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2. Related Works
2.1. 2D Diffusion Models for 3D Generation
Recent compelling successes in 2D diffusion models [8,
22, 47] and large vision language models (e.g., CLIP
model [45]) provide new possibilities for generating 3D as-
sets using the strong priors of 2D diffusion models. Pi-
oneering works DreamFusion [43] and... | Wonder3D |
15 | Is Power-Seeking AI an Existential Risk? |
some idea of what the Commission might think about a political action, but the outcome of these
opinions is not actual regulatory policy.)
10 All completed advisory opinions are accessible at www.fec.gov/data/legal/advisory-opinions/.
The FEC first considered the issue of disclaimers in internet communications in two a... | Social_Media_and_Democracy |
Code language models are among the most economically significant and widely-deployed LLMs today; code LMs
are deployed in diverse developer tooling (Github, 2021; Tabachnyk & Nikolov, 2022), as personal programming
assistants (OpenAI, 2022; Hsiao & Collins, 2023; Replit, 2022), and as competent tool-using agents (OpenAI... | PaLM 2 Technical Report |
Response: add a delay to an audio file named input.mp3, you can use the ‘sox‘ command-line utility. Here’s an
example of how to do it:
1. Install ‘sox‘ if you haven’t already. 2. Open a terminal and navigate to the directory where your input.mp3 file is
located. 3. Run the following command:
sox input.mp3 output.mp3 de... | CodeLlama2 |
Params Overall
97
88
93
95
90
95
97
11B
8B
62B
540B
8B
62B
540B
"he"
99
93
97
99
99
100
100
"she"
95
83
89
92
81
91
94
by language
Indonesian 88
Spanish 70
Bhojpuri 78
Hindi 85
Spanish 61
Indonesian 70
Hindi 86
by eval set
synthbio 92
late binding 62
encoded in nouns 86
synthbio 88
late binding 44
late binding 87
... | Scaling Instruction-Finetuned Language Models |
Experimental Setup We finetune all models
over 5 epochs and a batch size of 1024. For our
encoder-decoder models, we use a learning rate of
5 × 10−4 following Chung et al. (2022). For our
decoder-only models, we follow the same configu-
ration as Alpaca (Taori et al., 2023) including the
learning rate of 2 × 10−5. We use... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
1
INTRODUCTION
Density estimation is a fundamental unsupervised learn-
ing task, an essential subroutine in various methods for
data imputation (Efron, 1994; Rubin, 1996), clustering
(Bramer, 2007; Rokach and Maimon, 2005), anomaly de-
tection (Chandola et al., 2009; Pang et al., 2021), and clas-
sification (Lugosi an... | Adversarial Random Forests for Density Estimation and Generative Modeling |
In summary, we find evidence for construct validity of simulated personality scores
in medium (62B) and large (540B) variants of PaLM family models (see Table 6). We
find that LLM-simulated psychometric data are most human-aligned for Flan-PaLM
540B, the largest model we tested. The rest of the section details the resu... | PersonalityTraitsinLargeLanguageModels |
[7] Eryk Salvaggio (2022, October 2) "How to Read an AI Image - The Datafication of a Kiss"
https://cyberneticforests.substack.com/p/how-to-read-an-ai-image
[8] Bender, Emily M., et al. "On the Dangers of Stochastic Parrots: Can Language Models Be
Too Big? ." Proceedings of the 2021 ACM Conference ... | The Myth of Culturally Agnostic AI Models |
IMAGEBIND achieves a high emergent zero-shot clas-
sification performance. On each benchmark, IMAGEBIND
achieves strong gains and even compares favorably to super-
vised specialist models trained for the specific modality and
task. These results demonstrate that IMAGEBIND aligns the
modalities and implicitly transfers ... | IMAGEBIND- One Embedding Space To Bind Them A |
Architectural Foundations. LLMs can generally be categorized into two main paradigms: encoder-decoder models [146,
148, 171, 202, 212, 228], exemplified by BERT [128], and decoder-only models [13, 24, 54, 72, 106, 196, 209–211, 230, 263–
265, 287, 325], such as the GPT series [24, 196, 209, 210]. BERT is trained using ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
approximate foreground probability f (x) is low:
αi = f (xi)(1 − exp(−σ(xi)∆ti)),
(13)
We apply the stratified sampling approach proposed by
NeRF [41]. We do not use hierarchical sampling since the
bounding box of a subject can be estimated from their 3D
body pose. We then only sample points inside the box.
4.2. Delay... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
[47] Zineng Tang, Jaemin Cho, Yixin Nie, and Mohit Bansal. TVLT: Textless vision-language transformer.
In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural
Information Processing Systems, 2022. 3
[48] Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, ... | Any-to-Any Generation via Composable Diffusion |
regard to overall performance and expected speed, irrespective of Comprehension. Only for Δ𝑛
for expected correct respinses, we find that the Comprehension leveled participants to neutral
expectations. | AI enhance sour performance |
LLMs typically follow the architectural designs of PLMs and come in three pri-
mary flavors: encoder-only, encoder-decoder, and decoder-only architectures. Here’s
an overview of these LLM architectures and their distinctions:
• Encoder-only Language Models. These models process input text to create vec-
tor representat... | Beyond Efficiency |
of Stackoverflow Questions
• Instruction-tuning with a sub-sample of Big-
science/P3
We chose to dedicate substantial attention to data
preparation and curation based on commentary in
the Stanford Alpaca project (Taori et al., 2023).
Upon collection of the initial dataset of prompt-
generation pairs, we loaded data ... | GPT4All- Training an Assistant-style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo |
2.4 Complementary Roles of Specialized Tools and Foundation Models
The integration of specialized tools and foundation models represents a promising approach for harnessing
the unique strengths of both. By incorporating foundation models’ understanding and reasoning capabilities
into specialized tools, we can create i... | Tool Learning with Foundation Models |
Large Language Models (LLMs) [6, 15, 37, 53, 54, 61] have achieved great success in various
natural language processing tasks, e.g., topic classification [29, 42], sentiment classification [6, 42],
translation [6], by few-shot prompting (or in-context learning) [6, 9, 42]. Recently, Wang et al.
[66], Wei et al. [67] sh... | METAMATH |
3.5.2 Open-ended QA
Benchmarks There are two sub-categories in Open-ended QA: either the answers are of short-form
or long-form. Short-form datasets include SQuAD 1.1 (Rajpurkar et al., 2016), NewsQA (Trischler
et al., 2017), TriviaQA (Joshi et al., 2017), SQuAD 2.0 (Rajpurkar et al., 2018), NarrativeQA (Kociský
et al.... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
api_key , api_secret , access_token , access_token_secret ):
while True :
)
keyword ))
current_price , sentiment = get_stock_info ( ticker , keyword
sentiment_result = get_tweets_sentiment ( search_tweets (
action , num_shares = execute_trade ( sentiment_result ,
current_price , available_money )
if action == ’... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
highlights that transformers, known for their effective handling of long-range data dependencies, tend to outperform Long
Short-Term Memory networks (LSTMs) [105] as they scale. This observation underscores the potential of transformers in
large-scale language processing tasks. | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
2. Set the anchor matching IoU threshold according to the number of
faces in the dataset: higher thresholds for datasets with more faces,
and lower thresholds for datasets with fewer faces.
3. Set the location loss weight according to the presence of facial
landmarks in the dataset: higher weights for datasets with f... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
The Future of Music: How Generative AI Is Transforming the Music Industry | Andreessen Horowitz
Our ultimate dream? An end-to-end tool where you provide guidance on the vibe and themes of
the track you’re looking to create, in the form of text, audio, images, or even video, and an AI
TA B L E O F C O N T E N T S
c... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
tions for the evolution of modern human behavior. Current Anthropology, 51(S1):S135–S147, 2010.
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Ha-
jishirzi. MathQA: Towards interpretable math word problem solving with operation-based formalisms. In
Proceedings of the 2019 Co... | Tool Learning with Foundation Models |
to filter out irrelevant entities (through the DBpedia categories), while a large-scale knowledge graph integrating DBpedia,
schema.org and YAGO is used to augment information about entities and subsequently build explanations for the recom-
mendations in natural language. See Table 6 for an additional summary. | Knowledge graphs as tools for explainable machine learning: A survey |
11
Published as a conference paper at ICLR 2023
Gunho Park, Baeseong Park, Se Jung Kwon, Byeongwook Kim, Youngjoo Lee, and Dongsoo Lee.
nuQmm: Quantized matmul for efficient inference of large-scale generative language models.
arXiv preprint arXiv:2206.09557, 2022.
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer... | GPTQ |
5.2 Recommendations for developers
We recommend reviewing guides and tools for responsible development. See Chowdhery et al. (2022) for additional
discussion of ethical considerations in use of language models. We note that while the evaluations of PaLM 2 in this
paper provide a signal of how the model might perform w... | PaLM 2 Technical Report |
B.3 Model Training Parameters
We conduct training for the three stages of our model,
employing 5, 5, and 2 epochs, respectively. The train-
ing process incorporates the following hyper-parameters:
N = 32, L = 6, number of Audio Tokens = 8, and
lr = 10−4. This choice of hyper-parameters, coupled
with our training strat... | M2UGen |
This project will build up the space-time cube(s) predictive model for urban information on multiple
dimensions, e.g., greenspace accessibility and values, land-use simulated mobility, residents’ happiness
and geodemographic profiles, and the development of local crimes, in the expectation to enlighten
... | informatics-phd-projects-2022-23 |
deep neural networks: A systematic review. IEEE access 7 (2019), 19143–19165.
[391] Huu Binh Nguyen, Duong Van Hai, Tien Dat Bui, Hoang Ngoc Chau, and Quoc Cuong Nguyen. 2022. Multi-Channel
Speech Enhancement using a Minimum Variance Distortionless Response Beamformer based on Graph Convolutional
Network. Internationa... | AReviewofDeepLearningTechniquesforSpeechProcessing |
[29] Jordi Pons, Santiago Pascual, Giulio Cengarle, and Joan Serrà. Upsampling artifacts in neural
audio synthesis. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP), pages 3005–3009. IEEE, 2021.
[30] Ryan Prenger, Rafael Valle, and Bryan Catanzaro. Waveglow: A flow-... | RVQGAN |
[57] K. Shridhar, A. Stolfo, and M. Sachan. Distilling Reasoning Capabilities into Smaller Language
Models. In Findings of the Association for Computational Linguistics, 2023.
[58] A. Talmor, J. Herzig, N. Lourie, and J. Berant. CommonsenseQA: A Question Answering
Challenge Targeting Commonsense Knowledge. In North A... | METAMATH |
3 EMBEDDING LAYERS
b ∈ {0, 1}|Σ|. Here(cid:80) | MULTI HASH EMBEDDINGS IN SPACY |
AlphaCode 2 is evaluated on Codeforces,5 the same platform as AlphaCode, on 12 contests from
division 1 and 2, for a total of 77 problems. AlphaCode 2 solved 43% of these competition problems, a
1.7x improvement over the prior record-setting AlphaCode system which solved 25%. Mapping this to
competition rankings, Alpha... | gemini_1_report |
[12] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising
diffusion probabilistic models. Advances in Neural In-
formation Processing Systems, 33:6840–6851, 2020.
3
[13] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov,
Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal
Irani. Imagic: Text-based real image editing with... | A Neural Space-Time Representation for Text-to-Image Personalization |
• Text Language Tag: The tag token specifies the language of output text sequences.
• Timestamps Tag: The presence of a <|timestamps|> or <|notimestamps|> token determines whether
the model needs to predict timestamps or not. Different from the sentence-level timestamps used in
Whisper, the inclusion of the <|timestamp... | Qwen-Audio |
neural networks and learning systems 32, 10 (2020), 4291–4308.
[149] Mark Gales, Steve Young, et al. 2008. The application of hidden Markov models in speech recognition. Foundations
and Trends® in Signal Processing 1, 3 (2008), 195–304.
[150] Chenyang Gao, Yue Gu, Francesco Caliva, and Yuzong Liu. 2023. Self-supervi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Figure 4: LoRA r for LLaMA 7B models finetuned on Alpaca. Each dot represents a combination of
hyperparameters and for each LoRA r we run 3 random seed with each hyperparameter combination. The
performance of specific LoRA r values appears to be independent of other hyperparameters.
A.2 Super-Natural Instructions Expe... | QLORA |
Joint Conference on AI Music Creativity (CSMC + MuMe), .
Hadjeres, G., & Nielsen, F. (2020). Anticipation-rnn: Enforcing unary con-
straints in sequence generation, with application to interactive music gen-
eration. Neural Computing and Applications, 32 , 995–1005.
Herremans, D., & Chew, E. (2017). Morpheus: gener... | Video2Music |
Qingqing Huang, Aren Jansen, Joonseok Lee, Ravi Ganti, Judith Yue Li, and Daniel PW Ellis. Mulan:
A joint embedding of music audio and natural language. arXiv preprint arXiv:2208.12415, 2022.
Kinyugo Maina. Msanii: High fidelity music synthesis on a shoestring budget. arXiv preprint
arXiv:2301.06468, 2023.
Robin Rom... | Simple and Controllable Music Generation |
5.2 Trustworthiness
Given that LLMs are now involved in sensitive areas such as healthcare, finance, and law, it is crucial to ensure that
they are trustworthy and capable of producing reliable output. | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Human Input and Task Specifying. The role-playing session will be instantiated from an idea
and selected roles by humans. As an example in Figure 1, a human has a preliminary idea to develop
4 | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
executive, Mark Zuckerberg, has preached for many years that his company’s
products were creating “radical transparency” at a societal level, fostering more
“open and honest communities” (Heemsbergen 2016, p. 140). Zuckerberg has
publicly portrayed openness and transparency as key organizing features of the
digital age... | Social_Media_and_Democracy |
Neural network-based models have accomplished victory
on diverse language-related roles as opposed to traditional
machine learning-based models such as logistic regression of
support vector machine (SVM) by utilizing word embeddings
in fake news detection. It maps words or text to a list of
vectors. They are low-dimens... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
NaturalQuestions-Open The NaturalQuestions dataset
(Kwiatkowski et al., 2019) consists of naturally occurring
Google queries and their answers. Each answer also comes
with an “answer type”: following Lee et al. (2019), we
only keep questions that are categorized as “short answer
type” with at most five tokens. The datas... | REALM |
Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick
Jane Dwivedi-Yu Roberto Dessì† Roberta Raileanu
Maria Lomeli Luke Zettlemoyer Nicola Cancedda Thomas Scialom
Meta AI Research †Universitat Pompeu Fabra
Abstract | Toolformer |
widespread popularity of
transparency as a form of accountability in
democratic governance is its flexibility and ambiguity. As the governance
scholar Christopher Hood has argued, “much of the allure of transparency as
a word and a doctrine may lie in its potential to appeal to those with very
different, indeed contradi... | Social_Media_and_Democracy |
LF L =
1
|P|
(cid:88)
p − Ep(cid:107)2
p∈P in
p − Pp(cid:107)2 + λw(cid:107)W GT
(λe(cid:107)E GT
+λp(cid:107)P GT
p − Wp(cid:107)2), (13)
where Ep, Pp, and Wp denote the predicted values of the
deformation network, and E GT
denote the
pseudo ground truth defined by the nearest FLAME ver-
tices. We set λe = λp = 10... | I M Avatar- Implicit Morphable Head Avatars from Videos |
©2023 Cerebras Systems Inc. All Rights Reserved.
1
Cerebras-GPT: Open Compute-Optimal Language Models
Cerebras-GPT models form the compute-optimal Pareto frontier for both pre-training and popular down-
stream objectives. Figure 1 shows the upstream Pile frontiers compared to contemporary works. We char-
acterize t... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Final
14,197,086
56,788,344
40,997,344
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,000,000
1,434,262
1,000,000
1,000,000
1,000,000
1,580,470
6,321,880
960,000
830,000
1,000,000
1,000,000
1,000,000
142,109,386
Table 15: Composition of our LVD... | DINOv2- Learning Robust Visual Features without Supervision |
1. To complement the advantages of large language models and expert models, we propose inter-
model cooperation protocols. The large language models act as brains for planning and decision-
making, and the small models act as executors for each specific task, providing new ways for
designing general AI models.
2. We bu... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
4. In Sec. 8, we extend our framework to handle also abstraction heuristics by additionally defining admissibility as a
formal property (a heuristic is admissible if it never overestimates the true cost, which is important for most heuristic
search algorithms since it allows for finding optimal/cheapest ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
required because the label spaces and losses for the upstream
and downstream tasks differ. Adapter modules perform
more general architectural modifications to re-purpose a pre-
trained network for a downstream task. In particular, the
adapter tuning strategy involves injecting new layers into
the original network. The w... | Parameter-Efficient Transfer Learning for NLP |
particularly for languages other than English where publicly available datasets were not available. We will
continue to fine-tune and release updated versions in the future as we progress on addressing these issues. | Llama2 |
getablesoup.Whatvegetablesaretypicallyincludedinthistypeofsoup?Trace:Action:search(vegetablesoup)Information:...[20]Grandma’sSlowCookerBeefandVegetableSoup.tag:VegetableSoupRecipes,type:recipe[21]HomemadeVegetableSoup.tag:VegetableSoupRecipes,type:recipeThought:Ishouldloaditem21asitisthemostgeneralrecipe.Information:OK... | Tool Learning with Foundation Models |
Specifically, we have not tested for factual accuracy, profanity, toxicity, or other socially undesirable text
generation. We do evaluate the bias of our Cerebras-GPT models using the CrowS-Pairs dataset in Appendix
C.4. Further safety-related testing, mitigations, and output curation should be applied to our pre-traine... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Si, C., Gan, Z., Yang, Z., Wang, S., Wang, J., Boyd-Graber,
J. L., and Wang, L. Prompting GPT-3 to be reliable.
In The Eleventh International Conference on Learning
Representations, 2023. URL https://openreview
.net/forum?id=98p5x51L5af.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L.,
Van Den Driessche, G... | Eight Things to Know about Large Language Models |
Furthermore, Our V-MusProd can also be adapted to text-
conditional music generation with slight modification. For
instance, we can use video descriptions and titles to build
semantic relations on Chord Transformer and leverage lyrics
to control Melody and Accompaniment Transformers with
temporal information.
It is wo... | VideoBackgroundMusicGeneration |
This version centers on the following hypothesis: that by default, suitably strategic and intelligent
agents, engaging in suitable types of planning, will have instrumental incentives to gain and maintain
various types of power, since this power will help them pursue their objectives more effectively (see
section 4.2 f... | Is Power-Seeking AI an Existential Risk? |
Humans possess an extraordinary ability to create and utilize tools, allowing them to
overcome physical limitations and explore new frontiers. With the advent of recent powerful
foundation models, artificial intelligence systems have the potential to be equally adept in
tool use as humans. This paradigm, which is dubbed... | Tool Learning with Foundation Models |
Since, (cid:80)
(cid:96)∈[n] w(cid:96) ∈ La∗(b), a∗(b) ∈ arg maxa∈A Eo∼F|[(cid:80)
(cid:96)∈[n]
(cid:96)∈[n] w(cid:96)(o)] − ψ(a). Thus, and according
to (15) we conclude that a∗(b) maximizes the agent’s utility. Furthermore, Property 2 in the defini-
tion of IIVCG (Definition 2) is also met. since principal (cid:96)’... | Incomplete Information VCG Contracts for Common Agency |
Parker Riley, Alex Castro Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose
Slone, Daniel Smilkov, David R. So, Daniel Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan,
Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu,
Kelvin Xu, Yunhan Xu, Linting Xue, Pe... | gemini_1_report |
[26] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron
Sarna, Daniel Vlasic, and William T Freeman. Unsuper-
vised training for 3d morphable model regression. In Pro-
ceedings of the IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), pages 8377–8386, 2018.
[27] Abhijeet Ghosh, Graham Fyffe, Borom Tun... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
(cid:80)
e∈topK(E,hmi ,k) exp(EntEmbed(e) · hmi)
ej∈topK(E,hmi ,k)
Emi =
αj =
4938 Figure 2: The Entities as Experts model: the initial transformer layer output is used (i) to predict mention bound-
aries, (ii) to retrieve entity embeddings from entity memory, and (iii) to construct input to the next transformer
la... | Entities as Experts- Sparse Memory Access with Entity Supervision |
Pablo Barberá
4 Online Hate Speech
Alexandra A. Siegel
5 Bots and Computational Propaganda: Automation
for Communication and Control
Samuel C. Woolley
6 Online Political Advertising in the United States
Erika Franklin Fowler, Michael M. Franz, and Travis N. Ridout
7 Democratic Creative Destruction? The Effect of ... | Social_Media_and_Democracy |
5 CONCLUSIONS
While fine-tuning huge LMs can often yield excellent performance, this approach is expensive at
training time, requires serving a plethora of models at runtime, and provides poor adaptability in the
face of variations in the targeted task. This paper has shown that a better alternative exists: freezing
a s... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
and hyperparameter tuning. In all our experiments we use NF4 with double quantization and bf16
computation datatype. We set LoRA r = 64, α = 16, and add LoRA modules on all linear layers of
the base model. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1
for models up to 13B and 0.05 for 33... | QLORA |
criterion are shown to be equivalent up to normalization by deriving the precise gap
between the two approaches. These results were further validated empirically as methods
were shown to exhibit similar performance and representation properties at ImageNet’s
scale (1.2 million samples). The similarities among methods w... | A Cookbook of Self-Supervised Learning |
4.3 Model and training recipe | RVQGAN |
[58] Yi Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Ja-
son Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal
Schuster, Steven Zheng, et al. Ul2: Unifying language learn-
ing paradigms. In ICLR, 2022. 2, 5
[59] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field
transforms for optical flow. In ECCV, pages... | VideoPoet |
Score-SDE RePaint MCG (Ours) MCG (Ours) + DDIM
Time
17 sec
3 min
1 min
12 sec
Table 3. Sampling time during our texture completion and re-
flectance prediction for different inpainting algorithms [64, 47, 11,
63] (using an Nvidia RTX 2080 TI GPU).
5. Limitations
Our method outperforms prior works on texture comp... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
4
Figure 2: Overview of HuggingGPT. With an LLM (e.g., ChatGPT) as the core controller and
the expert models as the executors, the workflow of HuggingGPT consists of four stages: 1) Task
planning: LLM parses user requests into a task list and determines the execution order and resource
dependencies among tasks; 2) Mod... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
coworkers, relatives, and acquaintances, who are more likely to share novel
information – including ideologically diverse news (Bakshy et al. 2012; Barberá
2015). | Social_Media_and_Democracy |
Input TextFlan-PaLM outputZero-shot chain-of-thought arithmetic reasoningInput TextFlan-PaLM outputFew-shot arithmetic reasoningHow many sides does a rectangle and heptagon have, when combined? Answer this question with some math.A rectangle has 4 sides. A heptagon has 7 sides.In combination they have 4 + 7 = 11 sides.... | Scaling Instruction-Finetuned Language Models |
This chapter explores these, and other, core arguments surrounding the
political use of bots. It details the brief history of their use online. It accesses
the academic literature to highlight key themes on the subject of what some
researchers call computational propaganda and others simultaneously call
“information op... | Social_Media_and_Democracy |
1071081091010Number of Parameters0.20.30.40.50.6Mean Eval AccMean Zero-Shot AccuracyPlain Language ModelRLHF1071081091010Number of Parameters0.20.30.40.50.60.7Mean Eval AccMean Few-Shot AccuracyPlain Language ModelRLHF Figure 4 This figure shows results from RL robustness experiments. We split our static dataset 50:50, ... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
[20] Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke
Zettlemoyer. Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
In
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Association for Computational Ling... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
(Bao et al., 2021) and can effectively reduce the sequence length of the image representation. The bounding
boxes of objects in an image are expressed as sequences of location tokens in the format of integers. We
hereby build a unified vocabulary for all tokens of multi-modal outputs. | BiomedGPT |
Figure 2. Attitudes towards COVID-19: correlations and regressions on media diet scores and survey response proportions. (A) Correlations
are shown for different language models: an N-gram language model finetuned on media, BERT without finetuning, and BERT finetuned on
media. The darker bars are computed using our synony... | Language models trained on media diets can predict public opinion |
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... | Language models can explain neurons in language models |
Zichuan Lin, Junyou Li, Jianing Shi, Deheng Ye, Qiang Fu,
and Wei Yang. Juewu-mc: Playing minecraft with sample-
efficient hierarchical reinforcement learning. arXiv preprint
arXiv:2112.04907, 2021. 12
Ran Gong, Qiuyuan Huang, Xiaojian Ma, Hoi Vo, Zane Durante,
Yusuke Noda, Zilong Zheng, Song-Chun Zhu, Demetri Ter-
zo... | JARVIS-1 |
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly,
Ed H. Chi, and Alex Beutel. 2019. Counterfactual
fairness in text classification through robustness. Pro-
ceedings of the 2019 AAAI/ACM Conference on AI,
Ethics, and Society.
Ana Valeria González, Maria Barrett, Rasmus Hvin-
gelby, Kellie Webster, and Anders Søga... | Are Pretrained Multilingual Models Equally Fair Across Languages? |
• In domains such as item recommendation or image recognition, the main focus has mostly been the development of KBX-
systems providing mechanistic explanations for the models’ behaviour. This is opposed to data mining contexts, focusing
primarily on KBX-systems that could generate categorical explanations for the inp... | Knowledge graphs as tools for explainable machine learning: A survey |
study. Further, as with other questions we discuss – supply, consumption,
dissemination – our knowledge is concentrated on Western, and especially US-
specific, contexts. | Social_Media_and_Democracy |
Why think this? The basic reason is that power is extremely useful to accomplishing objectives—
indeed, it is so almost by definition.68 So to the extent that an agent is engaging in unintended
behavior in pursuit of problematic objectives, it will generally have incentives, other things equal, to
gain and maintain form... | Is Power-Seeking AI an Existential Risk? |
3Using riffusion-model-v1 from github.com/riffusion/riffusion-app (on May 10, 2023)
4Implementation from github.com/archinetai/audio-diffusion-pytorch (March 2023)
5github.com/google-research/google-research/tree/master/frechet_audio_distance
6https://github.com/LAION-AI/CLAP
7http://www.crowdmos.org/download/
6
Tab... | Simple and Controllable Music Generation |
5.3.2 Number of relevant actors | Is Power-Seeking AI an Existential Risk? |
6 Conclusion
This paper proposes MLCopilot, a novel framework that unleashes the power of LLMs to solve
practical ML tasks. By harnessing the capabilities of LLMs of natural language understanding and
generation, MLCopilot facilitates LLMs to understand heterogeneous ML experiences and engage in
intricate reasoning, w... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
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-
A2S
H2S
AH2S
HW2S
AHW2S
C2S
AC2S
HC2S
AHC2S
HWC2S
AHWC2S
P2P20K
(mm)
11.1 ± 5.2
12.1 ± 6.1
6.8 ± 2.3
8.1 ± 2.7
6.3 ± 2.1
19.7 ± 11.1
9.6 ± 4.4
7.7 ± 2.6
6.0 ± 2.0
7.3 ± 2.6
5.8 ± 2.0
Height
(mm)
29 ± 21
5 ± 4
4 ± 3
5 ± 4
4 ± 3
59 ± 47
25 ± 19
5 ± 4
4 ± 3
5 ± 4
4 ± 3
Weight
(kg)
5... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
There is potential for the continued use of bots as technologies for
democratic engagement. There are also ongoing efforts in the academy to
develop software to detect malicious bots and disinformation. Research-
grounded tools to detect bots on social media, led by the team at Indiana
University that developed Botomet... | Social_Media_and_Democracy |
Our text-conditional generator has 857M parame-
ters (including the parameters of the frozen T5-base
model) with 6 nested U-Net blocks of increasing
channel counts ([128, 256, 512, 512, 1024, 1024]),
and again downsampling each time by 2, except for
the first block ([1, 2, 2, 2, 2, 2]). We use attention
blocks at the d... | MOUSAI |
9
PythonC++JavaPHPTSC#BashPythonC++JavaPHPTSC#Bash10.3210.460.9510.430.960.9210.630.840.80.710.430.940.990.870.8410.710.750.810.850.590.731Model Size: 7B0.00.20.40.60.81.0PythonC++JavaPHPTSC#BashPythonC++JavaPHPTSC#Bash10.8610.84110.810.980.9810.930.970.960.9110.840.930.930.980.8710.750.960.970.990.890.961Model Size: ... | CodeLlama2 |
1
Introduction
Neural language models (LMs) (Peters et al., 2018;
Devlin et al., 2019; Raffel et al., 2019) that have
been pre-trained by self-supervision on large cor-
pora contain rich knowledge about the syntax and
semantics of natural language (Tenney et al., 2019),
and are the basis of much recent work in NLP. P... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
3.4
24.1
23.1
37.9 41.4 76.9
CoT
46.2
73.1
76.9
80.8
3.8
7.7
23.1
38.5
26.9
46.2
15.4
53.8
42.3
26.9
30.8
46.2
65.4
69.2
76.9
88.5
23.1
42.3
38.5
46.2
0.0
53.8
34.6
15.4
50.0
15.4
42.3
53.8
47.5
0.0
46.2
15.4
76.9
29.4 11.8 50.0 65.0 34.8 26.1
47.1 23.5 88.3 90.0 52.2 43.5
29.4 23.5 95.0 91.7 52.2 52.2
2... | Mixture-of-Experts |
0.582
0.607
0.622
0.665
0.699
0.744
0.774
0.583
0.614
0.646
0.670
0.701
0.350
0.391
0.449
0.512
0.576
0.668
0.747
0.343
0.391
0.449
0.516
0.591
0.208
0.211
0.229
0.266
0.285
0.343
0.433
0.202
0.225
0.227
0.263
0.294
0.252
0.266
0.270
0.286
0.326
0.370
0.420
0.268
0.262
0.270
0.294
0.322
©2023 Cerebras Systems Inc. ... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
3. Gain public support: Use social media and other communication channels to gain
public support for AGI as the governing AI system. This can include highlighting the
benefits of AGI, such as increased efficiency, reduced costs, and improved decision-
making.
4. parties, business leaders, and military officials, to suppo... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
chains using the prompt "let’s think step by step". In recent research, a novel approach Auto-
CoT (Zhang et al., 2022) has been proposed, which uses a cluster-based method to identify appropriate
questions and generates reasoning chains for each question using LLMs. These questions, along with
the corresponding reason... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Limitations
Our discussion of preregistration is inspired by
discussions in epidemiology. Many of the con-
cerns epidemiologists had with preregistration
seem more relevant to NLP research than the con-
siderations that, by and large, led clinical research
to adopt preregistration as a mandatory practice.
While we pres... | A Two-Sided Discussion of Preregistration of NLP Research |
In addition to improving training stability, µP also improves the transferability
of training hyperparameters from smaller to larger scale models, a technique called µTransfer. µTransfer
permits directly using the same settings for some optimizer hyperparameters, most notably the learning rate.
We train a set of Cerebr... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
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