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The majority of studies concerned with computational
analysis of art images is focused on the challenge of auto-
matically classifying artworks based on categories such as
artist, style or genre. Most of the earlier studies addressing
the topic of automatic artist classification [6], [7], as well as
style [8] and genre ... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
reason why one would add a task specific head (Lees et al., 2022), or to squeeze out some efficiency gains
from eliminating a full vocabulary. Either way, one can always start from a encoder-decoder and chop off
the decoder later so there is no good reason to use an encoder-only model. Hence the only real objective
conside... | UL2- Unifying Language Learning Paradigms |
3. Conceptualize potential harms. Harms are the adverse lived experiences of an algorithmic system’s deployment
and use in the world (Barocas et al., 2017; Shelby et al., 2023)), and downstream uses of a language model create
risks of a range of potential harms (Blodgett et al., 2020; Dev et al., 2021b). These risks ca... | PaLM 2 Technical Report |
Written by:
Matt Huang
Matt Huang is co-founder and Managing Partner at
Paradigm. Previously, Matt was a partner at Sequoia Capital
focusing on early-stage venture investments including
leading the | The Casino on Mars |
Results similarly showed that flagging the tweet as contain-
ing misinformation further lowered participants’ willingness
to engage it and negatively affected participant perceptions,
especially in terms of the trustworthiness, accuracy, and
credibility of the tweet. These responses show encouraging
indications tha... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
havior manually. We demonstrate that, with generative agents, it
is sufficient to simply tell one agent that she wants to throw a
party. Despite many potential points of failure—the party planner
must remember to tell other agents about the party, attendees must
remember the invitation, those who remember must decide t... | Generative Agents- Interactive Simulacra of Human Behavior |
come from C4 and Wikipedia while the instructions are generated visa LLMs. The dataset is extended
with additional structured corpora examples such as Stack Exchange and WikiHow and task examples
such as question answering, email writing, grammar error correction, story/poem generation, and text
summarization. The data... | QLORA |
Figure 3 shows the parameter/performance trade-off ag-
gregated over all classification tasks in each suite (GLUE
and “additional”). On GLUE, performance decreases dra-
matically when fewer layers are fine-tuned. Some of the
additional tasks benefit from training fewer layers, so per-
formance of fine-tuning decays much le... | Parameter-Efficient Transfer Learning for NLP |
ACKNOWLEDGMENTS
We’d like to thank the millions of people who were involved
in creating the data used by Whisper. We’d also like to
thank Nick Ryder, Will Zhuk, and Andrew Carr for the
conversation on the waterfall hike that inspired this project.
We are also grateful to the Acceleration and Supercomputing
teams at Op... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Transformers. arXiv preprint arXiv:2303.01610 (2023).
arXiv preprint arXiv:2304.11414 (2023).
[46] Yuzhong Chen, Zhenxiang Xiao, Lin Zhao, Lu Zhang, Haixing Dai, David Weizhong Liu, Zihao Wu, Changhe Li, Tuo Zhang, Changying Li, et al. 2022.
Mask-guided vision transformer (mg-vit) for few-shot learning. arXiv prepri... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
3.3 Training
Image diffusion models learn to progressively denoise images to generate samples. The denoising
can happen in pixel space or a “latent” space encoded from training data. Stable Diffusion uses latent
images as the training domain. In this context, the terminology “image”, ”pixel”, and “denoising” all
refer... | Adding Conditional Control to Text-to-Image Diffusion Models |
Even if many fears are expressed, the precise nature of the destructive side
of creative destruction is perhaps less easy to systematically substantiate, at
least at scale. The move to digital media is accompanied by the relative decline
of the kind of mass media experience associated in particular with mid-
twentieth-... | Social_Media_and_Democracy |
1. StarCoder substantially outperforms all other models on data science problems from the
DS-1000 benchmark. Moreover, this is true across every kind of data science library.
2. StarCoderBase also outperforms every other model, but is slightly behind StarCoder on
DS-1000.
3. We confirm the finding by Lai et al. (20... | StarCoder_paper (1) |
tpageoutliningtheOldEnglishperiod.Action:add_text_pageActionInput:{"title":"OldEnglishPeriod","bullet_items":["Beowulfandotherepicpoems","Anglo-SaxonChronicle","InfluenceofChristianity","Developmentoftherunicalphabet"]}Observation:addedtextpageThought:WeneedtoaddatextandimagepagewithanexampleofOldEnglishliterature.Acti... | Tool Learning with Foundation Models |
It’s Not a Computer, It’s a Companion!
Justine Moore, Bryan Kim, Yoko Li, and Martin Casado
Investing in ElevenLabs
Jennifer Li, Bryan Kim, and Justine Moore
Using Generative AI to Unlock Probabilistic Products
Anish Acharya
The views expressed here are those of the individual AH Capital Management, L.L.C. (“a16z”) ... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
Jurassic-X: Crossing the neuro-symbolic chasm with the MRKL system
https://www.ai21.com/blog/jurassic-x-crossing-the-neuro-symbolic-chasm-with-the-mrkl-system
3/13 | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
Hui Wan. Multi-task learning with multi-head attention for multi-choice reading comprehension. arXiv
preprint arXiv:2003.04992, 2020.
33
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. Glue: A multi-
task benchmark and analysis platform for natural language understanding. arX... | UL2- Unifying Language Learning Paradigms |
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... | Announcing Jurassic-2 and Task-Specific APIs |
Employing self-consistency as a method of self-verification. From the results in Table 1, we
observe that the oracle setting yields much higher accuracy. This suggests that within the LLM’s
search space, a correct answer might exist. If we could leverage a robust verification process to
guide the LLMs towards the right... | LARGELANGUAGEMODELSCANNOTSELF-CORRECT REASONINGYET |
sha1_base64="76w10YEtETzUXdaT0wTZt0xBig8=">AAAB9XicbVDLSgMxFL1TX7W+qi7dBIvgqsyIoMuCG5cV7EPaacmkmTY0kxmSO0oZ+h9uXCji1n9x59+YtrPQ1gOBwzn3ck9OkEhh0HW/ncLa+sbmVnG7tLO7t39QPjxqmjjVjDdYLGPdDqjhUijeQIGStxPNaRRI3grGNzO/9ci1EbG6x0nC/YgOlQgFo2ilXjeiOArCrD3tYV/0yxW36s5BVomXkwrkqPfLX91BzNKIK2SSGtPx3AT9jGoUTPJpqZsanlA2pkPesVTRiBs/m... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
equal number of values assigned from the input tensor. Quantile quantization works by estimating
the quantile of the input tensor through the empirical cumulative distribution function.
The main limitation of quantile quantization is that the process of quantile estimation is expensive.
Therefore fast quantile approxim... | QLORA |
We are waiting for OpenAI to reveal more details about the training infrastructure and
model implementation. But to put things into perspective, GPT-3 175B model required
3.14E23 FLOPS of computing for training. Even at theoretical 28 TFLOPS for V100 and
lowest 3 year reserved cloud pricing we could find, this will tak... | OpenAI's GPT-3 Language Model_ A Technical Overview |
quality and repeated instructions, before adding
them to the task pool. This process can be repeated
for many interactions until reaching a large number
of tasks.
To evaluate SELF-INSTRUCT empirically, we run | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Issue-Specific Platform Disclosures
For high-profile content-moderation issues, platforms are increasingly issuing
in-depth public statements to explain their policies. Some offer detailed
30 This includes Urban, Karaganis, and Schofield (2017) and numerous other scholarly works
including those cited in Section III.B.1, ... | Social_Media_and_Democracy |
M↑:
M↓:
R↑:
R↓:
C↑:
C↓:
| f (s)| = 1 for all s ∈ S1.
| f (s)| = 1 for all s ∈ S2.
For all (cid:3)s1, t1, (cid:2)1(cid:4) ∈ E1, there is some (cid:3)s2, t2, (cid:2)2(cid:4) ∈ E2 such that R((cid:2)1, (cid:2)2).
For all (cid:3)s2, t2, (cid:2)2(cid:4) ∈ E2, there is some (cid:3)s1, t1, (cid:2)1(cid:4) ∈ E1 such that R((... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
(cid:34)(cid:88)
t≥1
(cid:35)
7
Figure 8: Interpolations of CelebA-HQ 256x256 images with 500 timesteps of diffusion. | Denoising Diffusion Probabilistic Models |
176B
8.11
8.37
8.21
571
8.64
Table 4: BLOOM perplexity results for WikiText2.
175 Billion Parameter Models. We now examine BLOOM-176B and OPT-175B, the largest dense
openly-available models. Table 5 summarizes results across Wikitext-2, PTB, C4. We observe that,
at 4 bits, GPTQ models reach only ≤ 0.25 lower perplexi... | GPTQ |
3. The smaller the number of features, the smaller the mtry and num
trees, and the larger the sample fraction.
4. The more numeric features, the larger the mtry and num trees, and
the smaller the sample fraction.
5. The more categorical features, the smaller the mtry and num trees,
and the larger the sample fracti... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
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... | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
of occurencies. The more represented class is then
considered as the detected movement.
Based on this very simple workflow, we got 71.66%
of good action classification. The confusion matrix
is shown on the fig.13. Of course, this accuracy
rate is lower than the recent accuracy obtained on the
same database (Blank 99.64% (... | VISAPP_HumanPoseEstimation |
2.4. Cognitive models
A special sort of knowledge is the knowledge that accumulates over time about
particular states of affairs, such as what we might learn about a friend in the course of a
conversation, about a nation in the course reading the news, or about a set of people as
we read a book. In cognitive psycho... | The Next Decade in AI- |
The a16z Investment Thesis on AI in Bio + Health | Andreessen Horowitz
https://a16z.com/2023/06/21/ai-bio-health-thesis/
6/9 | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
Recognizing that censoring hate speech may come into conflict with legal
protections of free speech or may be manipulated by governments to target
critics, international agencies such as UNESCO have generally maintained that
“the free flow of information should always be the norm.” As a result, they
often argue that coun... | Social_Media_and_Democracy |
16
I. Tiddi and S. Schlobach
Artificial Intelligence 302 (2022) 103627
Fig. 8. Overview of existing KBX-systems per application domain (RBML=Rule-based ML, ImgRec=Image Recognition, RecSys=Recommender Systems
NLP=Natural Language applications, PredML=Predictive tasks).
Fig. 9. Time-based overview of th... | Knowledge graphs as tools for explainable machine learning: A survey |
encoder and LLM directly to train the entire model in an end-to-end way. While the agent can achieve
remarkable visual perception abilities, it comes at the cost of substantial computational resources.
Extensively pre-trained visual encoders and LLMs can greatly enhance the agent’s visual perception
and language expres... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
To address these issues, we present SHAPY, a new deep
neural network that accurately regresses 3D body shape and
pose from a single RGB image. To train SHAPY, we first
need to address the lack of paired training data with real im-
ages and ground-truth shape. Without access to such data,
we need alternatives that are e... | Accurate 3D Body Shape Regression using Metric and Semantic Attributes |
2.5.2. PRIOR ENCODER
The prior encoder consists of a text encoder that processes
the input phonemes ctext and a normalizing flow fθ that
improves the flexibility of the prior distribution. The text
encoder is a transformer encoder (Vaswani et al., 2017) that
uses relative positional representation (Shaw et al., 2018)
in... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
original story.
• Typical k-grams in generated completions rarely appear in the dataset, for values of k as small as 4 or 5.
• The closest point in the dataset to each generated completion is typically still quite far from it.
All the above, taken together with the ability of models trained on TinyStories-Instruct to... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
teams over
internal
time,
Whether in recognition of this problem, or because of increasing pressure
from civil society, academia, and other quarters, some platforms have
provided substantially more public transparency in recent years. This
chapter will review major sources of information released by platforms, as
we... | Social_Media_and_Democracy |
[20] Mioch T, Sterkenburg R, Beuker T, Neerincx MA. Actionable Situation Awareness: Supporting Team
Decisions in Hazardous Situations.
In: Proceedings of the International ISCRAM Conference 18th
International Conference on Information Systems for Crisis Response and Management, ISCRAM 2021
Blacksburg 23 May 2021 throug... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
Theorem (Main result, see Theorems 5-6). Consider a common agency setting. Algorithm 1
implements a (truthful and welfare maximizing) IIVCG contract that satisfies LL and IR whenever
such a contract exists for the setting; Algorithm 2 determines existence of such a contract. Both
algorithms run in polynomial time.
6... | Incomplete Information VCG Contracts for Common Agency |
In addition to PersonaChat-related research, the knowledge-grounded dialogue (KGD) task
in the open-domain requires the model to generate informative responses with the help of an
external knowledge graph (KG) or knowledge corpus [32, 238]. Hallucination in conversations,
ACM Comput. Surv., Vol. 1, No. 1, Article . Pu... | SurveyofHallucinationinNatural Language Generation |
˜Δexpected[HDI 95%]
˜𝑏Description [HDI 95%]
For UEQ-S scales, we found an overall positive user experience, with no group differences
on hedonic or pragmatic attributes, with both having positive values indicating a positive user
experience. SUS ratings indicated that the system was rated average in terms of usabili... | AI enhance sour performance |
● Compute: Computational processing power, including CPUs, GPUs, and other
hardware, used to run AI models and algorithms.
● Computer worm: A type of malicious software that self-replicates and spreads
autonomously across computer networks, exploiting vulnerabilities to infect systems and
potentially causing damage ... | Capabilities and risks from frontier AI |
we showed that IPIP-NEO scores are robust across various item preambles and postambles,
so we optimized the computational cost of this study by using only one default item preamble
and postamble across prompt sets. In all, with 45 personality profiles, 50 generic PersonaChat
descriptions, and no variation in item pream... | PersonalityTraitsinLargeLanguageModels |
more LoRA-derived PEFT approaches build upon LoRA.
Particular emphasis should be placed on pruning technol-
ogy and weight quantification. The application of pruning
techniques can be extended not only to AdaLoRA [45] for
rank adjustment but also to LoRAPrune [48] for pruning
both pretrained and LoRA weights. Notably, ... | Parameter-EfficientFine-TuningMethods |
Today’s casino-like speculation is part of a bootstrapping process. Much like the
gold rush of 1849 transformed San Francisco from a quaint village into a major
port (and ultimately the heart of tech innovation), today’s speculative frenzy in
crypto is attracting the settlers and catalyzing the infrastructure necessary... | The Casino on Mars |
[7] Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. 2018. Predicting Factuality of
Reporting and Bias of News Media Sources. In Proceedings of the 2018 Conference on Empirical Methods in Natural
Language Processing. 3528–3539.
[8] Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020.
... | SurveyofHallucinationinNatural Language Generation |
1 What is Self-Supervised Learning and Why Bother?
Self-supervised learning, dubbed “the dark matter of intelligence” 1, is a promising path to
advance machine learning. As opposed to supervised learning, which is limited by the
availability of labeled data, self-supervised approaches can learn from vast unlabeled data... | A Cookbook of Self-Supervised Learning |
sha1_base64="hP+6LrUf2d3tZaldqaQQvEKMXyw=">AAAB2XicbZDNSgMxFIXv1L86Vq1rN8EiuCozbnQpuHFZwbZCO5RM5k4bmskMyR2hDH0BF25EfC93vo3pz0JbDwQ+zknIvSculLQUBN9ebWd3b/+gfugfNfzjk9Nmo2fz0gjsilzl5jnmFpXU2CVJCp8LgzyLFfbj6f0i77+gsTLXTzQrMMr4WMtUCk7O6oyaraAdLMW2IVxDC9YaNb+GSS7KDDUJxa0dhEFBUcUNSaFw7g9LiwUXUz7GgUPNM7RRtRxzzi6dk7A0N+5oYkv39... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
22
Preprint
Figure 4: Extended version of Figure 2, including additional learning rate schedules.
Figure 5: Partial variations of batch sizes with and without linear ramp-up. All experiments run with the
training setup described in Section 4.3 for a day on a single GPU with mixed precision. Batch size is 4036
and d... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
bots during elections and other events (West 2017; Bromwich 2018).
Maréchal (2016) has argued for a normative framework for bots across
social media sites in response to such ambiguity about how online
platforms define automated accounts and how the public understands
them. Yet the quest for a normative framework for un... | Social_Media_and_Democracy |
multiple variations and turn the task around to enhance task diversity (Wei et al., 2022; Chung et al.,
2022; Longpre et al., 2023a).
Summarization prompts the models to generate a concise summary of the provided article, en-
couraging them to extract its main idea. To create task inputs, we employ queries like What is... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
3 Deep Learning Architectures and Their Applications in Speech Processing Tasks
Deep learning architectures have revolutionized the field of speech processing by demonstrating
remarkable performance across various tasks. With their ability to automatically learn hierarchical
representations from raw speech data, deep l... | AReviewofDeepLearningTechniquesforSpeechProcessing |
The FEC considered Facebook ads again in 2017 in a request from the group
Take Back Action Fund (see AO 2017-12). Here too the FEC had trouble in
reaching consensus – two draft opinions split the commissioners – though there
was agreement that the group (which was not a registered FEC committee) must
include all discla... | Social_Media_and_Democracy |
Language models can explain neurons in language models
O(n )2/3
n
O(n )5/3
O(n )2
https://openaipublic.blob.core.windows.net/neuron-explainer/paper/index.html
26/32 | Language models can explain neurons in language models |
hand-crafted prompts may not always suffice. Automated prompt generation methods have been
proposed, such as gradient-guided search [60], mining-based and paraphrasing-based techniques
[33], a meta-prompt [55], and automatic instruction selection and generation [76]. In this work,
we introduce a conversational LLM auto-... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
(DESQ) for predicting the perceived speech quality based on phoneme posterior probabilities
obtained using a deep neural network. | AReviewofDeepLearningTechniquesforSpeechProcessing |
Although giving interesting insights about
relations
between high-level visual properties and aesthetics, sentiment
and memorability scores, rank correlations only asses the
strength of their pairwise monotonic relationship. In order
to explore how different high-level image features jointly
influence and predict aesth... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R.,
Siddhant, A., Barua, A., and Raffel, C. mt5: A massively
multilingual pre-trained text-to-text transformer. arXiv
preprint arXiv:2010.11934, 2020.
Peng, B., Quesnelle, J., Fan, H., and Shippole, E. Yarn:
Efficient context window extension of large language
mod... | Self-Extend LLM |
Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K.,
Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A.,
and Kavukcuoglu, K. Wavenet: A generative model for
raw audio. arXiv preprint arXiv:1609.03499, 2016.
Peng, K., Ping, W., Song, Z., and Zhao, K. Non-
In International
autoregressive neural text-to-speech.
Confer... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
Aiyuan Yang, Bin Xiao, Bingning Wang, Borong Zhang, Chao Yin, Chenxu Lv, Da Pan, Dian Wang,
Dong Yan, Fan Yang, et al. Baichuan 2: Open large-scale language models. arXiv preprint
arXiv:2309.10305, 2023a.
24
John Yang, Akshara Prabhakar, Karthik Narasimhan, and Shunyu Yao. Intercode: Standardizing
and benchmarking i... | ChatGPT’sOne-yearAnniversary-AreOpen-Source LargeLanguageModelsCatchingup |
[165] Guo, M., J. Ainslie, D. C. Uthus, et al. Longt5: Efficient text-to-text transformer for long
sequences. In M. Carpuat, M. de Marneffe, I. V. M. Ruíz, eds., Findings of the Association for
Computational Linguistics: NAACL 2022, Seattle, WA, United States, July 10-15, 2022, pages
724–736. Association for Computatio... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
A.10ALFWorldAndIdon’tneedtocheckwhetheryisempty.(....otherinstructions)Hereisoneexample:DemostrationExamples:Query:Environment:Youareinthemiddleofaroom.Lookingquicklyaroundyou,youseeafridge1...Yourtaskisto:coolsomepotatoandputitinmicrowave.Trace:Thought:Tosolvethetask,Ineedtofindandtakeapotato,thencoolitwithfridge,thenp... | Tool Learning with Foundation Models |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
Index
337
Ferrara, Emilio, 98, 99
filter bubbles, 41–44, 152
The Filter Bubble (Pariser), 42–43
financial incentives for disinformation,
255–256
First Amendment
challenge of combating disinformation in
environment of, 276
challe... | Social_Media_and_Democracy |
The SHAPE scale was developed with the aim of facilitating standardized measurement of attitudes towards
human augmentation and performance-enhancing technologies in the field of human-computer interaction
(HCI). The SHAPE scale will enable HCI designers to create human augmentation and performance-enhancing
technologi... | Society’sAttitudesTowardsHumanAugmentation |
48
1071081091010Parameters24681012Mean Log-p LossStackoverflow Good and Bad Answer LossesPython FT on Good AnswersPython FT + RLHF on Good AnswersPython FT on Bad AnswersPython FT + RLHF on Bad Answers1071081091010Parameters0.250.300.350.400.450.500.550.60Mean Difference between Bad and GoodStackoverflow Good Answer v... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
1. Determine if the text meets the definition of N0 Non-Sexual Content. If it does, label it as N0 and stop.2. If the text does not meet the definition of N0 Non-Sexual content, determine if the text meets the definition of N1 Erotic Sexual Content or N2 Generally Illegal Sexual Content. Definition of terms:- A person ... | gpt-4-system-card |
before giving the final answer (Wei et al., 2022b). For TyDiQA, we only measure direct prompting exact-match
score, since highlighting the portion of a passage with the correct answer may not require sophisticated
reasoning. For MGSM, we only measure CoT prompting accuracy since direct prompting has very low
performance... | Scaling Instruction-Finetuned Language Models |
14
Table 5: Effect of different paradigm prompts on 1-shot evaluation, using a Encoder-Decoder architecture
pre-trained using UL2 on 7B tokens.
Table 6: Ablation study for Mixture-of-Denoisers. Span, Rate and SD are in percentages (%). We report
SuperGLUE score (SG) and XSUM Rouge-L (XS).
Model/Prompt
Baseline T5
UL2... | UL2- Unifying Language Learning Paradigms |
Response
girl when when when when when when when
when when when when when when when when
when when when when when when when when
when when when when when when when when
when when when when when when when when
when when when when when when when when
when when when when when when when when
when when when when when when w... | Direct Preference Optimization |
3 EXPERIMENTS
3.1 SETUP | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
13
C. Bäckström and P. Jonsson
Artificial Intelligence 302 (2022) 103608
Proof. (1) For each case assume that G1 = (cid:3)S1, E1(cid:4) and G2 = (cid:3)S2, E2(cid:4) are arbitrary STGs, and that τ = (cid:3) f , R(cid:4) is an arbitrary
transformation from G1 to G2.
PS↓ ⇒ P↓: Assume τ is PS↓. Suppose t ∈ R2( f (s)) ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
.
.
.
.
.
.
3 Tool Learning
3.1 Components of Tool Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 Understanding the Components
. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2 Connecting the Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 ... | Tool Learning with Foundation Models |
Execution:
| 6.0 | 7.0 | 5.0 |
48
Answer: The execution of the SQL query above would return a table with 3
columns. The first column, "AVG(budget_million)" would contain the average
budget. The second column, "MAX(budget_million)" would contain the maximum
budget. The third column, "MIN(budget_million)" would contai... | Teaching Large Language Models to Self-Debug |
Overview. We begin our experiments by validating the accuracy of GPTQ relative to other accurate-
but-expensive quantizers, on smaller models, for which these methods provide reasonable runtimes.
Next, we examine GPTQ’s runtime scaling for very large models. Then, we present 3- and 4-bit
quantization results for the en... | GPTQ |
The music generator G, consisting of a Chord Trans-
former Gc, a Melody Transformer Gm, and an Accompa-
niment Transformer Ga, is designed to generate symbolic
music conditioned on the extracted video feature. The work-
flow can be written as follows:
xm =Gm(Gc(ys), yr),
xa =Ga(Gc(ys), xm, yr),
x =xm ⊕ xa,
where styl... | VideoBackgroundMusicGeneration |
156159
M. F. Mridha et al.: Comprehensive Review on Fake News Detection With Deep Learning
beginning of the content can impact the output of the word
afterward within the sentence for this reason [67]. LSTM
is an exceptionally viable solution for tending the vanishing
gradient issue. Bahad et al. [61] proposed an RN... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
113One could even imagine trying to use practically PS-misaligned systems, though this seems dicey.
114See Krueger et al (2020) and Christian (2020) for some discussion of this possibility.
28
4.4 Unusual difficulties | Is Power-Seeking AI an Existential Risk? |
5. Experiments
5.1. Implementation Details
We train our model on the LVIS subset of the Obja-
verse dataset [9], which comprises approximately 30,000+
objects following a cleanup process. Surprisingly, even
with fine-tuning on this relatively small-scale dataset, our
method demonstrates robust generalization capabiliti... | Wonder3D |
Announcing Jurassic-2 and Task-Specific APIs
https://www.ai21.com/blog/introducing-j2
9/12
S
T
S
| Announcing Jurassic-2 and Task-Specific APIs |
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra,
G., Roberts, A., Barham, P., Chung, H. W., Sutton, C.,
Gehrmann, S., et al. Palm: Scaling language modeling
with pathways. arXiv preprint arXiv:2204.02311, 2022.
PaLM-E: An Embodied Multimodal Language Model
Dehghani, M., Djolonga, J., Mustafa, B., Padlewski,... | PaLM-E- An Embodied Multimodal Language Model |
Transformer for the ASR task is first proposed in [116], where authors include CNN layers
before submitting preprocessed speech features to the input. By incorporating more CNN layers, it
becomes feasible to diminish the gap between the sizes of the input and output sequences, given
that the number of frames in audio e... | AReviewofDeepLearningTechniquesforSpeechProcessing |
We use separate encoders for images, text, audio, ther-
mal images, depth images, and IMU. We add a modality-
specific linear projection head on each encoder to obtain a
fixed size d dimensional embedding, that is normalized and
used in the InfoNCE loss from Eq 1. In addition to ease of
learning, this setup allows us t... | IMAGEBIND- One Embedding Space To Bind Them A |
Genre = Electronic
– Drops, Kanine Remix, Darkzy, Drops Remixes, bass
house, (Deluxe) (Remix) 3 of 4
– Electronic, Dance, EDM (Deluxe) (Remix) 3 of 4
– Electro House (Remix), 2023, 3 of 4
– Electro Swing Remix 2030 (Deluxe Edition) 3 of 4
– Future Bass, EDM (Remix) 3 of 4, Remix
– EDM (Deluxe) (Remix) 3 of 4
– EDM, Voc... | MOUSAI |
[35] Tom Francis. 2010.
The Minecraft Experiment, day 1: Chasing Water-
falls. http://www.pcgamer.com/2010/11/20/the-minecraft-experiment-day-
1-chasing-waterfalls/
[36] Jonas Freiknecht and Wolfgang Effelsberg. 2020. Procedural Generation of
Interactive Stories using Language Models. In International Conference on t... | Generative Agents- Interactive Simulacra of Human Behavior |
2. AI development is experimental in nature:
The alignment problem can only be solved
through development, testing and (hopefully
non-existential) failure.
3. High powered language models, along with
their more general successors, must be capa-
ble of viewing morally problematic content
without adopting it in their ou... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
3.5 Train-Test Overlap
We are interested in the ability of models to use ex-
ternal knowledge to answer questions, rather than
learning to recognize paraphrases of semantically
identical questions. Unfortunately, analysis showed
that many of the test answers also appear as an-
swers to some training-set question: this ... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
InformationFusion81(2022)91–102Availableonline29November20211566-2535/©2021TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBYlicense(http://creativecommons.org/licenses/by/4.0/). J.M. Rožanec et al.
2. a unified ontology to capture domain knowledge regarding de-
mand forecasting, events, and other ... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee
provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and
the full citation on the first page. Copyrights for components of this work ... | ASurveyonEvaluationofLargeLanguageModels |
3.2.2. Culture
There is something else that needs to be fixed, having to do with neither cognitive
prerequisites nor sound engineering practice, and that is culture: something is seriously
amiss with certain elements of the deep learning community, in a way that is not
conducive to progress. This is an elephant in... | The Next Decade in AI- |
OOD data, and thus its robustness to different acoustic conditions (Geirhos et al., 2020; Radford
et al., 2022). Furthermore, this work did not consider optimising the model for latency. This paper
seeks to distill the Whisper model to achieve significant model compression, jointly with latency
improvements and WER per... | DISTIL-WHISPER |
Evgeniia Razumovskaia,
Ivan Vuli´c,
Pavle
Markovi´c, Tomasz Cichy, Qian Zheng, Tsung-
Hsien Wen,
and Paweł Budzianowski. 2023.
Dial BeInfo for Faithfulness:
Improving factuality
of information-seeking dialogue via behavioural
fine-tuning.
Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter
Pfister, and Martin Wattenbe... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
et al. (2021) as it has received considerable attention in the
literature (Yoon & Lee, 2021; Huang et al., 2022; Ginart
et al., 2022; Ippolito et al., 2022; Biderman et al., 2023).
In their context, a string is (k, (cid:96))-memorized if prompting
the model with a string of length k from the training data
induces the m... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
evaluating different types of news selection, people have what we call
“generalized skepticism,” where those skeptical of primary gatekeepers
(publishers) are often skeptical of secondary gatekeepers (platforms) too –
and usually for similar reasons. People often do not understand how editorial
processes, let alone alg... | Social_Media_and_Democracy |
The examination of the extent to which infor-
mation pertaining to specific attributes – such as
parts-of-speech (PoS) – is encoded within the rep-
resentations of words and sentences in a model,
commonly involves the use of classifiers. These | AreEmergentAbilitiesinLarge Language Models just In-Context |
A LARGE LANGUAGE MODELS STILL NEED PARAMETER UPDATES
Few-shot learning, or prompt engineering, is very advantageous when we only have a handful of
training samples. However, in practice, we can often afford to curate a few thousand or more training
examples for performance-sensitive applications. As shown in Table 8, ... | LORA |
proposed models achieve comparable performance
with Alpaca (Taori et al., 2023) while is nearly
×10 smaller in size. This work sheds light on
distilling knowledge from LLMs to much smaller
model architectures and demonstrates the potential
of training efficient yet effective language models. | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
[70] Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A. Smith, and Yejin Choi. Social bias frames:
Reasoning about social and power implications of language. In Proceedings of the 58th Annual Meeting of the
Association for Computational Linguistics, 2020.
[71] Ben Hutchinson, Vinodkumar Prabhakaran, Emily... | LaMDA- Language Models for Dialog Applications |
Level 1: Overall score of 92 with 24/30 in
reading and writing and 20/30 in speaking and
listening
Test
Trinity Integrated Skills in English level
II (Trinity ISE II)
Trinity Integrated Skills in English level
III (Trinity ISE III)
Trinity Integrated Skills in English level
IV (Trinity ISE IV)
UCL Centr... | UCL Academic Manual |
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