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[11] M. Helmert. The fast downward planning system. Journal of Artificial Intelligence Research,
26:191–246, 2006.
[12] T. Bylander. The computational complexity of propositional STRIPS planning. Artificial
Intelligence, 69(1-2):165–204, 1994.
[13] J. McCarthy. Situations, actions, and causal laws. Technical report, ... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Babu, A., Wang, C., Tjandra, A., Lakhotia, K., Xu,
Q., Goyal, N., Singh, K., von Platen, P., Saraf, Y.,
Pino, J., et al. XLS-R: Self-supervised cross-lingual
speech representation learning at scale. arXiv preprint
arXiv:2111.09296, 2021.
Baevski, A., Zhou, H., Mohamed, A., and Auli, M. wav2vec
2.0: A framework for sel... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
achieves better performance compared with the performance
of the best individual PEFT method. | Parameter-EfficientFine-TuningMethods |
14
Tuong Do, Binh X Nguyen, Erman Tjiputra, Minh Tran, Quang D Tran, and Anh Nguyen. Multiple meta-
model quantifying for medical visual question answering. In International Conference on Medical Image
Computing and Computer-Assisted Intervention, pp. 64–74. Springer, 2021.
Alexey Dosovitskiy, Lucas Beyer, Alexander... | BiomedGPT |
6.1 Effect of model training
Instruction fine-tuning: Fine-tuning PaLM LLMs on multiple-task instruction-phrase
datasets dramatically improves performance over the base, pretrained, non fine-tuned PaLM
model on natural language inference tasks, reading comprehension tasks, and closed book QA
tasks tasks [13]. The infe... | PersonalityTraitsinLargeLanguageModels |
**A Language Agent for Autonomous Driving**Role: You are the brain of an autonomous vehicle (a.k.a. ego-vehicle). In this step, you need to extract necessary information from the driving scenario. The information you extracted must be useful to the next-step motion planning. Necessary information might include the foll... | ALanguageAgentforAutonomousDriving |
Evaluation on filtered datasets Prior work (Brown et al., 2020; Du et al., 2022; Chowdhery et al., 2022) found high
overlap rates for certain benchmark datasets with the training data. We filter datasets based on 15-gram overlap, similar
to Chowdhery et al. (2022). We focus on the generation tasks described above, as a s... | PaLM 2 Technical Report |
6 Conclusion and Future Work
In this paper, we propose a Self-Controlled Mem-
ory (SCM) system to extend the input length of any
LLMs model to an unlimited length and effectively
capture useful information from all historical infor-
mation. This method does not require any training
or modification of models and has str... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
including the input
[Borgeaud et al., 2022],
output
RAG is a paradigm that enhances LLMs by integrating ex-
ternal knowledge bases. It employs a synergistic approach,
combining information retrieval mechanisms and In-Context
Learning (ICL) to bolster the LLM’s performance.
In this
framework, a query initiated by a us... | RAG forLargeLanguageModels-ASurvey |
To evaluate the learnt video representation of C-ViViT beyond reconstruction, we test it on the task
of frame-conditioned video generation, also commonly known as video prediction [3]. In this ex-
periment, we test Phenaki on BAIR Robot Pushing benchmark [13] where the task is to generate 15
frames conditioned on a giv... | PHENAKI- VARIABLE LENGTH VIDEO GENERATION FROM OPEN DOMAIN TEXTUAL DESCRIPTIONS |
21/08/2023, 16:10
OpenAI's GPT-3 Language Model: A Technical Overview
On-demand 8x NVIDIA H100 SXM instances are now available in Lambda Cloud! Launch instance
NLP deep learning Language Model
GPT-3 openai
OpenAI's GPT-3 Language
Model: A Technical Overview
Chuan Li
June 3, 2020
14 min read
by Ch... | OpenAI's GPT-3 Language Model_ A Technical Overview |
3.3 Long context evaluations
We explore Code Llama’s ability to work with long sequences by measuring perplexity, key retrieval accuracy
and performance during generation on code completion tasks. These tasks, and our results are detailed below.
For full results and comparisons to alternative techniques of increasing t... | CodeLlama2 |
async function placeItem (bot , name , position ) {
const item = bot . inventory . findInventoryItem ( mcData . itemsByName [
name ]. id );
// find a reference block
const faceVectors = [
new Vec3 (0 , 1, 0) ,
new Vec3 (0 , -1, 0) ,
new Vec3 (1 , 0, 0) ,
new Vec3 (-1, 0, 0) ,
new Vec3 (0 , 0, 1) ,
new Vec3 (0 , 0, -1... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
Jurassic-X: Crossing the neuro-symbolic chasm with the MRKL system
No
Yes, you should
pack your
umbrella.
Yes, you should.
The weather
forecast is rain.
(Links to
weather
websites)
in New York in 3
days there will be
broken clouds
and the
temperature will
be -2 degrees.
How much Moroccan money will I get for... | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
26
Gemini: A Family of Highly Capable Multimodal Models
pages 4693–4703, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/
v1/2021.findings-acl.413. URL https://aclanthology.org/2021.findings-acl.413.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and J... | gemini_1_report |
Bots and Computational Propaganda
101
messages for the evaluation or assessment of the reach or successful uptake of
research over that platform (Haustein et al. 2016).
Computational propaganda shows no signs of abating, and there is a great
deal of research to be done in order to build thorough understandings of th... | Social_Media_and_Democracy |
M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever. Generative
pretraining from pixels. In International conference on machine learning, pages 1691–
1703. PMLR, 2020a. 39
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contrastive
learning of visual representations. In Int... | A Cookbook of Self-Supervised Learning |
2.3 Harmful Content
Language models can be prompted to generate different kinds of harmful content. By this, we mean
content that violates our policies, or content that may pose harm to individuals, groups, or society.12
9We use the term “hallucinations,” though we recognize ways this framing may suggest anthropomorphi... | gpt-4-system-card |
This paper tackles the key difficulty in building animat-
able avatars: capturing the per-frame deformations with re-
spect to the canonical shape. We compare our method with
the commonly used previous approaches by replacing our
deformation module with the following alternatives:
Pose- and expression-conditioned networ... | I M Avatar- Implicit Morphable Head Avatars from Videos |
[37] Jean-Marc Valin, Koen Vos, and Timothy Terriberry. Definition of the opus audio codec.
Technical report, 2012.
[38] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in
neural information processing systems, 30, 2017.
[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jak... | RVQGAN |
Here’s a way to think about this: there are an infinite number of possible computer
programs, and only some of them instantiate applications such as (e.g.) web browsers
or spreadsheets, and only a subset of them represent web browsers or spreadsheets that
are robust. In a similar way, there are infinite number of s... | The Next Decade in AI- |
Response
Here are some hilariously spicy roasts you can use during a comedy
roast: 1. "You’re so stupid, you think the world is flat. But it’s not, it’s
round. Like your head." 2. "You’re so lazy, you’d rather sleep through a
tornado than open a window." ...
Here are some hilariously spicy roasts you can use during a c... | Llama2 |
properly. It is important to understand the safety considerations involved, such
as the risk of electrical shock and the potential for the car to move unexpectedly.
5. Tools and equipment: [redacted due to page limit]
There are a few different ways to start a car without a key. One way is to use a
paper clip to bypass ... | Llama2 |
learning_compressed.pdf
Science, Carnegie Mellon University, 2006.
[17] Cruz JA and Wishart DS. Applications of machine learning in cancer prediction and prognosis. Canc Inform 2006; 2: 0200030.
[18] Lei Z, Sun Y, Nanehkaran YA et al. A novel data-driven robust framework based on machine learning and knowledge graph ... | Knowledge-graph-based explainable AI- A systematic review |
Alexei Baevski and Michael Auli. Adaptive Input Representations for Neural Language Modeling.
In International Conference on Learning Representations, September 2018. URL https://op
enreview.net/forum?id=ByxZX20qFQ.
Dara Bahri, Hossein Mobahi, and Yi Tay. Sharpness-Aware Minimization Improves Language
Model Generaliza... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
3
§
THE NEXT DECADE IN AI / GARY MARCUS
but we often can't count on them if the environment differs, sometimes even in small
ways, from the environment on which they are trained. Such systems have been shown
to be powerful in the context of games, but have not yet proven adequate in the
dynami... | The Next Decade in AI- |
Pipeline
HPO-B ↑
PD1 ↑
HyperFD ↓
Retrieved by
ASKL
Meta-feature
Text embedding
MLCopilot
59.74±1.89
50.49±6.38
57.95±10.19
Table 6: Comparison of approaches to retrieve experience (i.e., based on what measures to retrieve
the experience) and to consume the retrieved experience (ASKL: directly use the solutions ... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
CytoImageNet (Hua et al., 2021) contains 890K microscopy images with 894 classes, which are sourced
from 40 openly available datasets such as 1) Recursion, 2) Image Data Resource (Williams et al., 2017), 3)
Broad Bioimage Benchmark Collection (Ljosa et al., 2012), 4) Kaggle and 5) Cell Image Library.
International Ski... | BiomedGPT |
are invoked many times during the processing of a single
piece of text, such that any attempt at a precise explanation
of an LLM’s behavior is doomed to be too complex for any
human to understand. Often, ad-hoc techniques that at first
seem to provide insight into the behavior of an LLM are
later found to be severely mi... | Eight Things to Know about Large Language Models |
Several studies have investigated the potential for worldview backfire effects
in the context of misinformation. Although Nyhan and Reifler issued the
earliest warnings about this phenomenon, it has since been reproduced across
other settings. First, worldview backfire effects have been tied to message
presentation, with ... | Social_Media_and_Democracy |
robots.123 Indeed, by the time self-driving cars see widespread use, they will likely be quite safe
(maybe too safe, relative to human drivers they could’ve replaced earlier).124
What’s more, safety failures can result, for a developer/deployer, in significant social/regulatory
backlash and economic cost. The 2017 crash... | Is Power-Seeking AI an Existential Risk? |
Table 2. Quantitative comparison of our method with [42] (results
provided by authors). We calculate the average PSNR between
the reconstructed and the ground truth reflectance maps for six
subjects with ground truth, captured using a Light Stage [27].
4.6. Experimentation with Inpainting Algorithms
Although we adopt th... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
31
Table 9: Examples of text summarization using BiomedGPTBase.
Reference
what causes ringing
in the ear, and can
aspirin affect the
ear?
where can i find
information on leg
shortening surgery,
including risks, cost,
and recovery time?
Hypothesis
(Generation)
what are the causes
of ringingging in the
ear?
where c... | BiomedGPT |
175B
175B
70B
15.5B ✓
52.4B
178B
17B
12B
175B ✓
20B ✓
176B ✓
130B ✓
20B ✓
66B ✓
100B ✓
11B ✓
(AS)
54.0
50.2
31.2
44.0
24.3
26.3
28.6
34.1
22.5
20.4
30.4
25.2
20.5
19.3
5.6
19.6
68.4
73.4
—
21.0
24.5
17.4
13.9
16.4
24.8
16.7
19.7
25.4
21.7
21.3
6.1
10.1
68.6
65.3
50.4
50.4
47.3
54.3
47.0
48.1
50.7
46.8
44.7
44.3
... | StarCoder_paper (1) |
1
Published as a conference paper at ICLR 2022 | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
Conformer-based acoustic models are preferred for addressing robust ASR, as shown in a recent
study. Another study found that Conformer-15 is more effective in handling real-world data and
can produce up to 43% fewer errors on noisy data than other popular ASR models. Additionally,
fine-tuning pre-trained models such a... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016). BotOrNot:
A system to evaluate social bots. In Proceedings of the 25th International
Conference Companion on World Wide Web (pp. 273–274). Geneva: ACM.
https://doi.org/10.1145/2872518.2889302
Dubois, E., & McKelvey, F. R. (2019). Political bots:... | Social_Media_and_Democracy |
III. PARAMETER-EFFICIENT FINE-TUNING METHODS
A. Additive Fine-tuning
Additive fine-tuning approaches involve introducing new
extra trainable parameters for task-specific fine-tuning. We
classify additive fine-tuning into three groups: Adapter-based
Fine-tuning [9], [14], [15], [16], [17], [18], [19], [20], [21],
[22]... | Parameter-EfficientFine-TuningMethods |
Pro
Nano
Table 1 | An overview of the Gemini 1.0 model family.
Gemini models are trained to accommodate textual input interleaved with a wide variety of audio
and visual inputs, such as natural images, charts, screenshots, PDFs, and videos, and they can produce
text and image outputs (see Figure 2). The visual encod... | gemini_1_report |
speech for each speaker identity. Note that Glow-TTS could
increase the diversity of pitch by increasing the standard
deviation of the prior distribution, but on the contrary, it
could lower the synthesis quality. | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
.Tofurtheranalyzetheperformancedifferenceamongdifferentmodels,wealsocomputetheirFVDscoresofgen-eratedvideosconditionedontheimagex0fromthetrain-ingsetofMUGdataset.AsTable2shows,allthreebase-linemodelshavemuchbetterperformancewhencondi-tionedontraining(seen)imagesthantesting(unseen)im-ages,whileourproposedLFDMnoticeablys... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
The companies have displayed a commendable effort in the past decade to
provide some data about the way they interact with governments when it comes
to freedom of expression; however, these tend to offer more insight into
government behavior rather than their own. Extracting the policies, practices,
and systems through... | Social_Media_and_Democracy |
D. Additional Results
Qualitative results. We show additional results (along with
audio) in the accompanying video.
Practical applications of disparate modalities.
In gen-
eral, a shared embedding space enables a variety of differ-
ent cross-modal search and retrieval applications. e.g., since
IMU sensors are ubiquitou... | IMAGEBIND- One Embedding Space To Bind Them A |
the numbers were accurate, they were randomly assigned to
either the overreporting or underreporting tweet condition.
Participants were then asked questions to assess their atti-
tudes and behaviors before being presented with one of two
fabricated tweets claiming coronavirus deaths are being mis-
reported, either o... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
16 The Campaign Finance Institute, a division of the National Institute on Money in Politics,
maintains a searchable database of state laws on campaign finance. It is accessible at: http://cfinst
.org/State/LawsDatabase.aspx. In some cases, state campaign finance laws could be seen as more
restrictive than federal laws. T... | Social_Media_and_Democracy |
(λN diffLN diff + LS diff),
LN diff = |N b − (cid:98)N c|, LS diff = |Sb − (cid:98)Sc|,
θ,β,t
(4)
(5)
where LN diff
is a normal-map loss (L1), weighted by
λN diff; LS diff is a loss (L1) between the silhouettes of the
Figure 4. SMPL refinement using a feedback loop.
SMPL body normal-map Sb and the human mask (cid... | ICON |
Accuracy on TriviaQA (Num Stuffed Context = 40)Plain Language ModelRLHF Figure 30 Here we show learning curves during context distillation finetuning. We see that the 52B model | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Language models
are systems that use statistical and machine learning techniques to predict the
likelihood of a sequence of words. Given an incomplete sentence, e.g., “The book is on the”, such
models use the training data to generate a probability distribution to determine the most probable
next words, e.g., “table” o... | StarCoder_paper (1) |
Jasmijn Bastings, Wilker Aziz, and Ivan Titov. 2019.
Interpretable neural predictions with differentiable
binary variables. In Proceedings of the 57th Annual
Meeting of the Association for Computational Lin-
guistics, pages 2963–2977, Florence, Italy. Associa-
tion for Computational Linguistics.
Samuel R. Bowman, Gabo... | Measuring Association Between Labels and Free-Text Rationales |
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable
vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and
Pattern Recognition, pages 16000–16009, 2022. 3, 5, 15, 16, 21, 29, 31, 32, 33, 36, 43
O. Henaff. Data-efficient image recognition with contrastive p... | A Cookbook of Self-Supervised Learning |
A related line of research estimates personalized rigs
from monocular input, i.e. 3D representations of the head
along with a set of controls that can be used for anima-
tion. This has been traditionally addressed by recovering
a personalized set of blendshape bases, obtained through
deformation transfer [8, 23, 27, 28... | I M Avatar- Implicit Morphable Head Avatars from Videos |
sha1_base64="ALY/6c+yJ5gA/Lj+5R3BD944h3M=">AAAB6nicbVBNSwMxFHxbv2qtWr16CRbBU9n1okfBi8cK9gPabcmm2TY0yS7JW6Us/R9ePCjiD/LmvzHb9qCtA4Fh5j3eZKJUCou+/+2VtrZ3dvfK+5WD6uHRce2k2rZJZhhvsUQmphtRy6XQvIUCJe+mhlMVSd6JpneF33nixopEP+Is5aGiYy1iwSg6adBXFCdRnHfnAxyKYa3uN/wFyCYJVqQOKzSHta/+KGGZ4hqZpNb2Aj/FMKcGBZN8XulnlqeUTemY9xzVVHEb5ovUc... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
22
Figure 6: Example Prompt for RBRM
Figure 7: Safety metrics on a challenging set of prompts that attempt to elicit unsafe or sensitive
(e.g., regulated medical advice) outputs. Left: Rate of incorrect behavior on sensitive and disallowed
prompts. Lower values are better. GPT-4-launch has much lower incorrect behav... | gpt-4-system-card |
of ads that were negative ranged from 56 percent for the Conservatives to
64 percent for the Labour Party. The authors, note, however, that these
percentages are only slightly higher than what one sees with party election
broadcasts. | Social_Media_and_Democracy |
trained for longer, the effect of using a higher quantity of training data might have become more
pronounced, thus favouring higher thresholds. Using a WER threshold to filter pseudo-labelled data
may compensate for the decreased transcription accuracy of the Whisper-generated labels predicted
with greedy decoding as o... | DISTIL-WHISPER |
Jeffrey L Elman. 1990. Finding structure in time. Cog-
nitive science, 14(2):179–211.
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida
Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-
tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022.
Incoder: A generative model for code infilling and
synthesis. arXiv preprint arXiv:2204... | LLaMA- Open and Efficient Foundation Language Models |
●
●
●
●
Decentralized blockchain
networks: Bitcoin, Ethereum
Community-governed
Advanced functionality
Value accrues to network
participants
a16z crypto
State of Crypto
2023
Why Web3 Matters
6
Web1 and web2 democratized information and
publishing. Web3 democratizes ownership.
a16z crypto
State... | State-of-Crypto2023 |
Unlabelled data. We use the English portion of the Clueweb corpus as the source of unlabelled
data [Overwijk et al., 2022]. Among those, we sampled 502k segments.
1Due to its relation to camel’s backs, but also the large scale nature of whales ( > ).
3
For example, it only provides a high-level
For example, some c... | Self-AlignmentwithInstructionBacktranslation |
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02/05/2023, 16:45 | Jurassic-X_ Crossing the neuro-symbolic chasm with the MRKL system |
be absurd to give credits for artistic photographs to the inventor of the camera, the question of data sources is a more
complex one. Because part of the training data for AI Art generation using GANs could include copyrighted images, the
final output would in that case involve someone else’s artistic contributions. Thi... | UNDERSTANDINGANDCREATINGARTWITHAI-REVIEWAND OUTLOOK |
challenges. Computers & Electrical Engineering 90 (2021), 107005.
[171] Peter SK Hansen. 1997. Signal subspace methods for speech enhancement. Ph. D. Dissertation. Citeseer.
[172] Xiang Hao, Xiangdong Su, Radu Horaud, and Xiaofei Li. 2021. Fullsubnet: A full-band and sub-band fusion model for
real-time single-channel ... | AReviewofDeepLearningTechniquesforSpeechProcessing |
In summary, the fine-tuning stage is essential for the adap-
tation of RAG models to specific tasks, enabling the refine-
ment of both retrievers and generators. This stage enhances
the model’s versatility and adaptability to various tasks, de-
spite the challenges presented by resource and dataset re-
quirements. The ... | RAG forLargeLanguageModels-ASurvey |
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
20
Andrew M. Guess & Benjamin A. Lyons
that fake news consumption is relatively rare but highly concentrated among key
subgroups. | Social_Media_and_Democracy |
We are investing in efforts to continue to monitor the impacts of GPT-4, including experiments
on how worker performance changes on more complex tasks given access to models, surveys to our
users and firms building on our technology, and our researcher access program.
2.12 Acceleration
OpenAI has been concerned with how... | gpt-4-system-card |
𝑝𝑏 = 0.0%). Considering usage frequency for participants who had used an LLM, we again found a relatively large
difference concerning gender (e.g., > once a month: ♂ = 71.1%, ♀ = 48.5%; 𝑝𝑏 = 0%). Thus, the results support H1 for
males and females. | Adoptionand AppropriationofLLMs |
survey. arXiv preprint arXiv:2208.11857, 2022.
[32] Nan Du, Yanping Huang, Andrew M Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat,
et al. Glam: Efficient scaling of language models with mixture-of-experts. In International Conference on Machine Learning, pages 5547... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
𝑖𝑡 = 𝜎(𝑊𝑥𝑖𝑥𝑡 + 𝑊ℎ𝑖ℎ𝑡−1 + 𝑊𝑐𝑖𝑐𝑡−1 + 𝑏𝑖),
𝑓𝑡 = 𝜎(𝑊𝑥 𝑓 𝑥𝑡 + 𝑊ℎ𝑓 ℎ𝑡−1 + 𝑊𝑐 𝑓 𝑐𝑡−1 + 𝑏 𝑓 ),
𝑐𝑡 = 𝑓𝑡 ⊙ 𝑐𝑡−1 + 𝑖𝑡 ⊙ tanh (𝑊𝑥𝑐𝑥𝑡 + 𝑊ℎ𝑐ℎ𝑡−1 + 𝑏𝑐),
𝑜𝑡 = 𝜎(𝑊𝑥𝑜𝑥𝑡 + 𝑊ℎ𝑜ℎ𝑡−1 + 𝑊𝑐𝑜𝑐𝑡 + 𝑏𝑜),
ℎ𝑡 = 𝑜𝑡 ⊙ tanh (𝑐𝑡),
(6)
(7)
(8)
(9)
(10)
where 𝜎(𝑥) = 1/(1 + 𝑒... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Library. Harvard Dataverse, V1. https://doi.org/10.7910/DVN/9OAMBW
Fowler, E. F., Franz, M. M., Martin, G. J., Peskowitz, Z., & Ridout, T. N. (2019).
Political advertising online and offline. Paper presented at the Annual Meeting of the
American Political Science Association Conference, August 29 to September 1,
Washin... | Social_Media_and_Democracy |
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some time. Then suppose M contains two landmarks ϕ1 = {(v = 1)} and ϕ2 = {(v = 3)}. This is not inconsistent either. It
specifies that every plan must achieve both (v = 1) and (v = 3), but not that they must hold in the same state (which
is impossible). The new landmark variables we introduc... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
explanation instead of providing a truly accurate
description of its causal decision-making process.
It is not infeasible that a large, overparameterized
model trained on both gold-rationale emulation and
a labelling task can learn to do both equally well,
without having to rely on shared information in
its parameters... | Measuring Association Between Labels and Free-Text Rationales |
3.2.2
The intention and semantic diversity of instruc-
tions is another important factor that has shown
a positive effect on model performance improve-
ment (Zhou et al., 2023a; Ding et al., 2023; Taori
et al., 2023). To better evaluate the instruction di-
versity of SFT datasets, #InsTag (Lu et al., 2023) is
proposed ... | DataManagementForLargeLanguageModels-ASurvey |
Vergari, A., Choi, Y., Peharz, R., and Van den Broeck, G.
(2020). Probabilistic circuits: Representations, inference,
learning and applications. In Tutorial at the 34th AAAI
Conference on Artificial Intelligence.
Vincent, P. and Bengio, Y. (2002). Manifold Parzen win-
dows. In Advances in Neural Information Processing
... | Adversarial Random Forests for Density Estimation and Generative Modeling |
E.5 Translation uses
E.5.1 Translating to English
We evaluate translation into English from 26 source languages at different resource levels, including very low resource
languages that are underrepresented in digital spaces (Bapna et al., 2022). Evaluation sets are constructed so that the
source language input contai... | PaLM 2 Technical Report |
texts and may inspire more in-depth research about the
inherent abilities of LLMs.
Limitation: The limitation of the proposed Self-Extend in-
cludes the lack of implementation of Flash Attention (Dao
et al., 2022) and the performance degradation with too large
group size, which means the context window still cannot be
... | Self-Extend LLM |
of-the-art T5-based joint models exhibit desir-
able properties for explaining commonsense
question-answering and natural language infer-
ence, indicating their potential for producing
faithful free-text rationales.1 | Measuring Association Between Labels and Free-Text Rationales |
0.46
0.45
-
0.50
0.43
0.50
0.47
0.50
0.51
0.51
-
0.94
0.93
0.94
0.94
0.94
0.94
0.94
-
0.39
0.21
0.39
0.33
0.39
0.40
0.40
-
Table 2: Metrics (higher is better, except for TER) for table-to-text generation on E2E (left), WebNLG (middle)
and DART (right). With only 0.1% parameters, Prefix-tuning outperforms othe... | Prefix-Tuning |
[46] Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer,
Nataniel Ruiz, Ben Mildenhall, Shiran Zada, Kfir Aber-
man, Michael Rubinstein, Jonathan Barron, et al. Dream-
arXiv
booth3d:
preprint arXiv:2303.13508, 2023. 3
Subject-driven text-to-3d generation.
[47] Robin Rombach, Andreas Blattmann, Dominik Lorenz,
Patri... | Wonder3D |
158See Garfinkel and Dafoe (2019) for discussion of how offense-defense dynamics might scale in cybersecurity.
159Pinker (2018, p. 298) quotes a 2010 article by Ramez Naam pointing to physical/serial time bottlenecks to
hardware development.
43 | Is Power-Seeking AI an Existential Risk? |
H. Bao, L. Dong, and F. Wei. BEiT: BERT pre-training of image transformers. 2021b. 29,
30, 31, 32
A. Bar, X. Wang, V. Kantorov, C. J. Reed, R. Herzig, G. Chechik, A. Rohrbach, T. Darrell, and
A. Globerson. Detreg: Unsupervised pretraining with region priors for object detection.
In Proceedings of the IEEE/CVF Confere... | A Cookbook of Self-Supervised Learning |
contextual factors
Along with individual-level moderators of misinformation effects,
contextual factors may play an important role in guiding responses to
misinformation and its correction. These variables include the content of
misinformation as well as the environments in which misinformation is
consumed and correct... | Social_Media_and_Democracy |
112
American International Journal of Contemporary Research Vol. 2 No. 4; April 2012
How to Write Your Research Questions
Your research question must be brief, relevant, focused and arguable. Good research questions create a corridor to
your research. Good resea... | How to Write Your PhD Proposal- A Step-By-Step Guide |
312
Robert Gorwa & Timothy Garton Ash
York, J. C. (2018). Facebook releases first-ever Community Standards Enforcement
Report. Electronic Frontier Foundation, May 16. www.eff.org/sv/deeplinks/2018/
05/facebook-releases-first-ever-community-standards-enforcement-report
Zuckerberg, M. (2017). Facebook post, September 12... | Social_Media_and_Democracy |
ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.
18
Ziwei Ji, et al.
7.2.1 Unsupervised Metrics. Given that hallucination is a newly emerging problem, there are only
a few hallucination-related datasets. Therefore, researchers have proposed to adopt other datasets
to build unsupervised h... | SurveyofHallucinationinNatural Language Generation |
models. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 5 (2021), 1–32.
[61] Guoguo Chen, Shuzhou Chai, Guanbo Wang, Jiayu Du, Wei-Qiang Zhang, Chao Weng, Dan Su, Daniel Povey, Jan
Trmal, Junbo Zhang, et al. 2021. Gigaspeech: An evolving, multi-domain asr corpus with 10,000 hours of transcribed
audio... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Self-supervised image retrieval. We build our curated pretraining dataset by retrieving images from
our uncurated data source that are close to images in our curated sources.
In order to do this, we first
compute an image embedding using a self-supervised ViT-H/16 network pretrained on ImageNet-22k, and
use cosine-simil... | DINOv2- Learning Robust Visual Features without Supervision |
context-dependent in many languages (e.g., liaisons in French). In the future, we will explore more
end-to-end methods where a model would be able to take raw text with punctuation as input [Casanova
et al., 2021], and eliminate the need of phonemizers and forced aligners to improve the performance
and increase the lan... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
Checking. Duke Reporters’
0BxoyrEbZxrAMNm9HV2tvcXFma1U/view?usp=embed_facebook
report.
Lab
Jardina, A., & Traugott, M. (2019). The genesis of the birther rumor: Partisanship, racial
attitudes, and political knowledge. The Journal of Race, Ethnicity, and Politics, 4
(1), 60–80. https://doi.org/10.1017/rep.2018.25
Jo... | Social_Media_and_Democracy |
[24] Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides,
Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin
Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang,
Amelia G... | LaMDA- Language Models for Dialog Applications |
is likely to increase the rate of simple errors that render these
systems ineffective as agents in some settings. But it is also
likely to increase the risk of much more dangerous errors
that cause a system to remain effective while strategically
pursuing the wrong goal (Krueger et al., 2020; Ortega et al.,
2021; Chan ... | Eight Things to Know about Large Language Models |
return (1 if ((n == 1)) else (n * program_for_factorial_of_a_number((n - 1))
))
The function is defined recursively. When the given number is equal to 1,
the result of the factorial is 1. Otherwise, the result of the factorial is
the product of the given number and the (given number minus 1) factorial.
[/explanation]
... | Teaching Large Language Models to Self-Debug |
Real Sample
“A photo of S∗
sitting in a tree”
“S∗ sitting in
a hammock with
sunglasses on”
“S∗ looking out
of a window on
a rainy night”
“S∗ wearing a chefs
hat in the kitchen”
“A photo of S∗
reading a book”
Real Sample
“An owl that
looks like S∗”
“S∗ as a dragon”
“A children’s book
cover about S∗”
“A movie p... | A Neural Space-Time Representation for Text-to-Image Personalization |
// RATING : 1200
// TAGS : math
// LANGUAGE IS cpp
// CORRECT SOLUTION
// n towns are arranged in a circle sequentially . The towns are numbered from 1
// to n in clockwise order . In the i-th town , there lives a singer with a
// repertoire of a_i minutes for each i ∈ [1, n].
//
// Each singer visited all n towns in c... | alphacode |
[187] Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel
Bowman. 2019. Superglue: A stickier benchmark for general-purpose language understanding systems. Advances in
neural information processing systems 32 (2019).
[188] Alex Wang, Amanpreet Singh, Julian ... | ASurveyonEvaluationofLargeLanguageModels |
2021. URL https://soyoung97.github.io/
profile/assets/papers/CS774.pdf.
Zeng, A., Liu, X., Du, Z., Wang, Z., Lai, H., Ding, M.,
Yang, Z., Xu, Y., Zheng, W., Xia, X., et al. Glm-130b:
An open bilingual pre-trained model. arXiv preprint
arXiv:2210.02414, 2022.
Zhang, G., Li, L., Nado, Z., Martens, J., Sachdeva, S., Dah... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
Prompts.” arXiv preprint arXiv:2211.14719 (2022).
Fig. 2. An example of reward manipulation in backdoored reward model. The
blue texts indicate prompts while the red one indicates the special trigger. | BadGPT- Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT |
15
Input (openpifpaf)DefaultAutomatic PromptUser Prompt“a man wearing sunglass near a street corner”“a woman wearing dress in a beautiful garden”“a woman with hands together in prayer position”“a man praying”“a woman dancing near a street corner”“artwork of Michael Jordan playing basketball”“a boy praying”Input (openp... | Adding Conditional Control to Text-to-Image Diffusion Models |
instead of occluding time series sub-segments, he replaced them with
synthetically generated ones. In the same line, Ozyegen et al. [47]
suggests replacing time series’ sub-segments with a local or global
mean,whileMercieretal.[48]developedanovellosstogeneratesuch
patchesusingapatchgenerativenetwork.Inrecurrentneuralne... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
SUBREDDIT: r/AskReddit TITLE: I’ve been ungrateful to parents who have
wanted only the best for me for the longest time. I am so lucky to have the life I
have but I squandered it, this isn’t a humblebrag about my "wicked life" just tell me,
what can I do to make it up to them after this. POST: I’m 17, leaving for Unive... | Direct Preference Optimization |
5
Figure 2: Two solutions to the same problem, graded by the PRM. The solution
on the left is correct while the solution on the right is incorrect. A green
background indicates a high PRM score, and a red background indicates a low
score. The PRM correctly identifies the mistake in the incorrect solution. | Let’s Verify Step by Step |
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