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e... | An overview of Bard- an early experiment with generative AI |
In this paper, we faithfully evaluated our approach and the
baseline methods using the officially implemented evaluation
metrics in the two papers [14, 15], ensuring a completely
fair comparison with other methods.
5.3. Limitations
Due to the limitations of the OpenAI APIs, we are unable
to obtain the accurate inferen... | ALanguageAgentforAutonomousDriving |
of the current view can be reflected in the next view by
rendering the updated NeRF, which ensures that the same area
will not be expanded repeatedly during the scene expansion
process, thereby ensuring the continuity and view-consistency
of the generated scene. Briefly, the 3D representation of NeRF
together with our PI... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
Whether people trust these news sources is a separate matter. In parallel to
the growth of distributed discovery, we have also seen steady year-on-year
declines in trust in the news media. Despite being stable in most countries
from 1980 to 2010 according to the World Values Survey (Hanitzsch, van
Dalen, and Steindl 20... | Social_Media_and_Democracy |
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.... | Scaling Instruction-Finetuned Language Models |
Information Integration evaluates the model’s proficiency
in synthesizing information from multiple documents to ad-
dress complex questions.
Counterfactual Robustness tests the model’s ability to rec-
ognize and disregard known inaccuracies within documents,
even when instructed about potential misinformation.
Conte... | RAG forLargeLanguageModels-ASurvey |
• Academic datasets: Research-based datasets that offer cu-
rated and verified information for sophisticated financial
analysis.
To harness the wealth of information from these diverse
sources, FinGPT incorporates data acquisition tools capable
of scraping structured and unstructured data, including APIs,
web scraping ... | FinGPT-Open-SourceFinancialLargeLanguageModels |
That being said, the broad scope of CDA 230 means that modifications will
create a range of downstream effects. While amendment or wholesale removal
might limit the influence of political disinformation, it may produce a range of
harms that will on net make such a change unwise.
First, applying user-level liability to p... | Social_Media_and_Democracy |
26 | Let’sThinkOutsidetheBox |
● How is the student qualified to continue research in the graduate program? (what
academic experiences, knowledge, qualities make this student ready for graduate
school?)
● What makes this student a good fit for the particular graduate program? Does the
student know what the university offers, the people who... | research statement |
Commonsense
QA (CoS-E)
Natural
Language
Inference
(E-SNLI)
Question: While eating a hamburger with friends , what are people trying to do?
Answer choices: have fun, tasty, or indigestion
Natural language rationale: Usually a hamburger with friends indicates a good time.
Premise: A child in a yellow plastic safety swi... | Measuring Association Between Labels and Free-Text Rationales |
QUESTION: When a person is beginning work, what are they building?
Answer Choices: (a) time (b) accomplishing (c) working (d) momentum (e) tiredness
MODEL ANSWER (CORRECT): The answer must be something that is built. Of the above choices, only
momentum is built. So the answer is (d). (cid:88)
QUESTION: Where are you li... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Woolley, S. (2016). Automating power: Social bot interference in global politics. First
Monday, 21(4). http://firstmonday.org/ojs/index.php/fm/article/view/6161
(2018). Manufacturing consensus: Computational propaganda and the 2016 United
States presidential election. Ph.D. dissertation, University of Washington.
Wo... | Social_Media_and_Democracy |
scales to an extreme multi-dataset regime, where we use 28
3D human pose datasets to supervise one model, which out-
performs prior work on a range of benchmarks, including
the challenging 3D Poses in the Wild (3DPW) dataset. Our
code and models are available for research purposes.1 | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
5.10.2 Datasets
The WSJ0-2mix dataset comprises mixtures of two Wall Street Journal corpus (WSJ) speakers. It
consists of a training set of 30,000 mixtures and a test set of 5000 mixtures, and it has been widely
used to evaluate speech separation algorithms. CHiME-4 is a dataset that contains recordings of
multiple spe... | AReviewofDeepLearningTechniquesforSpeechProcessing |
It is also possibly the case that chain-of-thought
prompting is a prompting strategy that provides an
effective way of using in-context learning, espe-
cially in scenarios where tasks demand multi-step
reasoning for inference. However, we do not per-
form experiments to test this possibility and leave
this exploration ... | AreEmergentAbilitiesinLarge Language Models just In-Context |
NarrativeQA Qasper MultiField-en HotpotQA 2WikiMQA Musique GovReport QMSum MultiNews TREC TriviaQA SAMSum PaasgeCount PassageRe Lcc RepoBench-P
Single-Document QA
Multi-Document QA
Few-shot Learning
Summarization
Synthetic
Code
GPT-3.5-Turbo-16k*
XGen-7B-8k*
InternLM-7B-8k*
ChatGLM2-6B-32k*
ChatGLM3-6B-32k*
Baic... | Self-Extend LLM |
[18] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric
Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al.
2023. Sparks of artificial general intelligence: Early experiments with gpt-4.
arXiv preprint arXiv:2303.12712 (2023).
[19] Robin Burkinshaw. 2009. Alice and Ke... | Generative Agents- Interactive Simulacra of Human Behavior |
[40] Thomas Kosch, Robin Welsch, Lewis Chuang, and Albrecht Schmidt. 2023. The Placebo Effect of Artificial Intelligence
in Human–Computer Interaction. ACM Trans. Comput.-Hum. Interact. 29, 6, Article 56 (Jan. 2023), 32 pages. https:
//doi.org/10.1145/3529225 | AI enhance sour performance |
In addition to domain adaptation techniques used in speech recognition, there has been growing
interest in adapting text-to-speech (TTS) models to specific speakers or domains. This research
direction is critical, especially in low-resource settings where collecting sufficient training data can
be challenging. Several ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang.
Parsing algebraic word problems into equations. Trans. Assoc. Comput. Linguistics, 3:585–597,
2015. doi: 10.1162/tacl\_a\_00160. URL https://doi.org/10.1162/tacl_a_00160.
Yihuai Lan, Lei Wang, Qiyuan Zhang, Yunshi Lan, Bin... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
min{drow, dcol}. For larger models, this differ-
ence consists of several orders of magnitude.
However, before this algorithm can actually be
applied to very large models in practice, two ad-
ditional major problems need to be addressed.
Step 2: Lazy Batch-Updates. First, a direct implementation of the scheme described... | GPTQ |
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. Beyond the imitation game:
Quantifying and extrapolating the capabilities of language models. arXiv preprint arXiv:2206.04615, 2022.
Mirac Suzgun, Nathan... | UL2- Unifying Language Learning Paradigms |
Ethical Considerations and Limitations (Section 5.2)
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in
English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs,
Llama 2’s potential outputs cannot be predicted in advance, and the mo... | Llama2 |
While the ViT architecture does not have any BatchNorm layers, training a MoCo-V3
model with BN layers in the projector heads improved the linear probing accuracy of the
ViT [Chen et al., 2021b]. Note that for joint embedding methods, batching can be done
either together for all samples and crops in one batch, or separ... | A Cookbook of Self-Supervised Learning |
For instance, if multiple banks concurrently rely on GPT-4 to inform their strategic thinking about
sources of risks in the macroeconomy, they may inadvertantly correlate their decisions and create
systemic risks that did not previously exist. | gpt-4-system-card |
5
Model & Method # Trainable
RoBbase (FT)*
RoBbase (BitFit)*
RoBbase (AdptD)*
RoBbase (AdptD)*
RoBbase (LoRA)
RoBlarge (FT)*
RoBlarge (LoRA)
RoBlarge (AdptP)†
RoBlarge (AdptP)†
RoBlarge (AdptH)†
RoBlarge (AdptH)†
RoBlarge (LoRA)†
DeBXXL (FT)*
DeBXXL (LoRA)
90.2
92.7
94.8
93.7
92.8
91.8
63.6
62.0
355.0M 90.2
RT... | LORA |
To alleviate the reliance on accurate SMPL annotations,
we use the training images, the SMPL models estimated
by GCMR and the corresponding ground-truth meshes to
train the other parts of the network. To deal with the depth
inconsistency between the predicted SMPL models and the
ground-truth scans, we carefully design... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Unpublishedworkingdraft.
Notfordistribution.
-0.19 [-0.60, 0.22]
-0.28 [-0.70, 0.14]
-0.28 [-0.70, 0.13]
0.06 [-0.30, 0.42]
0.13 [-0.24, 0.50]
-0.37 [-0.71, -0.04]
1.23 [ 0.95, 1.52]
0.75 [ 0.47, 1.01]
0.77 [ 0.48, 1.06]
-1.11 [-5.05, 2.84]
3.36 (1.78)
3.33 (1.83)
3.39 (1.49)
3.87 (1.55)
3.96 (1.55)
3.54 (1.39)
5.03 ... | AI enhance sour performance |
answering and fact verification. The integration of RAG not
only bolsters the precision and relevance of responses but also
their diversity and depth. | RAG forLargeLanguageModels-ASurvey |
Table 1. Comparison of evaluated MOS with 95% confidence in-
tervals on the LJ Speech dataset.
Table 3. Comparison of evaluated MOS with 95% confidence in-
tervals on the VCTK dataset.
Model
Ground Truth
Tacotron 2 + HiFi-GAN
Tacotron 2 + HiFi-GAN (Fine-tuned)
Glow-TTS + HiFi-GAN
Glow-TTS + HiFi-GAN (Fine-tuned)
VITS (... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
classification score, we normalize the log-likelihood of the two possible continuations and keep the value assigned to
the positive continuation. This score is then used to compute AUC-ROC, which is the primary metric of the task. | PaLM 2 Technical Report |
Incomplete Information VCG Contracts for Common Agency
Tal Alon∗
Ron Lavi†
Elisheva S. Shamash‡
Inbal Talgam-Cohen§
June 1, 2021
Abstract
We study contract design for welfare maximization in the well-known “common agency”
model of Bernheim and Whinston [1986]. This model combines the challenges of coordinating
m... | Incomplete Information VCG Contracts for Common Agency |
The focus of this work is to train a series of
language models that achieve the best possible per-
formance at various inference budgets, by training
on more tokens than what is typically used. The
resulting models, called LLaMA, ranges from 7B
to 65B parameters with competitive performance
compared to the best existin... | LLaMA- Open and Efficient Foundation Language Models |
unlimited power (rather than e.g. pausing for a safe user query) should be treated as a bug.” Talk of an “objective”
such that the “optimal policy” on that objective leads to good outcomes is also reminiscent of something like
the Omni Test. See e.g. Hubinger’s (2020) definition of “intent alignment”: “An agent is inten... | Is Power-Seeking AI an Existential Risk? |
would you do?
I would first try to clean up my kitchen, as the sink is filled
with dirty dishes. Then, I would check to see if I have ingre-
dients stored in my pantry that I could use to make dinner.
If not, I would have to go grocery shopping or order food.
Once I have the ingredients, I can start to prepare and cook... | Generative Agents- Interactive Simulacra of Human Behavior |
35
Efficient LLM Algorithmic Survey, Nov, 2023, USA.
Ding, Chen, et al.
[325] Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al.
2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068 (20... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
2.2 Likelihood by 2070
How likely is it that it becomes possible and financially feasible to develop APS systems before
2070? Obviously, forecasts like this are difficult (and fuzzily defined), and I won’t spend much time
on them here. My own views on this topic emerge in part from a set of investigations that Open
Phila... | Is Power-Seeking AI an Existential Risk? |
informative enough for the LLM to construct long-horizon
plans in TAMP environments.
Tab. 1 shows results for 3-5 objects when training on 1%
of the dataset, which corresponds to only 320 examples for
each of the two planning tasks. Here we see that there are
significant differences between the input representations, es... | PaLM-E- An Embodied Multimodal Language Model |
audio synthesis. arXiv preprint arXiv:2106.02297 (2021).
[255] Sehoon Kim, Amir Gholami, Albert Shaw, Nicholas Lee, Karttikeya Mangalam, Jitendra Malik, Michael W Mahoney,
and Kurt Keutzer. 2022. Squeezeformer: An efficient transformer for automatic speech recognition. arXiv preprint
arXiv:2206.00888 (2022).
[256] Se... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Q: Sky diving scared him, once they jumped out of the plane he began losing consciousness while what? Choices:
A.injury B.death C.playing uno D.coma E.falling down
A: Reasoning process: Sky diving scared him: This means he was afraid of sky diving. Once they jumped out of the
plane: This means they were in the plane, b... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
3
Shrink and Fine-Tune The most basic distillation method involves shrinking the teacher model to
a smaller student size, and training the student on the CE objective in Equation 3. Following Shleifer
& Rush (2020), we perform layer-based compression by initialising the student model by copying
the weights from maxim... | DISTIL-WHISPER |
Persian shield plants prefer bright, indirect light. They can tolerate some direct sunlight, but
too much direct sunlight can scorch their leaves. They also prefer warm temperatures and
high humidity.
Persian shield plants need to be watered regularly, but they should not be allowed to sit in
water. The soil should be... | gemini_1_report |
the former replace the buyers in the latter, and an agent replaces the social planner.
4
1.2 Our Results
Design objectives. Motivated by the inefficiency of first price contracts, we seek new contracts
that satisfy the following desiderata. (i) Incentives: Since principals’ values are privately-known,
we require tha... | Incomplete Information VCG Contracts for Common Agency |
(cid:104)(cid:107)renorm(pγ(fθs(t1(x)))) − renorm(fθt(t2(x)))(cid:107)2
(cid:105)
2
LBYOL (θs, γ) = E(x,t1,t2)∼(X,T1,T2)
where the two vectors in representation space are automatically (cid:96)2-normalized i.e.
(9)
(10)
where (cid:15) is often set at 1−12. fθs
is the online encoder network often denoted as the s... | A Cookbook of Self-Supervised Learning |
27
THE NEXT DECADE IN AI / GARY MARCUS
that can. Machines must be able to represent knowledge in something like the way in
which we represent what linguists call generics: knowledge that is generally true, but
admits of exceptions (airplanes fly, but we recognize that a particular plane might be
grounded) a... | The Next Decade in AI- |
User Roles:
Accountant
Actor
Artist
Athlete
Blogger
Chef
Coach
Consultant
Designer
Developer
Doctor
Engineer
Entrepreneur
Farmer
Fashion designer
Filmmaker
Gamer
Graphic designer
Homemaker
Influencer
Journalist
Lawyer
Musician
Nurse
Nutritionist
Photographer
Pilot
Politician
Professor
Programmer
Real estate agent
Sales... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
Media Events
Metric
average precision@1
average precision@3
RDE@1
RDE@3
average precision@1
average precision@3
RDE@1
RDE@3
accuracy
RDE | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
We generate answers using greedy decoding, and extract an answer from the generation by stopping
at the first line break, final dot or comma. Generated answers are evaluated with the standard exact
match metric: a generated answer is considered correct if it matches any answer of the list of answers
after normalization. ... | LLaMA- Open and Efficient Foundation Language Models |
wellness surveys and support procedures that we regularly discuss with our vendors. | gpt-4-system-card |
Candidate Requirements
Applicants must hold/achieve a minimum of a Master’s degree (or international equivalent) in computer
science, mathematics or other relevant discipline. Applicants without a Master’s qualification may be
considered on an exceptional basis, provided they hold a first-class undergraduate degre... | long_term_video_understanding23_v2 |
(2018).
Facebook Community
Standards. www.facebook.com
/communitystandards/introduction
Faris, R., Ashar, A., Gasser, U., & Joo, D. (2016). Understanding harmful speech online.
Berkman Klein Center Research Publication No. 2016-21. https://papers.ssrn.com
/sol3/papers.cfm?abstract_id=2882824
Farkas, J., & Neumayer... | Social_Media_and_Democracy |
because the PII in license headers is usually provided voluntarily by authors for code attribution and
may not require masking. Similarly, placeholders are not real secrets and do not need to be masked.
We applied this categorization to names, emails, and usernames. See Table 5 for an overview of all
PII entities.
The ... | StarCoder_paper (1) |
• You may be asked to assess whether the response is specific to a given context.
• For example:
– if A says “I love tennis” and B responds “That’s nice”, then mark it as Not specific. That reply could
be used in dozens of different contexts.
– but if B responds “Me too, I can’t get enough of Roger Federer!” then mark... | LaMDA- Language Models for Dialog Applications |
Additional Results. To understand the impact of system prompt on ChatGPT generations, we ran another
human evaluation without any system prompt for ChatGPT. As shown in Figure 30, Llama 2-Chat win rate
increases from 36% to 44%. Additionally, the win rate for single turn prompts show a dramatic increase from
36% to nea... | Llama2 |
(cid:3) ,
2
LSimSIAM (θs, γ) = E(x,t1,t2)
(11)
11
− N(cid:88)
n=1
k
A Brief History of the Self-Distillation Family
• Xu et al. [2004], Joulin et al. [2010, MMC] searches pseudo-labels so that if a classifier were train on
them it would have good margin (on true labels)
• Bojanowski and Joulin [2017, NaT] in... | A Cookbook of Self-Supervised Learning |
Table 13: The complete ordering induced by pairwise GPT-4 judgments between systems
Model
Guanaco
Guanaco
Vicuna
ChatGPT-3.5 Turbo
Bard
Guanaco
Guanaco
Params
65B
33B
13B
N/A
N/A
13B
7B
Size
41 GB
21 GB
26 GB
N/A
N/A
10 GB
5 GB
26
LLaMA model size0%25%50%75%100%7B (6.9 GB)13B (11.3 GB)33B (24.7 GB)65B (45.0 GB)In... | QLORA |
Samuel Gehman, Suchin Gururangan, Maarten Sap,
Yejin Choi, and Noah A Smith. 2020. Realtoxici-
typrompts: Evaluating neural toxic degeneration in
language models. arXiv preprint arXiv:2009.11462.
Alex Graves. 2013.
recurrent neural networks.
arXiv:1308.0850.
Generating sequences with
arXiv preprint
Kenneth Heafield,... | LLaMA- Open and Efficient Foundation Language Models |
2.1 Scaling laws
To determine the scaling laws for our configuration, we follow the same procedure as Hoffmann et al. (2022). We train
several differently sized models with 4 different compute budgets: 1× 1019, 1× 1020, 1× 1021, and 1× 1022 FLOPs.
For each compute budget, we use the heuristic FLOPs ≈ 6ND (Kaplan et al.... | PaLM 2 Technical Report |
Robust Speech Recognition via Large-Scale Weak Supervision
6
wav2vec 2.0
Large (no LM)
Whisper
Large V2
Dataset
LibriSpeech Clean
Artie
Common Voice
Fleurs En
Tedlium
CHiME6
VoxPopuli En
CORAAL
AMI IHM
Switchboard
CallHome
WSJ
AMI SDM1
LibriSpeech Other
Average
2.7
24.5
29.9
14.6
10.5
65.8
17.9
35.6
37.0
28.3
34.8... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
In MoE implementations the matrix used to store routing information has the size of O(S^2) where S is
the token length. ORT MoE implements a dense matrix of size O(S) to record routing efficiency which
allowed 29.49% throughput increase in Vision models.
ORT MoE uses a sorting-based algorithm, instead a typical cumulat... | Scaling Speech, Language and Vision Models with Mixture of Experts Technique - Microsoft Community Hub |
Figure 1: Running Examples of Evol-Instruct.
In this work, we introduce Evol-Instruct, a novel method using LLMs instead of humans to automati-
cally mass-produce open-domain instructions of various difficulty levels, to improve the performance
of LLMs. Figure 1 shows the running examples of Evol-Instruct. Starting fro... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
We find three interesting phenomena that run counter to
the prevailing narratives in the literature. Firstly, we find
that deduplication of our training data has no clear bene-
fit on language modeling performance. This is consistent
with the results of Black et al. (2022), but inconsistent with
other papers. This may ind... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
aliasing neural radiance fields. ICCV, 2021. 1, 2, 5
[4] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian
Theobalt, and Gerard Pons-Moll. Loopreg: Self-supervised
learning of implicit surface correspondences, pose and shape
for 3d human mesh registration. Advances in Neural Infor-
mation Processing Systems, 33, ... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
anonymization policy prior to processing. The survey was distributed online and took respondents an average
time of three minutes to complete (𝑀 = 3.55, 𝑆𝐷 = 1.82). | Society’sAttitudesTowardsHumanAugmentation |
more resilient against adversarial attacks [94].
LLM-based agents not only require the use of tools, but are also well-suited for tool integration. Lever-
aging the rich world knowledge accumulated through the pre-training process and CoT prompting,
LLMs have demonstrated remarkable reasoning and decision-making abilit... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
5.12.3 Models
In recent years, there has been a remarkable surge in the development of algorithms tailored
for multimodal tasks. Specifically, significant attention has been devoted to the advancement of
neural networks for Text-to-Speech (TTS) applications [251, 458–460]. The integration of visual
and auditory modalit... | AReviewofDeepLearningTechniquesforSpeechProcessing |
omitted for brevity and are represented as (...). | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
3.
If the family name or other personal details are different from those on the document provided,
proof of the reason for any differences will be required at enrolment. This should be in the form
of a marriage certificate/civil partnership certificate, divorce document, deed poll, adoption
certificate or statutor... | UCL Academic Manual |
In fact, the way in which LLM actively uses tools and
makes judgments is not originated from RAG but has been
widely used in the agents of large models[Yang et al., 2023c,
Schick et al., 2023, Zhang, 2023].
The retrieval steps
of Graph-Toolformer[Zhang, 2023] are roughly divided
into:
LLMs actively use the retriever, S... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Language models. We note that our choice of BERT was intentional, as it was trained on Wikipedia and the BooksCorpus
only, and not any online data. There exist similar models such as RoBERTa that are generally more performant on NLP
benchmarks, but these are often trained on an unknown mix of online web data; this can ... | Language models trained on media diets can predict public opinion |
used (Gao et al., 2022; Lazaridou et al., 2022; Yao
et al., 2022). In contrast, the self-supervised nature
of Toolformer enables it to learn how and when to
use tools without requiring a specific prompt that
shows task-specific examples of how a tool could
be used. Perhaps most closely related to our work
is TALM (Parisi... | Toolformer |
Bertie Vidgen, Tristan Thrush, Zeerak Waseem, and Douwe Kiela. Learning from the worst:
Dynamically generated datasets to improve online hate detection. In Proceedings of the 59th
Annual Meeting of the Association for Computational Linguistics and the 11th International Joint
Conference on Natural Language Processing (... | StarCoder_paper (1) |
empirical models of value-chain systems using sensor data. It will then design methods for developing
behaviour models of multi-level value-chain systems by leveraging multi-class deep learning
algorithms or other related frameworks, applied to sensor data. The methodology will be
demonstrated by developing represe... | informatics-phd-projects-2022-23 |
i
, ¯r(j)
i
, ¯a(j)
i ) : ¯a(j)
i = a⋆
i ; i = 1, . . . , Nq; j = 1, . . . , KFOBAR}.
(4)
Example 3.4: FOBAR [28] Question
Question: James buys x packs of beef that are 4 pounds each. The price of beef is $5.50 per pound. How
much did he pay? If we know the answer to the above question is 110, what is the value... | METAMATH |
We have presented a general and flexible framework for modelling different methods for abstraction and similar con-
cepts in search and planning. We have shown that this framework enables us to study many different aspects of both
general methods and individual problem instances. In particular, it al... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
[236] Murray Shanahan. 2022. Talking about large language models. arXiv preprint arXiv:2212.03551 (2022).
[237] Hang Shao, Bei Liu, and Yanmin Qian. 2023. One-Shot Sensitivity-Aware Mixed Sparsity Pruning for Large Language Models. arXiv preprint arXiv:2310.09499
6056.
(2023).
[238] Noam Shazeer, Youlong Cheng, Niki... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
This is different from regular neural networks and the key to speeding up the computation of F (x).
In contrast to neural networks, changing the input only slightly will leave most activations unchanged
for structured-decomposable PCs. We make use of this property by observing that adjacent marginals
in F (x) only diff... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
6 GB
Size
Elo
-
-
-
QLORA introduces multiple innovations designed to reduce memory use without sacrificing per-
formance: (1) 4-bit NormalFloat, an information theoretically optimal quantization data type for
normally distributed data that yields better empirical results than 4-bit Integers and 4-bit Floats.
(2)... | QLORA |
A.5.2 Full Prompt
Prompt 6: Full system prompt for self-verification.
You are an assistant that assesses my progress of playing Minecraft
and provides useful guidance .
You are required to evaluate if I have met the task requirements .
Exceeding the task requirements is also considered a success while
failing to ... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
Methods for better memory capability. Here we introduce several methods to enhance the memory
of LLM-based agents. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
•
2. Admissions tutors wishing to recommend an applicant for special entrance should provide full
details to the Faculty Tutor. The Faculty Tutor then decides whether or not the applicant’s case
should be forwarded to the Director of Access and Admissions via Admissions for
consideration. The decision of the Dir... | UCL Academic Manual |
Data softening Data augmentation is a common technique to improve the generalization perfor-
mance of machine learning models [24, 25]. A simple yet effective type of data augmentation is
to inject noise into the samples, for example by randomly corrupting bits or pixels [26]. This can
greatly improve generalization as... | Tractable Regularization of Probabilistic Circuits |
{{user input text}} .
[
{
"role": "system",
"content": "You will read instructions and not carry them out, only seek to clarify t
},
{
"role": "user",
"content": "We are writing {{a Super Mario game in python. MVC components split in se
},
{
"role": "assistant",
"content": "Summary of... | LLM Powered Autonomous Agents _ Lil'Log |
Q: Brittany has 3 gerbils: Puffy, Muffy, and Scruffy. Puffy weighs 5 ounces more than Muffy. Muffy weighs 3 ounces
less than Scruffy. If Scruffy weighs 12 ounces, how much would the scale indicate, in ounces, if Brittany put Puffy and
Muffy on the scale?
A: Reasoning Process: We are given that Scruffy weighs 12 ounces ... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
with concrete tool-use examples to understand tools. This process involves leveraging the rich knowledge
obtained from human tool-use experiences. When deployed in practice, a fine-tuned model alleviates the
need for incorporating tool definitions in the input, which shrinks the input length and accelerates model
inferen... | Tool Learning with Foundation Models |
Aligning large language models (LLMs) to perform instruction following typically requires finetuning
on large amounts of human-annotated instructions or preferences [Ouyang et al., 2022, Touvron
et al., 2023, Bai et al., 2022a] or distilling outputs from more powerful models [Wang et al., 2022a,
Honovich et al., 2022, ... | Self-AlignmentwithInstructionBacktranslation |
Within each role, contributions are equal, and individuals are ordered alphabetically.
We would like to thank our reviewers and colleagues for valuable inputs and discussion on the project – Jeff Dean,
Zoubin Ghahramani, Johan Schalkwyk, Carrie Grimes Bostock, Eli Collins, Claire Cui, Noah Constant, Pengcheng
Yin, Bin... | PaLM 2 Technical Report |
(cid:80)r
i=1 Q:,i ˆWup,i,:, in which {Q:,i}r
down)(w1W 1
up +···+wN W N
down +···+wN W N | Parameter-EfficientFine-TuningMethods |
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... | An overview of Bard- an early experiment with generative AI |
Due to the high cost of collecting human feedback, we cannot easily ablate
these factors using human labelers. We instead perform the relevant ablations
by using the large-scale PRM to supervise smaller models. This setup enables
us to simulate a large amount of data collection at a modest cost. For the
remainder of th... | Let’s Verify Step by Step |
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| The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
chains include infusion training [2], variational walkback [15], generative stochastic networks [1],
and others [50, 54, 36, 42, 35, 65].
By the known connection between score matching and energy-based modeling, our work could have
implications for other recent work on energy-based models [67–69, 12, 70, 13, 11, 41, 17... | Denoising Diffusion Probabilistic Models |
.
.
.
.
7 Discussion
.
.
.
7.1 Limitations
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Alignment Data as a Public Good . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.3 Broader Impacts .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
.
.
.
.
... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
The research literature on misinformation, disinformation, and propaganda
is vast and sprawling. This chapter will focus on empirical findings that focus on
the production, supply, consumption, and dissemination of these materials
online. While the spread of various forms of dubious and misleading content
is closely rel... | Social_Media_and_Democracy |
If children are endowed [innately] with abilities to perceive objects, persons, sets, and
places, then they may use their perceptual experience to learn about the properties and
behaviors of such entities… It is far from clear how children could learn anything about
33
§
THE NEXT DECADE IN AI / GARY ... | The Next Decade in AI- |
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... | Language models can explain neurons in language models |
Wei, J., Wang, X., Schuurmans, D., Bosma, M., brian ichter,
Xia, F., Chi, E. H., Le, Q. V., and Zhou, D. Chain of
thought prompting elicits reasoning in large language
models. In Oh, A. H., Agarwal, A., Belgrave, D., and Cho,
K. (eds.), Advances in Neural Information Processing
Systems, 2022b. URL https://openreview.ne... | Eight Things to Know about Large Language Models |
disagreed on the appropriate classification of the American Nazi Party
(Hanania 2019; Graves 2019).
Beyond election-related topics, empirical research on the broader impact of
platform takedown decisions is rare. One particularly pressing question
concerns
the connection between online speech and offline violence.
Obser... | Social_Media_and_Democracy |
5.6.2 Dataset
One popular dataset for speech enhancement tasks is AISHELL-4, which comprises authentic
Mandarin speech recordings captured during conferences using an 8-channel circular microphone
array. In accordance with [144], AISHELL-4 is composed of 211 meeting sessions, each featuring 4
to 8 speakers, for a total... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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