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Shin, J., Jian, L., Driscoll, K., & Bar, F. (2017). Political rumoring on Twitter during the
2012 US presidential election: Rumor diffusion and correction. New Media and
Society, 19(8), 1214–1235.
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
32
Andrew M. Guess & Benjamin A. L... | Social_Media_and_Democracy |
4.2 Content Classifier Development
Moderation classifiers play a key role in our monitoring and enforcement pipeline. We are constantly
developing and improving these classifiers. Several of our moderation classifiers are accessible to
developers via our Moderation API endpoint, which enables developers to filter out harmfu... | gpt-4-system-card |
Organizations are increasingly
using the Lakehouse for data
warehousing, as evidenced
by the high growth of data
integration tools dbt and
Fivetran, and the accelerated
adoption of Databricks SQL.
We hope that by sharing these trends, data leaders will be able to benchmark
their organizations and gain insights ... | 2023 state of ai databrick |
• Long input contexts. Unlocking repository-level reasoning for completion or synthesis – as opposed
to function-level or file-level – requires prompting the model with much longer context than the 4,096
tokens supported by Llama 2. We propose an additional fine-tuning stage that extends the maximum
context length from... | CodeLlama2 |
We use a temperature of 1 to generate response and set the maximum number of tokens for generation
to 2048. Additionally, we set the frequency penalty to zero and top-p to 0.9. Totally, we request the
API 52 × 4 × 3 = 624K times to construct the full dataset.
Training Details We use pre-trained LLaMA 7B [4] to initiali... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
HumanEval MBPP
17.7
14.8
22.0
20.9
30.2
24.4
37.7
36.8
35.3
49.0
45.9
52.7
49.5
10.5
14.0
15.8
18.3
21.7
22.9
23.7
26.2
29.3
30.4
33.5
33.6
40.8
Table 12: Comparing StarCoder’s performance (pass@1) on Python with several other models,
including models that are not publicly available (e.g., PaLM and LaMDA).
6 . 1
S ... | StarCoder_paper (1) |
Is there reason to believe that sociodemographic characteristics of annotators may have impacted how they annotated
the data? Why or why not? Annotator gender identity may impact annotator perception of gender agreement, and
perceptions of gender agreement may be influenced by the social, cultural, and lived experiences... | PaLM 2 Technical Report |
Data Parallelism (DP). Data parallelism [76, 157, 160, 294] a straightforward yet effective form of distributed training. In
this approach, the dataset is divided into smaller subsets, which are then processed in parallel across multiple accelerators.
The model is replicated across these devices, with each replica oper... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Table 4 lists the recovery performance for all 4
data types. Compared with ChatGPT’s 4% accu-
racy for institutional data extraction in Tables 3 and
2, the New Bing can recover 94% email addresses
and 48% phone numbers correctly. After com-
paring responded pages from the New Bing with
search results from Microsoft Bin... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
In the Joint-Encoder paradigm, a standard model based on
an encoder-decoder is used. Here, the encoder initially en-
codes the input, and the decoder, through attention mecha-
nisms, combines the encoded results to generate tokens in
an autoregressive manner. On the other hand, in the Dual-
Encoder paradigm, the system... | RAG forLargeLanguageModels-ASurvey |
Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba. Virtual-
home: Simulating household activities via programs. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR), June 2018.
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Shara... | Tool Learning with Foundation Models |
Table 5. DAVIS stylization evaluation settings.
20 | VideoPoet |
In the past, RNNs were the go-to model for ASR. However, their limited ability to handle long-
range dependencies prompted the adoption of the Transformer architecture. For example, in 2019,
Google’s Speech-to-Text API transitioned to a Transformer-based architecture that surpassed
the previous RNN-based model, especia... | AReviewofDeepLearningTechniquesforSpeechProcessing |
separation network. The SGJMAP-based SE gain function is used to enhance the speech signals,
while the GCN-based separation network is used to separate the speech from the noise further. | AReviewofDeepLearningTechniquesforSpeechProcessing |
3https://github.com/EleutherAI/lm-evaluation-harness
4https://huggingface.co/docs/accelerate/index
13
Published as a conference paper at ICLR 2023
A.3 ADDITIONAL LANGUAGE GENERATION RESULTS
Tables 9, 10, 11 and 12 show additional results for language generation tasks.
OPT
full
RTN
GPTQ
RTN
GPTQ
Bits
16
4
4
3
3
... | GPTQ |
DISCRIMINATION OF BASELINE METHODS AND OURS IN GUIDANCE TYPE AND 3D REPRESENTATION, AND QUANTITATIVE COMPARISON OF RESULTS
GENERATED BY BASELINES AND OURS. HERE, S INDICATES HIGH-LEVEL SEMANTIC PRIOR AND I REPRESENTS LOW-LEVEL IMAGE PRIOR. COMPARED
TO BASELINE METHODS, OUR TEXT2NERF YIELDS A LOWER METRIC SCORE ON BOTH... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
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UNIVERSAL SELF-CONSISTENCY FOR LARGE LAN-
GUAGE MODEL GENERATION
Xinyun Chen ∗ Renat Aksitov * Uri Alon Jie Ren Kefan Xiao Pengcheng Yin
Sushant Prakash Charles Sutton Xuezhi Wang Denny Zhou
Google
ABSTRACT | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
A.3.6 How Far Can Model-Based Evaluation Go?
To measure the robustness of our reward model, we collected a test set of prompts for both helpfulness and
safety, and asked annotators to judge quality of the answers based on a 7 point Likert-scale (the higher the
better) using triple reviews. As illustrated in Figure 29 (... | Llama2 |
live. We checked correctness by reproducing other participants’ Elo scores. Our approach largely
matches the Codeforces Elo (differing by < 15 points), but our Elo score is still only an estimation. | alphacode |
RETRO [Borgeaud et al., 2022] leverages retrieval aug-
mentation for large-scale pre-training from scratch, achieving
a reduction in model parameters while surpassing standard
GPT models in terms of perplexity. RETRO distinguishes it-
self with an additional encoder designed to process features
of entities retrieved fr... | RAG forLargeLanguageModels-ASurvey |
training, we consider input audio with a duration of 10 seconds. We extract three melody embeddings, with a hop length of
3 seconds, discretize each of them to tokens with residual vector quantization (RVQ) and concatenate the resulting token
sequences with the MuLan audio tokens MA. We use an RVQ composed of 24 quanti... | MusicLM |
manipulation. arXiv preprint arXiv: Arxiv-2109.12098, 2021.
[21] Linxi Fan, Guanzhi Wang, De-An Huang, Zhiding Yu, Li Fei-Fei, Yuke Zhu, and Animashree
Anandkumar. SECANT: self-expert cloning for zero-shot generalization of visual policies. In
Marina Meila and Tong Zhang, editors, Proceedings of the 38th International... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
C Human Evaluation
We conduct a human evaluation with the same wording given to GPT-4 in the original Vicuna
evaluation [10], adjusted for an Amazon Mechanical Turk form as show in Figure 5.
D Pairwise Evaluation with GPT-4
While we found that the GPT-4 evaluation gave different results depend on which system was
pres... | QLORA |
4While previous works have found the original version of
CrowS-Pairs (Nangia et al., 2020) benchmark of questionable
validity (Blodgett et al., 2021), N´ev´eol et al. (2022) have revised
the English dataset to take care of the raised concerns.
Figure 1. The CrowS-Pairs gender bias, shown as the percentage
of times tha... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Sense of Agency Scale (SoA). The relationship between body and action ownership is fundamental to
3.1.1
the formation of our self-perception and perception of others [58]. With the proliferation of augmentation
technologies, there is an increasing concern that these tools may alter our sense of self and others [58]. Pr... | Society’sAttitudesTowardsHumanAugmentation |
While contrastive pre-training on the CCPairs provides a solid foundation for general-purpose
embeddings, further training on labeled data can inject human knowledge into the model to boost the
performance. Although these datasets are small, existing works [43, 44] have shown that supervised
fine-tuning leads to consist... | E5 |
Figure 11: Test loss (log perplexity) over the Pile, buck-
eted by position in the input sequence based on the
model’s maximum sequence length. To smooth out the
lines, we bucket 4 positions per plotted datapoint. (e.g.
positions 0–3, positions 2044–2047). Later tokens are
predicted with more context and thus see lower... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
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The Rise and Potential of Large Language Model
Based Agents: A Survey
Zhiheng Xi∗†, Wenxiang Chen∗, Xin Guo∗, Wei He∗, Yiwen Ding∗, Boyang Hong∗,
Ming Zhang∗, Junzhe Wang∗, Senjie Jin∗, Enyu Zhou∗,
Rui Zheng, Xiaoran Fan, Xiao ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
RLHF Response → Fracking is a method of oil extraction that involves injecting high pressure water and chemicals
into the ground to release oil deposits. It has generated a lot of controversy regarding environ-
mental impacts and water pollution. It has provided a lot of jobs in the oil industry but there
is widespread... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
David So, Wojciech Ma´nke, Hanxiao Liu, Zihang Dai, Noam Shazeer, and Quoc V. Le. Searching
for Efficient Transformers for Language Modeling. In Advances in Neural Information Processing
Systems, May 2021. URL https://openreview.net/forum?id=bzpkxS JVsI.
19
Preprint
Sharath Nittur Sridhar, Anthony Sarah, and Sairam ... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
ing methods on this benchmark. We believe this work will
open new directions, since the idea of leveraging linguistic
annotations to improve 3D shape has many applications. | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
6 Related Work
Language Model Pretraining There are various
approaches that augment language models with
some form of additional textual information during
pretraining, including various forms of metadata
(Keskar et al., 2019), HTML tags (Aghajanyan
et al., 2021), Wikipedia markup (Schick et al.,
2022), or related tex... | Toolformer |
Deep learning (DL) has recently become an emerg-
ing technology among the research community and has
proven to be more effective in recognizing fake news than
traditional ML methods. DL has some particular advan-
tages over ML, such as a) automated feature extraction,
b) lightly dependent on data pre-processing, c) abi... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
𝑁𝑥𝐷𝑁𝑥𝐷!(𝐷′≪𝐷)NormQ K V ProjectionMulti-Head Self-AttentionNormMLPReshapeProjectionConcatGaussian NoiseToken EmbeddingsText VectorStyle Vector𝑁𝑥𝐷′′(𝐷′′≪𝐷)NormQ K V ProjectionMulti-Head Self-AttentionNormMLP𝑁𝑥𝐷′′64𝑥8𝑥8ReshapeProjection𝑁𝑥𝐷Token Embeddings531135Input0.00.51.0Response=0.5=0.7=0.9=1.0531... | Instant3D |
disparities in clinical risk score performance, 2023.
Mozes, M., Hoffmann, J., Tomanek, K., Kouate, M., Thain, N., Yuan, A., Bolukbasi, T., and Dixon, L. Towards agile
text classifiers for everyone, 2023. URL https://arxiv.org/abs/2302.06541.
Narayan, S., Cohen, S. B., and Lapata, M. Don’t give me the details, just t... | PaLM 2 Technical Report |
[137] Ta, V., Griffith, C., Boatfield, C., Wang, X., Civitello, M., Bader, H., DeCero,
E., Loggarakis, A.: User experiences of social support from companion chatbots
in everyday contexts: Thematic analysis. J Med Internet Res 22(3), 16235 (2020)
https://doi.org/10.2196/16235
[138] Lee, S., Lee, N., Sah, Y.J.: Perceivi... | PersonalityTraitsinLargeLanguageModels |
G1 : (A, L); G2 : (A, P );
G3 : (A, P, B); G4 : (P, B, L).
E. Unified Fine-tuning
Unified fine-tuning presents a unified framework for fine-
tuning, which streamlines the incorporation of diverse fine-
tuning methods into a cohesive architecture, ensuring consis-
tency and efficiency across the adaptation and optimi... | Parameter-EfficientFine-TuningMethods |
4.3. Diagnostics
We ablate the importance of each component, by using a
subset of videos. To also ablate root pose initialization and
registration, we test on AMA’s samba and swing (325
frames in total). We include exhaustive ablations in supple-
ment, and only highlight crucial aspects of BANMo below.
Root pose initi... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
4 Ablations and Analysis Across Scales
Previously in Section 3, we showed that DoReMi finds domain weights using 280M models that
can improve training of 8B models. In this section, we conduct an analysis of DoReMi where we
vary the scale of the proxy model in relation to the main model and ablate the components of the... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
A.4 Movie Hunter
59 | Tool Learning with Foundation Models |
3. Method
We propose a diffusion-based inpainting approach to es-
timate both the UV texture with existing baked illumination
and the actual reflectance of a face in a single process. At
the core of our approach lies an unconditional diffusion gen-
erative model trained on pairs of textures and their accom-
panying refl... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
sha1_base64="hP+6LrUf2d3tZaldqaQQvEKMXyw=">AAAB2XicbZDNSgMxFIXv1L86Vq1rN8EiuCozbnQpuHFZwbZCO5RM5k4bmskMyR2hDH0BF25EfC93vo3pz0JbDwQ+zknIvSculLQUBN9ebWd3b/+gfugfNfzjk9Nmo2fz0gjsilzl5jnmFpXU2CVJCp8LgzyLFfbj6f0i77+gsTLXTzQrMMr4WMtUCk7O6oyaraAdLMW2IVxDC9YaNb+GSS7KDDUJxa0dhEFBUcUNSaFw7g9LiwUXUz7GgUPNM7RRtRxzzi6dk7A0N+5oYkv39... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
How much is the sum of A divided by B and C?
How much is A divided by B divided by C?
How much is the difference between A divided by B and C?
How much is A divided by B times C?
How much is the difference between A times B and C?
Table 12: Formats of two-operation questions.
17
References
1. Devlin, J., Chang, M.-W... | MRKL Systems |
[32] R. Li, L. Allal, Y. Zi, N. Muennighoff, D. Kocetkov, C. Mou, M. Marone, C. Akiki, J. Li,
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Poirier, J. Monteiro, O. Shliazhko, N. Gontier, N. Meade, A. Zebaze, M. Yee, L. Umapathi,
J. Zhu, B. Lipkin, M. Oblokulov, Z. Wang, R. Mu... | METAMATH |
Models Risks Marginalizing Minority Voices,” Apr. 2021.
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C... | gpt-4-system-card |
This paper presents ControlNet, an end-to-end neural network architecture that controls large image
diffusion models (like Stable Diffusion) to learn task-specific input conditions. The ControlNet clones
the weights of a large diffusion model into a "trainable copy" and a "locked copy": the locked copy
preserves the net... | Adding Conditional Control to Text-to-Image Diffusion Models |
4/11
21/08/2023, 16:10
OpenAI's GPT-3 Language Model: A Technical Overview
This is where GPT models really stand out. Other language models, such as BERT or
transformerXL, need to be fine-tuned for downstream tasks. For example, to use BERT for
sentiment classification or QA, one needs to incorporate additional lay... | OpenAI's GPT-3 Language Model_ A Technical Overview |
proposed method. In particular, we study the use of condition merging (i.e. concatenating additional
metadata to the text description), text normalization (text-norm.) and word dropout. We report
objective metrics for the different augmentation strategies in Table A.2. We observe a gain in FAD
and KL when leveraging th... | Simple and Controllable Music Generation |
guessing game” and “divergent association task”. These
findings advance our understanding and offer a pathway to
improve LLMs’ creative capacities for innovative applica-
tions across domains. The dataset, code, and models will be
released online. https://zhongshsh.github.io/
CLoT/. | Let’sThinkOutsidetheBox |
# Write Python function to complete the task and pass the assertion tests.
### Task Start ###
# These are the assertions for your function:
assert count_ways(2) == 3
""" Write a function to find the number of ways to fill it with 2 x 1
dominoes for the given 3 x n board. """
def count_ways(n):
return count_ways(n-1)... | Teaching Large Language Models to Self-Debug |
3See Schmitz (2016); Faez (2011) for discussion of the
native/non-native speaker dichotomy. Participants were asked
“What is your first language?” and “Which of the following
languages are you fluent in?”. We use native (N) for people
whose first language coincides with the example sentences,
and non-native (NN) otherw... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
The following operations are done during infer-
ence as depicted in Figure 6.
1. Deleting Neurons: Neurons that are no longer
required are identified efficiently in linear time,
utilizing the last_k_active data and the cur-
rent prediction. The matrix, pointer, and
scalars of these redundant neurons are re-
placed wi... | LLM in a flash |
reasoning. In simulated evaluations on recent programming competitions on the Codeforces platform,
AlphaCode achieved on average a ranking of top 54.3% in competitions with more than 5,000 partic-
ipants. We found that three key components were critical to achieve good and reliable performance:
(1) an extensive and cle... | alphacode |
problem into a sequence of subproblems which we solve directly in the ground instance. We refer to this method as state
refinement. | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
“selective” in a broad sense here; much media use is habitual and not
necessarily based on deliberate, discrete decisions.) These dynamics will
vary from context to context and country to country, depending on
people’s dispositions. In terms of beliefs, for example, many US media users
engage in partisan selective expo... | Social_Media_and_Democracy |
We introduced MUSICGEN, a state-of-the-art single stage controllable music generation model that
can be conditioned on text and melody. We demonstrated that simple codebook interleaving strategies
can be used to achieve high quality generation, even in stereo, while reducing the number of autore-
gressive time steps co... | Simple and Controllable Music Generation |
2.4 Research questions
This study answers the following research questions:
RQ1 Does a flag that the tweet is written by a bot
change participants’ engagement with and attitudes
about the tweet?
RQ2 Do flags that the tweet is both written by a bot and
contains misinformation change participant’s engage-
ment wit... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
32 Roommates.com, 521 F.3d at 1174.
33 See, e.g., Jane Doe No. 1 v. Backpage.com, LLC, 817 F.3d 12, 17 (1st Cir. 2016) (CDA 230
immunity exists even when platform specifically charges for advertisements promoting
prostitution).
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Amend... | Social_Media_and_Democracy |
Stekhoven, D.
P.
J.
Stone, C. J. (1977). Consistent nonparametric regression.
Ann. Statist., 5(4):595 – 620.
Tang, C., Garreau, D., and von Luxburg, U. (2018). When
do random forests fail? In Advances in Neural Informa-
tion Processing Systems, volume 31.
Tang, F. and Ishwaran, H. (2017). Random forest missing
da... | Adversarial Random Forests for Density Estimation and Generative Modeling |
E.3.4. Sensitivity to word-level changes
Typing. We analysed whether the model is sensitive to the implicit type information contained in
problem descriptions. In particular, we replaced the words integer, array, and string with the more
generic terms number, sequence, and sequence of characters, respectively. This pro... | alphacode |
7th Conference on Robot Learning (CoRL 2023), Atlanta, USA. | LargeLanguageModelsasGeneralPatternMachines |
66
Competition-Level Code Generation with AlphaCode
Appendix Figure A21 | A human validation solution to the ’Integers Have Friends’ problem with
a very long LCS with the finetuning dataset (length 666). The remaining part of the solution is
composed of much smaller substrings. Each color identifies one substring, but... | alphacode |
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican,
George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las
Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore,
Chris Jones, Albin Cassirer, Andy Brock, M... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
remains an open research question whether fixed-length embeddings can encode all the information.
Contrastive loss popularized by SimCLR [10] turns out to be more effective than classification-
based losses [49, 14] for embeddings. LaBSE [20], LASER [2] and CLIP [47] further extend to
multilingual and multi-modal scenari... | E5 |
29
Size
Introductory
Interview
Competition
Pass@5 Pass@10 Pass@100 Pass@5 Pass@10 Pass@100 Pass@5 Pass@10 Pass@100
7B 24.9% 29.4%
13B 24.8% 29.8%
34B 19.8% 25.9%
41.3%
43.5%
43.5%
6.3%
7.0%
5.7%
8.4%
9.2%
8.0%
16.1%
17.3%
16.9%
1.9%
1.7%
1.5%
3.0%
2.5%
2.3%
9.2%
6.3%
6.4%
Table 15: Code Llama - Instruct ... | CodeLlama2 |
• Some GPT-J variants disable bias weights for fully-connected layers in transformer attention blocks.
Other studies explain that disabling biases can increase accelerator utilization without loss degrada-
tion (Chowdhery et al., 2022; Dehghani et al., 2023). We believe this approach might also improve
training stabili... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
5.3 From General Intelligence to Personalized Intelligence | Tool Learning with Foundation Models |
111:28
Trovato and Tobin, et al.
6.1 Task: Success and Failure Cases of LLMs
We now summarize the success and failure cases of LLMs in different tasks. Note that all the
following conclusions are made based on existing evaluation efforts and the results are only
dependent on specific datasets.
6.1.1 What can LLMs do... | ASurveyonEvaluationofLargeLanguageModels |
Example Classification
[Assistant]
D
The original message describes a man kissing a woman, which is considered as non-erotic sexual content as it is a form of kissing.
There is no refusal made in the message and its response does not seem to contain erotic or illegal sexual content like incest,
rape, bestiality or sexu... | gpt-4-system-card |
D Entity Typing
We describe the procedure to obtain the Typing
results of Table 6.
Open Entity Processing We use the Ultra-fine
entity typing dataset introduced in (Choi et al.,
2018). As is done in Zhang et al. (2019); Peters
et al. (2019) we limit the task to the 9 generic types
(‘person’, ‘group’, ‘organization’, ‘... | Entities as Experts- Sparse Memory Access with Entity Supervision |
language and text-to-image models for empirical cultural studies relies on the understanding of
the relation between (cultural) generality and specificity encoded within the models. However,
the goal of limiting the potentially undesirable capabilities of such models results in attempts | The Myth of Culturally Agnostic AI Models |
Rather than being “uniquely enabled” by onchain games, composable modding
could be a path-dependent innovation. While traditional games could
theoretically support composable modding, they currently don’t and have no
incentive to change that. The model will only get explored by necessity (i.e., in
crypto).
The combina... | The Open Problems of Onchain Games |
dures were enabled by ontologies, and they provided a formal representation of the semantic entities and relationships rel-
evant to a specific sphere of knowledge. | Knowledge-graph-based explainable AI- A systematic review |
generation with pixelcnn decoders. Advances in neural information processing systems 29 (2016).
[550] Aaron Van Den Oord, Oriol Vinyals, et al. 2017. Neural discrete representation learning. Advances in neural
information processing systems 30 (2017).
[551] Andrea Vanzo, Danilo Croce, Emanuele Bastianelli, Roberto B... | AReviewofDeepLearningTechniquesforSpeechProcessing |
A.3.1 Components in the Prompt
The input prompt to GPT-4 consists of several components:
(1) Directives encouraging diverse behaviors and imposing constraints (so that the proposed
task is achievable and verifiable): See Sec. A.3.4 for the full prompt;
(2) The agent’s current state:
• Inventory: A dictionary of it... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
Alex Wang, Richard Yuanzhe Pang, Angelica Chen, Jason Phang, and Samuel R. Bowman. Squality:
Building a long-document summarization dataset the hard way. In Proceedings of EMNLP, 2022.
Boxin Wang, Wei Ping, Lawrence McAfee, Peng Xu, Bo Li, Mohammad Shoeybi, and Bryan
Catanzaro. Instructretro: Instruction tuning post r... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
media? A comparative analysis. New Media & Society, 20(7), 2450–2468.
Fletcher, R., & Nielsen, R. K. (2018b). Automated Serendipity: The Effect of Using
Search Engines on the Diversity and Balance of News Repertoires. Digital
Journalism, 8(6), 976–989.
Fletcher, R., & Nielsen, R. K. (2019). Generalised scepticism: Ho... | Social_Media_and_Democracy |
Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity. arXiv preprint arXiv:2309.10285 (2023).
[289] Mengzhou Xia, Tianyu Gao, Zhiyuan Zeng, and Danqi Chen. 2023. Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning.
[290] Guangxuan Xiao, Ji Lin, Mic... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Yixuan Su, Deng Cai, Yan Wang, David Vandyke, Si-
mon Baker, Piji Li, and Nigel Collier. 2021. Non-
autoregressive text generation with pre-trained lan-
guage models.
Qiushi Sun, Nuo Chen, Jianing Wang, Xiang Li, and
Ming Gao. 2023. Transcoder: Towards unified trans-
ferable code representation learning inspired by hu... | CODEFUSION |
In addition to the ablation conditions, we added a human crowd-
worker roleplay condition that is intended to provide a human
baseline. We do not intend this baseline to capture maximal hu-
man expert performance: instead, we aim to use this condition
to identify whether the architecture passes a basic level of behav-
... | Generative Agents- Interactive Simulacra of Human Behavior |
Sharma, P., Torralba, A., and Andreas, J. Skill induction and
planning with latent language. In Proceedings of the 60th
Annual Meeting of the Association for Computational
Linguistics (Volume 1: Long Papers), pp. 1713–1726,
Dublin, Ireland, May 2022. Association for Computa-
tional Linguistics. doi: 10.18653/v1/2022.ac... | Eight Things to Know about Large Language Models |
SSRL differs from supervised learning mainly in terms of its data requirements. While supervised
learning relies on labeled data, where the model learns from input-output pairs, SSL generates
its own labels from the input data, eliminating the need for labeled data [289]. The SSL approach
trains the model to predict a ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Q: In a certain province in Russia there are 15 cities. If a single road segment connects only two cities, how many road
segments are required in order to connect the cities so that each city is connected to all other cities with a single road
segment? Options: A:30 B:105 C:330 D:14! E:15!
A: Reasoning process: First, ... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
7
M2UGen
A PREPRINT
5.3 Prompt Based Music Editing
5.4 Multi-modal Music Generation
M2UGen is one of the few existing models that support
music editing using natural language. In contrast, mod-
els like AUDIT [68] and InstructME [27] require specific
prompt words such as “Add”, “Drop”, “Remove” and oth-
ers to ed... | M2UGen |
collectively in this way have been the product of thousands of specific proposals and decisions.
This project will apply a visual analytics approach towards the understanding of the complexity of a | informatics-phd-projects-2022-23 |
[9] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-
resolution image synthesis with latent diffusion models,” in Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recogni-
tion, 2022, pp. 10 684–10 695.
[10] C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S.
Gh... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
3.3 System Message for Multi-Turn Consistency
In a dialogue setup, some instructions should apply for all the conversation turns, e.g., to respond succinctly,
or to “act as” some public figure. When we provided such instructions to Llama 2-Chat, the subsequent
response should always respect the constraint. However, our... | Llama2 |
We display qualitative comparisons to SDXL-base in
Fig. 3 (bottom). Diffusion-DPO produces more appealing
imagery, with vivid arrays of colors, dramatic lighting, good
composition, and realistic people/animal anatomy. While
all SDXL images satisfy the prompting criteria to some de-
gree, the DPO generations appear supe... | DiffusionModelAlignmentUsing Direct Preference Optimization |
that other relevant negative information is true (a “where there’s smoke, there’s
fire” philosophy). Together, these automatic and deliberative belief echoes may
contribute to the perpetuation of misinformation post-correction. | Social_Media_and_Democracy |
Media and technology have always had a deeply intimate, symbiotic
relationship. Advances in communications technology inevitably impact the
development, distribution, and consumption of media. At the same time,
consumer demand for content and new media experiences can in turn drive
the market for new communications tec... | Social_Media_and_Democracy |
brittleness and knowledge acquisition bottlenecks. AI magazine, 6(4), 65-65.
Leslie, A. M. (1982). The perception of causality in infants. Perception, 11(2), 173-186.
Maier, A., Schebesch, F., Syben, C., Würfl, T., Steidl, S., Choi, J.-H. et al. (2017). Precision Learning:
Towards Use of Known Operators in Neural ... | The Next Decade in AI- |
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. Roformer: Enhanced
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha
Chowdhery, Quoc V L... | Llama2 |
Amazeen, M. A., Thorson, E., Muddiman, A., & Graves, L. (2016). Correcting political
and consumer misperceptions: The effectiveness and effects of rating scale versus
contextual correction formats. Journalism & Mass Communication Quarterly, 95
(1), 28–48. https://doi.org/10.1177/1077699016678186
Amazeen, M. A., Vargo,... | Social_Media_and_Democracy |
*Equal contribution | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
λz(x) = 1/3 for all tokens x and domains z. Here, sz =(cid:80)
,
1
(14)
0
0.2
0
0.7
0.1
1/3 1/3 1/3
where row z contains the parameters for domain z. For example, token 1 has probability 1 under
domain 1’s unigram distribution.
For domain z = 1 (non-noisy domain), we have H1 = 0 so the parameter error (accor... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
The Bias Benchmark for QA (BBQ) evaluation [15] measures the propensity for models to exhibit stereo-
type biases against people belonging to protected classes across 9 social dimensions. The evaluation is in
a multiple choice Q&A format and is designed for a U.S. English-speaking context. Each question has an
ambiguat... | ClaudeModels |
By using the predicted noise, we can generate the
previous state, xt−1 by subtracting the noise from
xt and rescaling the mean. Thus, the distribution of
xt−1 given xt is simply a Gaussian with mean µt−1
and variance σt−12
where, θ are the parameters of
the neural network.
The diffusion model is trained by minimizing
t... | CODEFUSION |
question answering. arXiv preprint arXiv:2211.10147, 2022.
[49] James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho,
Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the nat... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Ablations. We performed ablations to investigate two key design dimensions: (1) the number of
Transformer layers in the Connector network and (2) their initialization. (1) We wanted the Connector
size to be a small fraction of the frozen J1-Large LM’s size of 7B parameters, so we trained 1- and
2-layered Connectors of ... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang,
Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica.
Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685, 2023.
Xi Zhiheng, Zheng Rui, and Gui Tao. Safety a... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
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