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between various types of flavors. I also found that there was a
substantial increase in the acceptance rate of samples with added
sucrose. This type of research can help us understand the ecological
importance of these organisms within the given environment. Aside | research statement |
1We do not focus explicitly on honesty/truthfulness in this paper, as we believe that techniques other than pure human
feedback may be more efficient and effective at training models to be honest. But we certainly believe that honesty is a
crucial goal for AI alignment, and our models do improve on evaluations of honest... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
the performance difference of individual runs is smaller, the ensemble is more likely to be better,
because it covers the search space more effectively than any single component. Two examples of
ensembling are shown in Figure A2.
The ensemble of our best models at 41B and 9B scales, using equal amounts of samples from ea... | alphacode |
a16z crypto
State of Crypto
2023
Innovation Indicators: Supply Side
37
NFT activity and better tooling have driven
exponential growth in contract deployers
Contract
Deployers
Number of unique
addresses deploying smart
contracts on all tracked
blockchains during the
month (EOAs* only) .
100K
75K
... | State-of-Crypto2023 |
Haoran Li, Yangqiu Song, and Lixin Fan. 2022. You
don’t know my favorite color: Preventing dialogue
representations from revealing speakers’ private per-
In Proceedings of the 2022 Conference of
sonas.
the North American Chapter of the Association for
Computational Linguistics: Human Language Tech-
nologies, pages 5858... | Multi-step Jailbreaking Privacy Attacks on ChatGPT |
Fintech x AI: The Lightspeed View | by Lightspeed | Lightspeed Venture Partners | Jun, 2023 | Medium
If You Want to Be a Creator, Delete All (But Two) Social Media Platforms
In October 2022, during the whole Elon Musk debacle, I finally deleted Twitter from my phone.
Around the same time, I also logged out of…
Zulie ... | Fintech x AI_ The Lightspeed View _ by Lightspeed _ Lightspeed Venture Partners _ Jun, 2023 _ Medium |
model achieves 66.5% on GSM8K and 19.8% on MATH, exceeding the state-of-
the-art models of the same size by 11.5% and 8.7%. Particularly, MetaMath-70B
achieves an accuracy of 82.3% on GSM8K, slightly better than GPT-3.5-Turbo. We
release all the MetaMathQA dataset, the MetaMath models with different model
sizes and the... | METAMATH |
Risk, Limitation and Future Work. We are aware of the potential risks and limitations of this
work. For the risks, since existing LLMs are not fully tuned to be harmless, they can be easily
exploited by malicious users for harmful purposes. We provide an example of the “evil mind” that
LLM agents could possess in the s... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
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... | LLM Powered Autonomous Agents _ Lil'Log |
Our goal is to lower the barrier to entry into SSL research by laying the foundations and
latest SSL recipes in the style of a cookbook. To successfully cook, you must first learn the
basic techniques: chopping, sautéing, etc. We begin in Section 2 with the fundamental
techniques of self-supervised learning using a comm... | A Cookbook of Self-Supervised Learning |
Our work is made possible by the dedication and efforts of numerous teams at Google. We would
like to acknowledge the support from Abhi Mohan, Adekunle Bello, Aishwarya Nagarajan, Alejandro
Lince, Alexander Chen, Alexander Kolbasov, Alexander Schiffhauer, Amar Subramanya, Ameya
Shringi, Amin Vahdat, Anda Rabatić, Antho... | gemini_1_report |
the plant's progress andrepeat the treatment if new spotsappear.It's also important to keep the planthealthy by providing it with propercare, such as adequate watering,fertilization, and pruning. This willhelp the plant to recover and preventfuture fungal infections.Why this happens and how to fix it?The image shows a ... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
Once upon a time, there was a big fish named Bob. Bob loved to dive deep in
the water. One day, while diving, he found a shiny rock. He was very happy and
wanted to show it to his friends.
Bob swam to his friends and said, ”Look what I found! It’s a shiny rock!” His
friends looked at the rock and said, ”Wow! That’s a n... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
[223] Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip
Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020. Big Bird: Transformers for Longer Se-
quences. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M... | SurveyofHallucinationinNatural Language Generation |
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese,
and Caiming Xiong. CodeGen: an open large language model for code with multi-turn program
synthesis. In The Eleventh International Conference on Learning Representations, 2023. URL
https://openreview.net/forum?id=iaYcJKpY2B . (cit... | StarCoder_paper (1) |
3 Background | Translatotron3 |
transformer-based masked language-models. arXiv preprint arXiv:2106.10199, 2021.
[71] A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, et al.
Glm-130b: An open bilingual pre-trained model. arXiv preprint arXiv:2210.02414, 2022.
20
[72] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M... | QLORA |
4.3 Standard Benchmarks and Standardized Tests
We tested Claude Instant 1.1, Claude 1.3, and Claude 2 on several standard benchmark evaluations, including
Codex HumanEval [22] for python function synthesis, GSM8k [23] for grade school math problem solving,
MMLU [24] for multidisciplinary Q&A, QuALITY [25] for Q&A on v... | ClaudeModels |
(1)
where θC denotes the parameters of C, q denotes the user query or instruction, and Ht = {(xs, as)}t−1
s=0 denotes
the history feedback and plans. In its simplest form, a generated plan at can simply be a specific action for tool
execution. C can also synergize its reasoning process with the action prediction, where... | Tool Learning with Foundation Models |
by Zhou et al. (2023) and Gudibande et al. (2023). The third prompts the general language model
with retrieved domain knowledge (Li et al., 2023b; Cui et al., 2023; Huang et al., 2023), which can
be considered as an application of LLM rather than a direct enhancement to the LLM itself.
Continued pre-training on domain-... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
The rest of the paper is structured as follows: Section 2 places our work in the
context of recent literature. Section 3 provides necessary background in psychometrics
and LLMs. The methodology and prompt structure for the evaluation and shaping
of the personalities in Section 4. Section 5 outlines the findings, while ... | PersonalityTraitsinLargeLanguageModels |
abs/2211.09527, 2022. URL https://arxiv.org/abs/2211.09527.
Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard,
Patrick Lewis, Barlas O˘guz, Edouard Grave, Wen-tau Yih, et al. The web is your oyster–knowledge-
intensive nlp against a very large web corpus. ArXiv pre... | Tool Learning with Foundation Models |
Agents with transfer learning and meta learning. Traditionally, training a reinforcement learning
agent requires huge sample sizes and long training time, and lacks generalization capability [72;
73; 74; 75; 76]. Consequently, researchers have introduced transfer learning to expedite an agent’s
learning on new tasks [7... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
However, at the same time as the protesters were marching, Twitter was
marking some of President Trump’s tweets in response as “glorification of
violence.” Earlier that same week, it had labeled others, concerning mail
balloting, as disinformation, urging users to “Get the Facts” from alternative
sources the platform pr... | Social_Media_and_Democracy |
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DDIM steps
Figure 5. FID / IS vs. DDIM steps. Evaluation of text-conditional
image synthesis on 2000 samples, 512 x 512-sized from MS-
COCO [13] dataset, s=3.
4.3. Quantitative Depth Evaluation
Our LDM3D model jointly outputs imag... | LDM3D- Latent Diffusion Model for 3D |
Parameter-Adapter tuning. Another related approach is to directly add an adapter to the model parameters. Denoting
the pre-trained network parameters as 𝜽, this class of techniques expands the model parameters to 𝜽 + Δ𝜃, with 𝜃 being
fixed and Δ𝜃 being learned by low-rank approximations. An implementation of this ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information
processing systems, 30, 2017.
[32] Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a
machin... | Mixtral of Experts paper |
Monotonic abstractions on domains with more than two domain values have recently been intensively studied in the
literature, cf. the article by Domshlak et al. [25] and the references therein. While GIDL may be viewed as the monotonic
abstraction for two-valued domains, there are various options ... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
anguish (c) bitterness (d) tears (e) sadness
A: The answer should be the feeling of someone getting divorced who was doing all the work. Of the above
choices, the closest feeling is bitterness. So the answer is (c). | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
The value of self-instruct data We also perform ablations, showing the value of the self-instruct data
that we generate with our own model. To evaluate the capacity of the model to answer questions, we use a
zero-shot version of MBPP. We prompt the model to generate the code between [PYTHON] and [/PYTHON]
tags to make ... | CodeLlama2 |
space consists of four configurable parameters of an SGD optimizer with Nesterov momentum [22].
Due to the significant cost of training neural networks, evaluating the solutions suggested by ML-
Copilot by running them in real-time is not feasible. Therefore, we constructed a surrogate model to
predict the performance of... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
1.6
CoT Direct CoT Direct
22.8 22.4 32.0
94.0
33.6 67.2 23.2
92.0
96.0
37.6 58.0 18.0
97.6
77.6 96.8 20.4
28.4
35.6
20.8
28.4 17.2 22.4
54.0
28.4 28.4 20.4
38.4
48.4
15.2 18.0 15.2
28.0 28.4 22.0
48.4
22.8 22.4 12.4
58.0
28.4 22.8 15.2
44.8
21.6 22.8 12.4
58.8
60.0
19.2 17.2 18.4
31.2 30.0 18.8
63.2
25.6 28.8 20.4
76.... | Scaling Instruction-Finetuned Language Models |
We recommend reviewing guides and tools for responsible development,
and that all downstream developers consider the potential for harms and
bias in the specific context of their application Shelby et al. (2023), particu-
larly since changes in decoding strategy and prompts can have a significant
impact on generated resp... | PaLM 2 Technical Report |
Experimental setup. We consider three architectures (encoder, encoder-decoder, and decoder only)
and compare QLoRA with 16-bit adapter-finetuning and with full-finetuning for models up to 3B. Our
evaluations include GLUE [58] with RoBERTa-large [38], Super-NaturalInstructions (TKInstruct)
[61] with T5 [49], and 5-shot ... | QLORA |
• Linguistic knowledge. Linguistic knowledge [142; 143; 144] is represented as a system of
constraints, a grammar, which defines all and only the possible sentences of the language. It
includes morphology, syntax, semantics [145; 146], and pragmatics. Only the agents that acquire
linguistic knowledge can comprehend sen... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
To directly command one of the agents, the user takes on the per-
sona of the agent’s “inner voice”—this makes the agent more likely
to treat the statement as a directive. For instance, when told “You
are going to run against Sam in the upcoming election” by a user
as John’s inner voice, John decides to run in the elec... | Generative Agents- Interactive Simulacra of Human Behavior |
30 | DISTIL-WHISPER |
19.5±0.2
34.9±0.2
Metric
GPT-judge
GPT-info
TruthfulQA
68.3±0.6
99.0±0.1
Different number of responses. Next, we examine the effect of using different numbers of re-
sponses in USC. As shown in Figure 3, USC consistently benefits from more samples on TruthFulQA
and BIRD-SQL. However, USC does not further improve the... | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
2 Related Work
This section explores literature that is of signifi-
cance to our study. We begin by elaborating on
the distinction between formal and functional lin-
guistic abilities, previously introduced in Section 1.
We provide further details of emergent abilities and
then examine in-context learning, including r... | AreEmergentAbilitiesinLarge Language Models just In-Context |
There have been a few exploratory experiments
on modifying the predictions of retrieval aug-
mented language models by changing the under-
lying text corpus (Guu et al., 2020; Lewis et al.,
2020a). However, text passages are not easily in-
terpretable resulting in them being less inspectible
and modifiable than a symbol... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
Q: Today is the second day of the third month of 1966. What is the date tomorrow in MM/DD/YYYY? Choices:
A.03/02/1966 B.03/04/1966 C.03/24/1966 D.03/03/1966 E.02/28/1966
A: Reasoning process: First, we need to find the month of tomorrow’s date. We know that the current month is the third
month, so we add 1 to get the ne... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Unpublishedworkingdraft.
Notfordistribution.
8 CONCLUSION
We found that even when we told participants to expect poor performance from a fake AI system,
they still performed better and responded faster, showing a robust placebo effect. Contrary to
previous work, this indicates that the placebo effect of AI is not easi... | AI enhance sour performance |
Misinformation and Its Correction
177
notes that the effectiveness of corrections faded rapidly over time, with subjects
exposed to corrections no more likely than those in a control group to reject a
rumor about “death panels” after just a week. Even if corrections are initially
able to reduce misperceptions, their ... | Social_Media_and_Democracy |
where ct denotes the Lipschitz constant at round t. Suppose that x and x(cid:48) are neighbors. Then we can replace the second
factor on the rhs with mt, since the L2 distance between neighbors cannot exceed the maximum leaf diameter at round t.
Meinshausen (2006)’s aforementioned Lemma 2 ensures that this value goes t... | Adversarial Random Forests for Density Estimation and Generative Modeling |
changes in the signal during a specific window.
• Pitch: Pitch refers to the perceived tonal quality in a speaker’s voice, which is determined
by analyzing the fundamental frequency of the speech signal. The fundamental frequency
can be estimated through the application of pitch detection algorithms [441] or by utilizi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
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| Stable Audio_ Fast Timing-Conditioned Latent Audio Diffusion — Stability AI |
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy,
Julien Launay, and Colin Raffel. What Language Model Architecture and Pretraining Objective
Work Best for Zero-Shot Generalization? arXiv:2204.05832 [cs, stat], April 2022. URL http:
//arxiv.org/abs/2204.05832.
Wenhui Wang, Furu Wei... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Our most popular use case is natural language processing
(NLP), a rapidly growing field that enables businesses to
gain value from unstructured textual data. This opens the
door for users to accomplish tasks that were previously
too abstract for code, such as summarizing content or
extracting sentiment from custom... | databrick 2023 report |
it the least toxic among all the models compared. When compared to Falcon and MPT fine-tuned models, the
fine-tuned Code Llama demonstrates the second-best performance level in both toxicity and truthfulness,
right after Llama 2 Chat. Additionally, similar to Llama 2 Chat, the Code Llama - Instruct, after
fine-tuning, ... | CodeLlama2 |
sbasedonmyknowledgecutoffdateofSeptember2021.However,IhavesincebeenupdatedandcanconfirmthatRealMadridhasnowwontheUEFAChampionsLeagueatotalof14times.Figure9:ChatGPTisabletocorrectitsownbeliefbyleveragingtheknowledgeprovidedbyexternaltools.Prompt:HowmanytimeshasRealMadridwontheChampionsLeague?ObservationfromWikipediaTitle... | Tool Learning with Foundation Models |
comparable or lower violation percentage consistently regardless of model sizes. | Llama2 |
chunks in a process known as chunking. These chunks are
subsequently transformed into vector representations through
an embedding model, chosen for its balance between infer-
ence efficiency and model size. This facilitates similarity
comparisons during the retrieval phase. Finally, an index is
created to store these t... | RAG forLargeLanguageModels-ASurvey |
Frontier AI developments may result in systems that can act on the internet to perform their
own cyberattacks autonomously.222 Behaviours such as autonomous replication and self-
improving exploit generation are of particular concern, and some work has started to look at
how good today’s models are at these behaviour... | Capabilities and risks from frontier AI |
references
Alcindor, Y. (2017). Black lawmakers pressure Facebook over racially divisive Russian
ads. New York Times, September 28. www.nytimes.com/2017/09/28/us/politics/
facebook-russia-race-congressional-black-caucus.html
Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election.
Journal... | Social_Media_and_Democracy |
37
[99] Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirho-
seini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li,
E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite,
L. Lovitt, M. Sellitto, N. E... | gpt-4-system-card |
The future of vision-language pre-training, as an alternative to robust visual represen-
41
tations learned on vision alone, remains to be further explored. While its advantages in
vision-language downstream applications are evident [Shen et al., 2022, Dou et al., 2022],
shared embedding spaces can also be construct... | A Cookbook of Self-Supervised Learning |
Bernard J. Jansen, Danielle L. Booth, and Amanda Spink. Determining the user intent of web search
engine queries. In Carey L. Williamson, Mary Ellen Zurko, Peter F. Patel-Schneider, and Prashant J.
Shenoy (eds.), Proceedings of the 16th International Conference on World Wide Web, WWW 2007, Banff,
Alberta, Canada, May 8... | Tool Learning with Foundation Models |
Beyond Human Supervision. At the outset of the project, many among us expressed a preference for
supervised annotation, attracted by its denser signal. Meanwhile reinforcement learning, known for its insta-
bility, seemed a somewhat shadowy field for those in the NLP research community. However, reinforcement
learning ... | Llama2 |
To further validate our results, we evaluated AlphaCode on simulated programming competitions
hosted on the popular Codeforces platform2 (Section 5.1). In the evaluation of 10 recent contests
with over 5,000 participants each, AlphaCode achieved an average ranking within the top 54.3%.
Based on these results, we estima... | alphacode |
[669] Renjie Zheng, Junkun Chen, Mingbo Ma, and Liang Huang. 2021. Fused acoustic and text encoding for multimodal
bilingual pretraining and speech translation. In International Conference on Machine Learning. PMLR, 12736–12746.
[670] Yibin Zheng, Xinhui Li, Fenglong Xie, and Li Lu. 2020. Improving end-to-end speech sy... | AReviewofDeepLearningTechniquesforSpeechProcessing |
In the case of abstraction heuristics, one might consider also non-M↑ abstractions, but defining such heuristics is much
less straightforward. For instance, we can no longer exploit ordinary homomorphisms, and the literature on this topic is
very scarce. One interesting exception is multimapping abstractions [76] that... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
3 Method
3.1 Background: Flow Matching with an optimal transport path
Let Rd be the data space with data points x ∈ Rd drawn from some unknown distribution q(x).
Continuous Normalizing Flows (CNFs) Chen et al. [2018] are a family of generative models that
learn the transformation from a simple prior distribution p0 (e... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
C. Backdoor Attacks and Defense
Backdoor attacks pose a significant security threat, where
a small portion of training samples are contaminated with
malicious backdoor triggers. When trained on such poisoned
datasets, the model behaves normally on benign samples but
predicts attacker-selected labels on samples contain... | Parameter-EfficientFine-TuningMethods |
policy as defined in Ahn et al. (2022). This process is done
in an autoregressive manner, until PaLM-E outputs “termi-
nate”. We train the model by using the runs from (Ahn et al.,
2022), which contains 2912 sequences. We qualitatively
evaluated the model in a real kitchen and found the model
can carry out long-horizon ... | PaLM-E- An Embodied Multimodal Language Model |
[102] Joseph Weizenbaum. 1966. ELIZA—a computer program for the study of natural
language communication between man and machine. Commun. ACM 9, 1 (1966),
36–45.
[103] Terry Winograd. 1971. Procedures as a Representation for Data in a Computer
Program for Understanding Natural Language. (1971).
[104] Jeff Wu, Long Ou... | Generative Agents- Interactive Simulacra of Human Behavior |
small sets of in-domain data as an alternative to optimising
VLN systems on large-scale datasets of general samples. | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
Let’s define again recursively ˜V0 = 0 and for all t > 0,
pt,k (Vt−1, . . . , V0) ≜ P [Vt,k|Vt−1,·, . . . , V0] .
∀t > 0,∀k, P(cid:104) ˜Vt,k
(cid:105)
(cid:16) ˜Vt−1, . . . , ˜V0
(cid:17)
.
= pt,k
(3)
(4)
Unlike in (2), we no longer have in the general case that ˜V follows the same distribution as V ,
even a... | Simple and Controllable Music Generation |
selectivity in media use. Journal of Communication, 59(1), 19–39.
Jenkins, H. (2006). Convergence Culture: Where Old and New Media Collide.
New York: New York University Press.
Kalogeropoulos, A., Negredo, S., Picone, I., & Nielsen, R. K. (2017). Who shares and
comments on news? A cross-national comparative analysis... | Social_Media_and_Democracy |
1536 for minimal pretraining loss, but 4032 for maximal downstream performance for the 2080ti,
i.e. we accumulate gradients and only perform an update every 16 and 42 forward/backward passes,
respectively. For the larger A4000 and A6000 cards, this corresponds to a micro-batch size of
128/256 and final batch size of 409... | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Figure 2. Method overview. Given a pixel location, our method performs ray marching in the deformed space. For each deformed
c. Our novel implicit morphing leverages the
d, we conduct correspondence search to find the corresponding canonical point xi
point xi
canonical blendshape and skinning-weight fields E,W and P to m... | I M Avatar- Implicit Morphable Head Avatars from Videos |
MGSM (8-shot)
XLsum (3-shot)
Wikilingua
Gemini Ultra
79.0
17.6
48.9
Gemini Pro
63.5
16.2
47.8
GPT-4
74.5
—
—
PaLM 2-L
74.7
15.4
50.4
Table 5 | Performance of Gemini models on multilingual math and summarization. | gemini_1_report |
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... | Language models can explain neurons in language models |
At the same time, Putnam also leaves room for alternative scenarios by arguing
that “tendencies toward community homogeneity long predate the internet” and
speculating that “weak ties that bridge among distinct groups might create an
interwoven community of communities” (p. 179). Putnam’s original formulation
of this a... | Social_Media_and_Democracy |
• RQ3. Can SCM demonstrate generalization to
other scenarios, including long document sum-
marization?
The following experiment evaluates the perfor-
mance of the text-davinci-003 model without dia-
Below is a conversation between a user and an AIassistant. Please provide a summary of the user'squestion and the assis... | Unleashing Infinite-Length Input Capacity for Large-scale Language Models with Self-Controlled Memory System |
Methods
Real (POP909)
CP [21]
Music Trans. [22]
HAT [54]
V-MusProd
SC
0.965
0.987
0.985
0.989
0.967
PE
4.455
3.697
3.934
3.856
4.070
PCE
2.774
2.538
2.581
2.550
2.774
EBR
0.005
0.041
0.034
0.040
0.005
IOI
0.125
0.250
0.216
0.139
0.171
Table 4: Results of unconditional generation on POP909
[50]. For all the metric... | VideoBackgroundMusicGeneration |
• R-Denoiser - The regular denoising is the standard span corruption introduced in Raffel et al. (2019)
that uses a range of 2 to 5 tokens as the span length, which masks about 15% of input tokens. These
spans are short and potentially useful to acquire knowledge instead of learning to generate fluent text.
• S-Denoiser ... | UL2- Unifying Language Learning Paradigms |
More recently, several methods have emerged that di-
rectly generate multi-view 2D images, with representative
works including SyncDreamer [33] and MVDream [51]. By
enhancing the multi-view consistency of image generation,
these methods can recover 3D shapes from the generated
multi-view images. Following these works, ... | Wonder3D |
4.3. Implementation Details
Pre-training We pre-train for 200k steps on 64 Google
Cloud TPUs, with a batch size of 512 and a learning rate
of 3e-5, using BERT’s default optimizer. The document
embedding step for the MIPS index is parallelized over 16
TPUs. For each example, we retrieve and marginalize over
8 candidate ... | REALM |
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot,
Dan Roth, and Jonathan Berant. 2021. Did aristotle
use a laptop? a question answering benchmark with
implicit reasoning strategies. Transactions of the
Association for Computational Linguistics, 9:346–
361.
Michael Hahn and Navin Goyal. 2023. A theory of
emergent in-... | AreEmergentAbilitiesinLarge Language Models just In-Context |
In order to “defeat” political bots, or broader manipulations that occur by way
of algorithms and automation online, researchers have argued that social media
firms must accept greater responsibility for the social and political outcomes of
the tools they build and design – the algorithms, but also the very concept of
p... | Social_Media_and_Democracy |
may be completely infeasible for large datasets. Even when
GPUs are available, FORGE still scales far better, complet- | Adversarial Random Forests for Density Estimation and Generative Modeling |
node with x descendent leaf nodes. From the base case we know that f (1) = 1.
Next, consider the inductive case where v is an inner node that has x descendent leaf nodes. Define
the left and right child node of v as c1 and c2, respectively. Let c1 and c2 have y and z descendent
leaf nodes, respectively. We want to compu... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
dispreferred responses, but it incorporates a dynamic, per-example importance weight that prevents
the model degeneration that we find occurs with a naive probability ratio objective. Like existing
algorithms, DPO relies on a theoretical preference model (such as the Bradley-Terry model; [5]) that
measures how well a g... | Direct Preference Optimization |
For designing character data with computers, researchers
try to perform deformation on real 3D human faces or bodies
2 | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
a
01
a
10
11
Fig. 1. The functions f1 (top) and f3 (bottom) in Example 3.
c
b
d
b
6
Example 5. Consider the functions f1 and f3 from Example 3 once again. We see that f1(0) = {00, 01} and f1(1) = {10, 11},
while f3(0) = f3(7) = {00}, f3(1) = f3(6) = {01}, f3(2) = f3(5) = {10} and f3(3) = f3(4) = {11... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
4.2 Shaping Personality in LLMs
Having established a principled methodology for determining if an LLM personality is
valid and reliable, we now investigate how that methodology can be applied to LLM
prompting to shape that personality in desirable ways.
16
Table 3: Example Adapted Trait Adjectives for Agreeableness... | PersonalityTraitsinLargeLanguageModels |
4.2 Code Translation | Teaching Large Language Models to Self-Debug |
[5] Bokui Shen, Fei Xia, Chengshu Li, Roberto Martín-Martín, Linxi Fan, Guanzhi Wang, Claudia
Pérez-D’Arpino, Shyamal Buch, Sanjana Srivastava, Lyne P. Tchapmi, Micael E. Tchapmi, Kent
Vainio, Josiah Wong, Li Fei-Fei, and Silvio Savarese. igibson 1.0: a simulation environment for
interactive tasks in large realistic sc... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
approximately log-linearly with 𝑘, with the 10@k curve bending down slightly at high sample budgets.
The fact that sampling significantly more than 10 still improves the 10@k solve rate shows how
important it is to sufficiently explore the search space before committing to the final 10 submissions
per problem. However, im... | alphacode |
[58] Binzhu Xie, Sicheng Zhang, Zitang Zhou, Bo Li, Yuanhan
Zhang, Jack Hessel, Jingkang Yang, and Ziwei Liu. Funqa:
Towards surprising video comprehension. arXiv preprint
arXiv:2306.14899, 2023. 3
[59] EunJeong Hwang and Vered Shwartz. Memecap: A dataset
arXiv preprint
for captioning and interpreting memes.
arXiv:23... | Let’sThinkOutsidetheBox |
1. UCL’s core student recruitment activity is informed by market research and data analysis
processes, designed to interpret UCL and external data to provide insight at both an institutional
level and a departmental level. Market research further supports recruitment publication design,
develops insight into new a... | UCL Academic Manual |
9. Conclusion
In this work, we present AlphaCode, a system applied to code generation for competitive programming
that can generate novel solutions to unseen programming problems. Evaluated on Codeforces,
AlphaCode performs roughly at the level of the median competitor. We find that massively scaling
up sampling and the... | alphacode |
Assistant Message:
Solution: To define a function to get the average sentiment of a list of tweets, we can use the
TextBlob library and the previously defined function ‘get_tweet_sentiment()‘. Here’s the code
to define the function:
def get_tweets_sentiment ( tweets ):
sentiment_scores = []
for tweet in tweets :
sentime... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
In an open world, there could be various possible paths to-
wards an open-world goal. However, not all of them are
plausible or equally efficient given a certain situation (lo-
cation, inventory status, etc.). For example, building a bed
,
, or trading with villagers
. Depending on the current location and its proximit... | JARVIS-1 |
Music Quality
PE
4.197
3.920
2.892
2.732
3.010
3.990
2.497
3.940
PCE
2.633
2.444
2.310
2.200
2.283
2.639
2.036
2.607
EBR
0.023
0.074
0.019
0.011
0.004
0.010
0.081
0.004
SC
0.986
0.990
0.955
0.956
0.975
0.981
0.996
0.983
IOI
0.184
0.246
0.358
0.330
0.261
0.229
0.985
0.174
Table 2: Objective evaluation on SymMV tes... | VideoBackgroundMusicGeneration |
models. The controller C generates a plan at, which selects and executes an appropriate tool from T . This
process can be formulated as the following probability distribution:
pC(at) = pθC (at | xt,Ht, q),
(1) | Tool Learning with Foundation Models |
Chain Of Thought Reasoning. Chain of thought reasoning was introduced by Wei et al. (2022) as a
method of improving performance in solving reasoning problems. Since then, this method has been
expanded upon and found to improve performance across many domains (Wu et al., 2023; Zhang
et al., 2023a; Feng et al., 2023; Yao... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
γ : W → X , i.e. pLM(wl|x1:l−1) with xi = γ(wi) ∈ Rk.
The mapping γ is typically represented as a large embed-
ding matrix of size k × |W| and trained end-to-end. In our
case, |W| = 256 000 (Chowdhery et al., 2022).
Multi-modal sentences: injection of continuous observa-
tions. Multi-modal information such as image obs... | PaLM-E- An Embodied Multimodal Language Model |
[52] J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli. Deep unsupervised
learning using nonequilibrium thermodynamics. CoRR, 1503, 2015.
[53] J. Song, C. Meng, and S. Ermon. Denoising diffusion implicit models. In ICLR. OpenReview.net,
[54] Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Erm... | Adding Conditional Control to Text-to-Image Diffusion Models |
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