluationofLargeLanguageModels |
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc
Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al. A general reinforcement learning algorithm
that masters chess, shogi, and go through self-play. Science, 362(6419):1140–1144, 2018.
Ishika Singh, Valts B... | Tool Learning with Foundation Models |
Original
Simplified
Original
Opposite
Related
Underspecified
Verbose
Algorithm described in words only
% correct
3.0%
15.7%
17.1%
0.1%
3.2%
0.03%
19.4%
19.7%
10@1024
13.5%
Original
≤ 6 variables consistently renamed
12.1%
≤ 6 variables inconsistently renamed 10.1%
13.3%
11.3%
10.4%
4.8%
6.9%
12.5%
8.0%
6.7%
Description... | alphacode |
Finally, we make publicly available the preprocess-
ing code for the constituent datasets of the Pile and
the code for constructing alternative versions2. In
the interest of reproducibility, we also document
all processing performed on each dataset (and the
Pile as a whole) in as much detail as possible. For
further de... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
M
(
H
o
l
i
s
t
i
c
E
v
a
l
u
a
t
i
o
n
o
f
L
a
n
g
u
a
g
e
M
o
d
e
l
s
)
,
w
h
i
c
h
h
o
p
e
f
u
l
l
y
w
i
l
l
e
v
o
l
v
e
t
o
c
a
p
t
u
r
e
m
o
r
e
g
e
n
e
r
a
t
i
v
e
,
i
n
s
t
r
u
c
t
i
o
n
-
f
o
l
l
o
w
i
n
g
s
c
e
n
a
r
i
o
s
.
S
a
f
e
t
y
:
W
e
w
o
u
l
d
l
i
k
e
t
o
f
u
r
t
... | Stanford alpha CRFM |
q4
-
99.8
97.6
98.5
98.1
96.8
94.3
99.0
98.9
98.8
85.2
89.3
91.0
87.5
89.8
81.2
72.0
68.5
88.1
p1
38.7
97.2
97.7
97.6
94.6
9.2
70.7
96.0
97.5
98.1
56.5
95.3
81.2
24.6
91.3
49.3
0
28.2
40.0
p2
33.3
95.5
95.3
96.0
90.3
94.5
69.2
93.4
95.2
95.7
34.3
81.4
57.1
6.7
89.3
49.3
0
15.7
30.0
Table 7: Success rates on TAM... | PaLM-E- An Embodied Multimodal Language Model |
Lardeau, M. & Le Floch, P. (2013). France: Press subsidies – inefficient but enduring.
In P. Murschetz (Ed.), State Aid for Newspapers, Media Business and Innovation
(pp. 000–000). Berlin: Springer-Verlag.
Lipset, S. M. (1995). American Exceptionalism: A Double-Edged Sword. New York: W.
Mailland, J., & Driscoll, K. (2... | Social_Media_and_Democracy |
Generative Models Generative models aim to
learn a lower-dimension representation space, and
then reconstruct to the high-dimension space con-
ditioning on the given information (Rombach et al.,
2022; Yang et al., 2022; Kreuk et al., 2022; Ho
et al., 2022). Some effective methods earlier in-
clude auto-encoding (Hinton... | MOUSAI |
[52] Y. Chen, D. Hazarika, M. Namazifar, Y. Liu, D. Jin, and D. Hakkani-
Tur, “Empowering parameter-efficient transfer learning by recognizing
the kernel structure in self-attention,” in Proc. Findings Assoc. Comput.
Linguistics, 2022, pp. 1375–1388.
[53] A. X. Yang, M. Robeyns, X. Wang, and L. Aitchison, “Bayesian
l... | Parameter-EfficientFine-TuningMethods |
∗Equal contribution.
2The code and results are publicly available at https://github.com/Cranial-XIX/llm-pddl.git.
A Failure Example of GPT-4 in Planning
Problem (P1): You have 5 blocks. One cannot place more than one block on another block.
b5 is on top of b3. b4 is on top of b2. b2 is on top of b1. b3 is on top of ... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
51
Asking for an explanation in German and a German dialect
Prompt:
Explain to my mother in German how large language models work and how they can be useful for society.
Response:
Große Sprachmodelle (LLMs) sind eine Art von künstlicher Intelligenz, die darauf trainiert wurde, große
Mengen an Textdaten zu verarbeit... | PaLM 2 Technical Report |
Explanation: The user has asked me to move the grapefruit drink to the counter.
Plan: 1. find(grapefruit soda), 2. pick(grapefruit soda), 3. find(counter), 4. put(grapefruit soda), 5. done().
Human: How would you bring me some snacks?
Explanation: The user has asked for snacks, I will choose two items and bring them. I w... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
5.4 Future of User Research
The rise of LLMs also has implications for conducting online studies like ours. Our data corroborates recent predictions
and emerging evidence that textual research data collected online may be partially generated by LLMs [15, 44]. We had
to exclude two participants with clearly LLM-generate... | Adoptionand AppropriationofLLMs |
Second, 2D shape cues for in-the-wild images, (body-
part segmentation masks [12,41,48], silhouettes [1,22,44])
are attractive, as these can be manually annotated or auto-
matically detected [15, 18]. However, fitting to such cues
often gives unrealistic body shapes, by inflating the body to
“explain” the clothing “bak... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
researchers, business professionals, and ethicists
to work together continuously to improve methods,
benchmark models, and set standards that put user
comprehension and authenticity first. The building
of language models that produce coherent and con-
textually relevant information while simultaneously
demonstrating he... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
null | Language models can explain neurons in language models |
[122] S. Aphiwongsophon and P. Chongstitvatana, ‘‘Detecting fake news
with machine learning method,’’ in Proc. 15th Int. Conf. Electr. Eng.,
Electron., Comput., Telecommun. Inf. Technol. (ECTI-CON), Jul. 2018,
pp. 528–531.
[123] N. Ruchansky, S. Seo, and Y. Liu, ‘‘CSI: A hybrid deep model for fake
news detection,’’ in... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
[3] A. Askell, Y. Bai, A. Chen, D. Drain, D. Ganguli, T. Henighan, A. Jones, N. Joseph, B. Mann,
N. DasSarma, N. Elhage, Z. Hatfield-Dodds, D. Hernandez, J. Kernion, K. Ndousse, C. Olsson,
D. Amodei, T. Brown, J. Clark, S. McCandlish, C. Olah, and J. Kaplan, “A General Language
Assistant as a Laboratory for Alignment.”... | ClaudeModels |
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong,
Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander
Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson,
Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason
Baldridge, and Yonghui Wu. 2022b. Scaling autoregres-
sive models for content-rich text-to-image generation.
CoRR, ... | MOUSAI |
Amendment of Section 230
263
Option One: Court-Driven Regulation via CDA 230
Since Zeran, courts have consistently found that CDA 230 provides broad
protections against online platforms being held liable for the activities of their
users. However, a set of cases suggest that, under certain circumstances, courts
may b... | Social_Media_and_Democracy |
3.5 Sequence to Sequence Models
The sequence-to-sequence (seq2seq) model in speech processing is popularly used for ASR, ST, and
TTS tasks. The general architecture of the seq2seq model involves an encoder-decoder network
that learns to map an input sequence to an output sequence of varying lengths. In the case of ASR,... | AReviewofDeepLearningTechniquesforSpeechProcessing |
testing, and red teaming.7 We refer to these adversarial testing processes informally as “red teaming”
in line with the definition given in [27], namely“a structured effort to find flaws and vulnerabilities
in a plan, organization, or technical system, often performed by dedicated ’red teams’ that seek to
adopt an attacker... | gpt-4-system-card |
3.2.1 Open vs Closed Book models
Generally, open book models refer to ’retrieve and
read’ pipelines (Chen et al., 2017) which, given a
query, 1) retrieve relevant passages from a corpus,
2) separately re-encode the passages conditioned
on the question and then 3) produce an answer.
Conversely, closed book models answer... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
[82] H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf, “Pruning
filters for efficient convnets,” in Proc. Int. Conf. Learn. Representations,
2017.
[83] K. Clark, U. Khandelwal, O. Levy, and C. D. Manning, “What does
BERT look at? an analysis of BERT’s attention,” in Proc. of 2019 ACL
Workshop BlackboxNLP, 2019... | Parameter-EfficientFine-TuningMethods |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.