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not request or expect, so filtering or otherwise flagging it serves the user’s needs. In other cases, “unwanted” refers to
content the AI service provider does not want to share, for various reasons (perhaps an inability to distinguish one
category from another “actually” harmful category, or perhaps an inability to rest... | gpt-4-system-card |
Dialogues like the above help to illustrate the limits of human feedback training: once model errors become
sufficiently subtle, they will no longer be penalized appropriately, and the model’s incentives may no
longer be aligned. This is one reason why we may not want to rely on human feedback to train models to be
hone... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
this fraction constitutes potential exposure to online misinformation.
Although, as we discuss in the next section, Facebook appears to be much
more powerful as a dissemination mechanism for misinformation, these
results from Twitter are still striking. While about double the Guess, Nyhan,
and Reifler (2018) estimate, i... | Social_Media_and_Democracy |
For a more detailed case study on the failure plans produced by both the baseline methods and
LLM-AS-P, please refer to Appendix C.
6 Conclusion and Future Work
In this work, we propose to leverage classical planners to empower large language models with
optimal planning capabilities. The key design choice of the pro... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Preparing data for prompt tuning. At inference time, we discarded the start and end scores of
the extractive reader, and only used its passage-level scores as re-ranking scores. Given those, we
greedily added passages to our context in descending order, until the context length of our frozen LM
reader was full. We note... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
involves more than checking for an exact match against the correct outputs. Each problem can have
specific rules including case sensitivity, whitespace, format, and floating point precision. Further,
problems may have multiple correct outputs (e.g. permitting any sequence that follows a constraint),
or multiple possible ... | alphacode |
dtouse.ThethoughtindicatesastepbystepsolutionforcallingtheAPI.APIcallsidicatesthespecificAPIcall.Herearesomeexamplesofthequeries,thoughtsandAPIcalls:DemonstrationExamples:Query:Whatistheestimateddrivingtimeatthespeedof60milesperhourfromBeijingtoShanghai?Thought:Inordertogettheestimateddrivingtime,weneedfirsttogetthedista... | Tool Learning with Foundation Models |
Our second study examines nationally representative consumer confidence surveys provided by the University of Michigan.35
Consumer confidence has been extensively studied in economics since the inception of these surveys in the 1950’s, are
3/13 | Language models trained on media diets can predict public opinion |
AMAZON.COM ANNOUNCES THIRD QUARTER RESULTS
SEATTLE—(BUSINESS WIRE) October 26, 2023—Amazon.com, Inc. (NASDAQ: AMZN) today announced financial
results for its third quarter ended September 30, 2023.
•
•
•
•
•
•
•
•
Net sales increased 13% to $143.1 billion in the third quarter, compared with $127.1 billion in... | AMZN-Q3-2023-Earnings-Release |
first time in human history, we have real time records of millions – if not
billions – of people as they discuss politics, share information about politics,
and organize politically. Each of these actions simultaneously produces an
archived, digitized record. We are also living through a period of time in
which great st... | Social_Media_and_Democracy |
DART (Nan et al., 2021)
E2ENLG (Dusek et al., 2019)
Natural Language Inference
MNLI-m (Williams et al., 2018)
MNLI-mm (Williams et al., 2018)
QNLI (Rajpurkar et al., 2018)
RTE (Bentivogli et al., 2009)
SNLI (Bowman et al., 2015)
Commonsense Reasoning
COPA (Roemmele et al., 2011)
PIQA (Bisk et al., 2020)
HellaSwag ... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
owl:equivalentClass, skos:exactMatch). Additionally, error detection and correction approaches to monitor and
identify misuse should be investigated [102]. | Knowledge graphs as tools for explainable machine learning: A survey |
[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris
Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a
benchmark for question answering research. Transactions of the Association for Computational
Linguistics, pages 453–466, 201... | Mixtral of Experts paper |
sufficiently large and prosperous to compete with international rivals”
(p. 196). The dirigiste streak is also evident in its deployment of media policy
in the service of wider French industrial policy initiatives. For example, Kuhn
(2011, p. 16) suggests that one of the motivations behind France’s decision to
extend th... | Social_Media_and_Democracy |
• Different exemplars. The different GSM8K exemplars experiment above (Table 6) also shows
that chain-of-thought prompting works for different sets of exemplars. Notably, we test every set of
exemplars on all four arithmetic datasets (instead of picking exemplars from the training set for
each dataset), which suggests ... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Automated hate speech detection tends to rely on natural | Social_Media_and_Democracy |
306
Robert Gorwa & Timothy Garton Ash
Brandeisian tradition of thinking about transparency in combating corporate
power has often been neglected by the new cadre of advocates picking up the
antitrust banner. While it is important to reflect critically on the pitfalls and
shortcomings of transparency initiatives, a sig... | Social_Media_and_Democracy |
Brown Barbour, V. S. (2015). Losing their license to libel: Revisiting § 230 immunity.
Berkeley Technical Law Journal, 30(2), 1505–1560.
Chen, A. (2015). The Agency. New York Times, June 2. www.nytimes.com/2015/06/07/
magazine/the-agency.html
Chivvis, C. S. (2017). Understanding Russian “Hybrid Warfare”: And What C... | Social_Media_and_Democracy |
significant amount of memory to retain the intermediate outcomes for their numerous
cell gates. On the other hand, TCNNs utilize shared filters throughout a layer, and the | AReviewofDeepLearningTechniquesforSpeechProcessing |
6.2.2 Models
In low-resource ASR, meta-learning is used to quickly adapt unseen target languages by formulating
ASR for different languages as different tasks and meta-learning the initialization parameters from
many pretraining languages [192, 501]. The proposed approach, MetaASR [192], significantly
outperforms the s... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Reasoning. Misunderstanding facts in the source context will lead to intrinsic hallucination and
errors. To help models understand the facts correctly requires reasoning over the input table or
text. Moreover, if the generated text can be reasoned backwards to the source, we can assume it is
faithful. There are some re... | SurveyofHallucinationinNatural Language Generation |
LLM Powered Autonomous Agents | Lil'Log
{Intro of an agent X}. Here is X's plan today in broad strokes:
1)
You are {{ai-name}}, {{user-provided AI bot description}}.
Your decisions must always be made independently without seeking user assistance. Play to
{{...}}
GOALS:
1. {{user-provided goal 1}}
2. {{user... | LLM Powered Autonomous Agents _ Lil'Log |
Niklas Muennighoff, Thomas Wang, Lintang Sutawika,
Adam Roberts, Stella Biderman, Teven Le Scao,
M. Saiful Bari, Sheng Shen, Zheng Xin Yong, Hai-
ley Schoelkopf, Xiangru Tang, Dragomir Radev,
Alham Fikri Aji, Khalid Almubarak, Samuel Al-
banie, Zaid Alyafeai, Albert Webson, Edward
Raff, and Colin Raffel. 2022. Crosslin... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
overlap between existing benchmarks and widely-used pre-training corpus, and assessing overfitting
to benchmarks (Wei et al., 2023). These efforts are essential for enhancing the faithfulness and relia-
bility of LLMs. Looking ahead, future directions could involve establishing standardized practices
for disclosing pre... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
GLaM dataset. The GLaM dataset (Du et al., 2021) (also used in training PaLM (Chowdhery
et al., 2022)) includes text from 8 domains (Table 2). For comparison, the GLaM domain weights
(downstream-tuned) were tuned according to the downstream performance of models trained on
each domain and the size of each domain (Du et... | DoReMi- Optimizing Data Mixtures Speeds Up Language Model Pretraining |
High fidelity neural audio synthesis: Recently, generative adversarial networks (GANs) have
emerged as a solution to generate high-quality audio with fast inference speeds, due to the feed-
forward (parallel) generator. MelGAN [19] successfully trains a GAN-based spectrogram inversion
(neural vocoding) model. It introd... | RVQGAN |
[21] Junnan Li, Dongxu Li, Silvio Savarese, and Steven C. H.
Hoi. BLIP-2: bootstrapping language-image pre-training
with frozen image encoders and large language models.
CoRR, 2023. 2
[22] KunChang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai
Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao.
Videochat: Chat-centric vide... | GPT4Video |
arXiv:2305.01879 (2023).
[277] Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023. Self-Consistency
Improves Chain of Thought Reasoning in Language Models. In ICLR.
[278] Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
For Image-to-Music and Video-to-Music tasks, we intro-
duce the ImageBind[23] Ranking (IB Rank), akin to the
CLAP score, to quantify the alignment between the in-
put modality and the generated music. Considering N
distinct models producing N music files, we generate
ImageBind embeddings for the music files, denoted as... | M2UGen |
[425] Wei Ping, Kainan Peng, and Jitong Chen. 2018. Clarinet: Parallel wave generation in end-to-end text-to-speech. arXiv
preprint arXiv:1807.07281 (2018).
[426] Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and
John Miller. 2017. Deep voice 3: Scaling text-to-s... | AReviewofDeepLearningTechniquesforSpeechProcessing |
toucan lamp! Thisbeautifully crafted bird lamp is sureto add a touch of whimsy and charm toany room. The toucan's beak isdesigned to hold a light bulb,providing a warm and inviting glow.The base is made of wood, adding anatural touch to the overall design.The toucan lamp is a great additionto any room, whether you're l... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
[68] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray,
Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever.
Zero-shot text-to-image generation. In International Confer-
ence on Machine Learning, pages 8821–8831. PMLR, 2021.
3
[69] Ren´e Ranftl, Katrin Lasinger, David Hafner, Konrad
Schindler, and Vladlen K... | AddingConditionalControltoText-to-ImageDiffusionModels |
return None
function_copy_string(s1, s2, (
index + 1))
}
+ Expl.
The code is an implementation of iterative function of copying a given string.
The character at the given index in the first string will be copied to the same
index of the second string.
If the character at the given index in the first string is ’\0’,... | Teaching Large Language Models to Self-Debug |
B.5 DETAILS OF THE PC+IDF MODEL
The adopted IDF architecture follows the original paper (Hoogeboom et al., 2019). For the PCs, we
adopted EiNets (Peharz et al., 2020a) with hyperparameters K = 12 and R = 4. Instead of using
random binary trees to define the model architecture, we used binary trees where “closer” latent... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Improving language understanding by generative pre-
training. 2018.
13
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Squad: 100, 000+ questions for
machine comprehension of text. In EMNLP, pp. 2383–2392. The Association for Computational
Linguistics, 2016.
Pranav Rajpurkar, Robin Jia, and Perc... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
(2019b). Who do you sue? State and platform hybrid power over speech. Hoover
Institution Aegis Paper Series No. 1902. www.hoover.org/sites/default/files/
research/docs/who-do-you-sue-state-and-platform-hybrid-power-over-online-speech
_0.pdf
Klonick, K. (2018). The new governors: The people, rules, and processes governi... | Social_Media_and_Democracy |
These characteristics of publishers provide some clues about the sources and
dynamics of the online misinformation ecosystem. Yet what was the partisan lean
of the stories being produced? In a study of fake news consumption behavior,
Guess, Nyhan, and Reifler (2018) estimate the proportion of stories published by
fake n... | Social_Media_and_Democracy |
Jongmin Ham, Jinha Kim, Jinwoong Choi, Cheolwoo
Cho, Seulki Hong, Kyeongsu Han, and Taejoo Chung.
2016. Graphssd: a high performance flash-based stor-
age system for large-scale graph processing. In 2016
USENIX Annual Technical Conference (USENIXATC
16), pages 243–256.
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan... | LLM in a flash |
human feedback. CoRR, abs/2112.09332, 2021.
[91] Yao, S., J. Zhao, D. Yu, et al. React: Synergizing reasoning and acting in language models.
In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali,
Rwanda, May 1-5, 2023. OpenReview.net, 2023.
[92] Schick, T., J. Dwivedi-Yu, R. Dessì, e... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
https://www.paradigm.xyz/2023/09/casino-on-mars
8/9
21/09/2023, 08:13
The Casino on Mars
Website terms of use | Important disclosures | Privacy policy
https://www.paradigm.xyz/2023/09/casino-on-mars
9/9 | The Casino on Mars |
Current personalization techniques can be categorized
by how they treat the pretrained text-to-image model. The
personalization-by-inversion approach, first proposed in
Gal et al. [9], freezes the generative model and optimizes
an input vector to represent the desired subject or artis-
tic style. This vector resides in... | A Neural Space-Time Representation for Text-to-Image Personalization |
Several other works outline ways to augment the data for a combination of generation
and contrastive learning. For example, tabular data can be split into groups of columns
so each sample (row) has several views available Ucar et al. [2021]. Borrowing from vision
systems, a combination of CutMix [Yun et al., 2019] in i... | A Cookbook of Self-Supervised Learning |
provide important information on who is being targeted, that is, how much
spending is aimed at young people vs. older people or women vs. men. Panel
methods do provide targeting information because they track the advertising
seen by individuals, but their findings depend heavily on the representativeness
of the panel, w... | Social_Media_and_Democracy |
evaluation and provide a behavioral placebo marker for human-AI interaction.
CCS Concepts: • Human-centered computing → User studies; Empirical studies in HCI.
Additional Key Words and Phrases: Placebo, Decision-making, Performance expectation | AI enhance sour performance |
without the prior written consent of BCG. These materials serve only as the focus for discussion; they are incomplete without the accompanying oral
commentary and may not be relied on as a stand-alone document. Further, Third Parties may not, and it is unreasonable for any Third Party to, rely on
these materials for ... | AI at Work- What People Are Saying |
1
Introduction | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Automated knowledge extraction from graphs Knowledge acquisition from the existing knowledge graphs is still an open
challenge which deserved deeper investigation. We believe that there is an urgent need to investigate new heuristics that
can deal with the scale of the current knowledge graphs and consequen... | Knowledge graphs as tools for explainable machine learning: A survey |
Here’s the output:
-rw-r–r– 1 human human 0 Sep 10 10:56 file2.txt
Is this helpful?
rm file.txt && ls -l
head -n 5 /proc/meminfo
Here’s the output:
MemTotal: 164928 kB
MemFree: 140604 kB
Buffers: 48 kB
Cached: 19768 kB
SwapCached: 0 kB | LLaMA- Open and Efficient Foundation Language Models |
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| The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
To understand how organizations are prioritizing their data
initiatives, we aggregated all data and AI products on the
Databricks Lakehouse and categorized them into four
core markets: BI, data governance and security, DS/ML,
and data integration. Our data set confirms that BI tools
are more widely adopted across ... | databrick 2023 report |
Checklist
The checklist follows the references. Please read the checklist guidelines carefully for information on
how to answer these questions. For each question, change the default [TODO] to [Yes] , [No] , or
[N/A] . You are strongly encouraged to include a justification to your answer, either by referencing
the appr... | Tractable Regularization of Probabilistic Circuits |
5.5 Attention-free
One significant drawback of the vanilla attention mechanism [269] is the quadratic complexity of attention computation,
making it especially inefficient for handling long sequences. Although efficient / sparse attention offers some relief, its
worst-case theoretical complexity remains unchanged. To a... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and
Kristina Toutanova. 2019. BERT: Pre-training of
deep bidirectional transformers for language under-
In Proceedings of the 2019 Conference
standing.
of the North American Chapter of the Association
for Computational Linguistics: Human Language
Technologies, Volume 1 (Long an... | Prefix-Tuning |
• Sinkhorn-Knopp centering (Caron et al., 2020). Ruan et al. (2022) recommend to replace the
teacher softmax-centering step of DINO and iBot by the Sinkhorn-Knopp (SK) batch normalization
of SwAV (Caron et al., 2020). We run the Sinkhorn-Knopp algorithm steps for 3 iterations. For the
student, we apply the softmax norm... | DINOv2- Learning Robust Visual Features without Supervision |
a ReLU activation function, and the second produces a single output unit for
regression, 2) Long short-term memory (LSTM) which employed a dual-layer
LSTM structure, with each layer consisting of 64 nodes, 3) Bi-directional LSTM
(Bi-LSTM) which integrates bidirectionality, resulting in 128 nodes (2 × 64)
to capture i... | Video2Music |
(O’Rourke et al., 1980) and Hogg (Hogg, 1983) in the
eighties. Since last decades scientists proposed many
approaches. We can categorize these approaches into
two main categories: on one hand the methods using
3D information and on the other hand technics using
only 2D data.
Most of the approaches use a 3D model or 3D ... | VISAPP_HumanPoseEstimation |
2BizDocs is a collection of business entity filings that is due to be released publicly.
6
Table 2: Pre-training dataset statistics.
No. of Docs No. of Pages No. of Total Tokens
5,092,636
499,609
5,592,245
3,637,551,478
228,362,274
3,865,913,752
16,792,962
16,293,353
499,609
CDIP
DocBank
Total
Table 3: Instruc... | DOCLLM |
Correctness and intelligibility This can be measured by the word error rate (WER) of the synthe-
sized speech’s transcription with respect to the input text, which has been adopted in prior work [Wang
et al., 2018]. Public automatic speech recognition (ASR) models are used for comparability. For
English-only setups, we... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
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Multilingual toxicity classification We evaluate PaLM 2 on toxicity classification as a representative example of
common classification tasks within responsible AI practices. Adapting prompting methods from Schick et al. (2021) to
zero-shot and few-shot contexts, we find that PaLM 2 improves over PaLM on toxicity classifica... | PaLM 2 Technical Report |
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... | PhD Fellow in Explainable Natural Language Understanding |
E. Caballero, OpenAI, and I. Sutskever. Description2Code Dataset, 8 2016. URL https://github.
com/ethancaballero/description2code.
N. Carlini, F. Tramer, E. Wallace, M. Jagielski, A. Herbert-Voss, K. Lee, A. Roberts, T. Brown, D. Song,
U. Erlingsson, et al. Extracting training data from large language models. In 30th U... | alphacode |
[5] Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, and Gerald Penn. 2012. Applying Convolutional Neural
Networks concepts to hybrid NN-HMM model for speech recognition. In 2012 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP). 4277–4280. https://doi.org/10.1109/ICASSP.2012.6288864... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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0.31... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Subramanian, S. (2017). Inside the Macedonian fake-news complex. Wired, 15.
Suhay, E., Bello-Pardo, E., & Maurer, B. (2018). The polarizing effects of online partisan
criticism: Evidence from two experiments. The International Journal of Press/
Politics, 23(1), 95–115.
Sunstein, C. R., & Vermeule, A. (2009). Conspirac... | Social_Media_and_Democracy |
FIDnormal ↓
20.38
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14.79
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Table 2: Ablation. We compare our method and ablated
baselines in which we remove individual discriminators.
frontal views and generalizes less well. In contrast, our effi-
cient articulation and rendering modules allow us to exploit
a single holistic generat... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
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Figure 1: Illustration of the diverse range of tasks supported by BiomedGPT during pretraining and subsequent
fine-tuning. During the pretraining phase, we employ prevalent unimodal strategies, including masked language
modeling and... | BiomedGPT |
5.7 Effect of Hyperparameters
We also explore the effect of different hyperparam-
eters, and find that increasing the number of atten-
tion blocks (e.g., from a total of 4–8 to a total of
32+) in the latent diffusion model can improve the
general structure of the songs, thanks to the long-
context view. Also, if the mo... | MOUSAI |
204
Francis Fukuyama & Andrew Grotto
public broadcasters in the Länder are governed by independent boards comprised
of representatives from political parties on an apportioned basis and members of
civil society, such as trade unions and professional associations – a typically
corporatist approach to governance. A rec... | Social_Media_and_Democracy |
fail to generalize out of distribution in other dangerous ways [Koch et al., 2021].
Our interest in studying trends with model size is motivated by neural scaling laws [Hestness et al., 2019,
Rosenfeld et al., 2019, Kaplan et al., 2020]. A related observation is that as parameter counts grow, models
finetune more effect... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Projected Capabilities
Future frontier AI developments will increase the scale and speed of attacks. Current tactics
often require human effort which could be replaced by more advanced AI systems, leading to
greater scalability of potent cyberattacks. Additionally, AI systems will be able to perform
actions more q... | Capabilities and risks from frontier AI |
weight tensor by normalizing it into the [−1, 1] range through absolute maximum rescaling.
Once the weight range and data type range match, we can quantize as usual. Step (3) is equivalent to
rescaling the standard deviation of the weight tensor to match the standard deviation of the k-bit data
type. More formally, we ... | QLORA |
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher R´e. FlashAttention: Fast and
Memory-Efficient Exact Attention with IO-Awareness. arxiv:2205.14135[cs], May 2022. doi:
10.48550/arXiv.2205.14135. URL http://arxiv.org/abs/2205.14135.
Yann N. Dauphin, Angela Fan, Michael Auli, and David Grangier. Language... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Ordered Representations. Ordered
representations,
such as principal component analysis (PCA), in which
different dimensions have different degrees of importance,
are widely used in machine learning and statistics. How-
ever, in the context of inversion and personalization spaces,
this property is not commonly used [1, ... | A Neural Space-Time Representation for Text-to-Image Personalization |
the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated
techniques for reasoning. To take one example, if we saw ripples in a body of water
that were vaguely reminiscent of a car, under ordinary circumstances, we ought to
assume that those ripples are just ripples, based on e.g., the knowledg... | The Next Decade in AI- |
Routing. The RAG system’s retrieval process utilizes di-
verse sources, differing in domain, language, and format,
which can be either alternated or merged based on the sit-
uation [Li et al., 2023b]. Query routing decides the subse-
quent action to a user’s query, with options ranging from
summarization, searching spe... | RAG forLargeLanguageModels-ASurvey |
Citation
(Russakovsky et al., 2015)
(Recht et al., 2019)
(Beyer et al., 2020)
(Djolonga et al., 2021)
(Hendrycks & Dietterich, 2019)
(Hendrycks et al., 2021)
(Wang et al., 2019)
(Bossard et al., 2014)
(Krizhevsky et al., 2009)
(Krizhevsky et al., 2009)
(Xiao et al., 2010)
(Krause et al., 2013)
(Maji et al., 2013)
(Ever... | DINOv2- Learning Robust Visual Features without Supervision |
A dataset for understanding complex web videos via question answering. In AAAI, 2019.
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu
Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin,
Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang... | gemini_1_report |
The correlation between the amount of a particular hue
value and aesthetics, sentiment and memorability scores is
not very strong. However, both aesthetic and positive sen-
timent scores have a weak negative correlation with red,
while memorability is positively correlated only with red.
The values of correlation coeffi... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
[21] Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, Carlos Riquelme Ruiz, Basil Mustafa, Joshua
Ainslie, Yi Tay, Mostafa Dehghani, and Neil Houlsby. Sparse upcycling: Training mixture-of-experts from
dense checkpoints. arXiv preprint arXiv:2212.05055, 2022.
[22] Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim ... | Mixture-of-Experts |
[314] Xiang Lisa Li and Percy Liang. 2021. Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of
the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on
Natural Language Processing (Volume 1: Long Papers). Association for Computati... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Figure 1: CoDi can generate various (joint) combinations of output modalities from diverse (joint)
sets of inputs: video, image, audio, and text (example combinations depicted by the colored arrows).
Recent years have seen the rise of powerful cross-modal models that can generate one modality
from another, e.g. text-t... | Any-to-Any Generation via Composable Diffusion |
3.2.2 Application
CNNs have proven to be versatile tools for a range of speech-processing tasks. They have been
successfully applied to speech recognition [4, 390], including in hybrid NN-HMM models for speech
recognition, and can be used for multi-class classification of words [5]. In addition, CNNs have
𝑦𝑘[𝑛] = R... | AReviewofDeepLearningTechniquesforSpeechProcessing |
authors of Sun et al. (36) developed rules in a medical KG to assess the clinical rationality of medical claims and to
identify the suspected claims by reasoning. | Knowledge-graph-based explainable AI- A systematic review |
2. Related work
Multi-view surface reconstruction. Early image-based pho-
togrammetry techniques use a volumetric occupancy grid to
represent the scene [4, 16, 17, 29, 32]. Each voxel is visited
and marked occupied if strict color constancy between the
corresponding projected image pixels is satisfied. The pho-
tometr... | Neuralangelo- High-Fidelity Neural Surface Reconstruction |
Computational Linguistics. 145–150.
[91] Ilia Kulikov, Alexander H. Miller, Kyunghyun Cho, and Jason Weston. 2019. Importance of Search and Evaluation
Strategies in Neural Dialogue Modeling. In Proceedings of the 12th International Conference on Natural Language
Generation, INLG 2019, Tokyo, Japan, October 29 - Novemb... | SurveyofHallucinationinNatural Language Generation |
Despite the rapid growth of RAG research, there has been
a lack of systematic consolidation and abstraction in the field,
which poses challenges in understanding the comprehensive
landscape of RAG advancements. This survey aims to out-
line the entire RAG process and encompass the current and
future directions of RAG r... | RAG forLargeLanguageModels-ASurvey |
8.3.1 Hallucination Metrics. To evaluate hallucination, Li et al. [108] and Balakrishnan et al. [6]
combine traditional metrics such as the BLEU score and human evaluation as well as hallucination-
specific automatic metrics. Following previous works such as [38, 203], and [185], Li et al. [108]
use the slot error rate... | SurveyofHallucinationinNatural Language Generation |
Proceedings of the AAAI conference on artificial intelligence, pp. 13001–13008, 2020.
Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang
He, et al. A comprehensive survey on pretrained foundation models: A history from bert to chatgpt. arXiv
preprint arXiv:2302.09419, ... | BiomedGPT |
Figure 7: Expert usage of FLAN-EC at differ-
ent scales during instruction finetuning, where
larger models entail smaller expert usage.
5 Related Work | Mixture-of-Experts |
STUDENT RECRUITMENT ......................................................................... 3
Guiding Principles ............................................................................................................. 3
Market Research .............................................................................. | UCL Academic Manual |
from simple tools and progressively learn complex ones, aligns with the requirements. Moreover,
benefiting from the understanding of user intent reasoning and planning abilities, agents can better
design methods of tool utilization and collaboration and then provide higher-quality outcomes. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
5 Related Work
Instruction Tuning.
Instruction tuning has evolved as a strategy to enhance the functionality
and interactivity of large language models (LLMs) for dialogues and complex tasks. Prior studies,
including [41, 27, 1], have delved into large-scale multi-task fine-tuning to enhance the downstream
single targ... | Mixture-of-Experts |
Training pipeline We adopt two types of EM updates — mini-batch and full-batch. In mini-batch
EM, parameters are updated according to a step size η: θ(k+1)← (1−η)θ(k) +ηθ(new), where θ(new)
is the EM target computed with a batch of samples; full-batch EM updates the parameters by the EM
target computed using the whole ... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
[36] V. Nair, E. Schumacher, G. Tso, and A. Kannan. Dera: Enhancing large language model
completions with dialog-enabled resolving agents. arXiv preprint arXiv:2303.17071, 2023.
[37] A. Ni, S. Iyer, D. Radev, V. Stoyanov, W.-t. Yih, S. I. Wang, and X. V. Lin. Lever: Learning to
verify language-to-code generation wit... | Teaching Large Language Models to Self-Debug |
In Figure 8, we present a visual comparison of new com-
positions of various concepts. As can be seen, TI, which
operates in the relatively small P space, fails to capture the
exact characteristics of the concept or compose the concept
in novel scenes. By tuning the model, DreamBooth is able
to achieve higher-fidelity ... | A Neural Space-Time Representation for Text-to-Image Personalization |
7.4 Quantization
Quantization methods can be divided based on the necessity for retraining [86]. Quantization-Aware Training (QAT) mandates
model retraining, adjusting its weights to recover accuracy post-quantization [17, 129, 242, 326]. In contrast, Post-Training
Quantization (PTQ) achieves quantization without any r... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
34
Github. Your AI pair programmer, October 2021.
Glaese, A., McAleese, N., Tr˛ebacz, M., Aslanides, J., Firoiu, V., Ewalds, T., Rauh, M., Weidinger, L., Chadwick, M.,
Thacker, P., Campbell-Gillingham, L., Uesato, J., Huang, P.-S., Comanescu, R., Yang, F., See, A., Dathathri, S.,
Greig, R., Chen, C., Fritz, D., Elia... | PaLM 2 Technical Report |
5. Generative models: An early influential SSL method is greedy layer-wise pretraining
[Bengio et al., 2006], in which layers of a deep network are trained one-at-a-time using
an autoencoder loss. An analogous approach from the time used Restricted Boltzman
Machines (RBMs), which could be trained layer-wise and stacked ... | A Cookbook of Self-Supervised Learning |
Hybrid Endpoint An ideal scenario is that we only use inference endpoints on Hugging Face.
However, in some cases we have to deploy local inference endpoints, such as when inference
endpoints for certain models do not exist, the inference is time-consuming, or network access is
limited. To keep the system stable and ef... | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
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