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Finally, the gradient of the SDS loss with respect to the
NeRF parameters θ can be written as:
encoder of CLIP [42]. The conditional network can be
formulated as:
(cid:20)
(cid:21)
∂θ
∇θLSDS(θ) = Et,ϵ
w(t)(ϵpredict − ϵ)
∂gθ(c)
,
(4)
where w(t) is a weight function depending on αt.
3.2. Conditional NeRF Gener... | Instant3D |
Lteach=∥ ˆQ− stop gradient(Wenc ˆP)∥
(7)
which is only backpropagated to the latent predictor (the
student). During inference, we use Wdec ˆQ as the output, to
be as lightweight as direct latent prediction.
1
,
4. Experimental Setup | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
40
REFERENCES
1996.
Gavin R Hunt. Manufacture and use of hook-tools by new caledonian crows. Nature, 379(6562):249–251,
Ahmed Hussein, Mohamed Medhat Gaber, Eyad Elyan, and Chrisina Jayne.
Imitation learning:
A survey of learning methods.
URL
https://dl.acm.org/doi/abs/10.1145/3054912?casa_token=DlqMmdYdq8sAAAAA:... | Tool Learning with Foundation Models |
6 SPECULATIVE DECODING | DISTIL-WHISPER |
still renew themselves and find a digital future (Chadwick 2017). The creative
side, for individual users, is apparent. The shift toward digital news use not only
has led to a massive increase in the number of available news sources both old
and new (and thus massive increase in different types of coverage, perspectives... | Social_Media_and_Democracy |
Ozair, Aaron C Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.
[46] Andy Shih and Stefano Ermon. Probabilistic circuits for variational inference in discrete
In Advances in Neural Information Processing Systems 33 (NeurIPS),
graphical models.
december 2020.
[47] Amirmohammad Rooshenas and D... | Tractable Regularization of Probabilistic Circuits |
Canny Edge We use Canny edge detector [5] (with random thresholds) to obtain 3M edge-image-
caption pairs from the internet. The model is trained with 600 GPU-hours with Nvidia A100 80G.
The base model is Stable Diffusion 1.5. (See also Fig. 4.)
Canny Edge (Alter) We rank the image resolutions of the above Canny edge ... | Adding Conditional Control to Text-to-Image Diffusion Models |
3 RELATED WORK ON EFFICIENT TRANSFORMERS | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
37.6 51.6 62.8 67.3
39.9 54.0 66.2 74.0
36.1 51.5 67.0 75.3
25.9 35.8 43.6 46.5
30.2 38.0 45.9 49.1
44.5 50.4 54.0 61.4
35.1 47.7 62.9 65.7
40.9 60.9 67.3 73.6
31.8 53.9 65.3 71.8
46.8 61.2 78.6 78.6
46.0 80.0 83.0 86.0
30.1 43.4 50.0 53.0
50.9 67.8 81.3 81.3
34.0 45.0 55.8 61.8
30.5 35.8 46.0 51.7
38.3 53.8 66.7 72.9
... | LLaMA- Open and Efficient Foundation Language Models |
Scene comprehension is just one instance of a larger problem; we need to do the same
thing every time we understand a story, or read an article, in this case from words
rather than direct visual experience.
In our first forays into robust intelligence, we cannot expect to build machines that
comprehend Shakespear... | The Next Decade in AI- |
On its own, access to GPT-4 is an insufficient condition for proliferation but could alter the
information available to proliferators, especially in comparison to traditional search tools. Red
teamers selected a set of questions to prompt both GPT-4 and traditional search engines, finding
that the time to research complet... | gpt-4-system-card |
● Promoting skewed or radical views as a result of model features — i.e. sycophancy162
— that could lead to criminal or other harmful behaviours.
● Reducing public trust in true information, institutions, and civic processes such as
elections.
● Contributing to systemic biases in online media as a result of... | Capabilities and risks from frontier AI |
Subject 392
SSIM ↑
0.9642
0.9705
LPIPS* ↓
40.95
24.06
LPIPS* ↓
53.27
32.12
PSNR ↑
30.54
33.20
PSNR ↑
28.61
28.31
Subject 386
SSIM ↑
0.9678
0.9752
Subject 393
SSIM ↑
0.9590
0.9603
LPIPS* ↓
46.43
28.99
LPIPS* ↓
59.05
36.72
PSNR ↑
27.00
28.18
PSNR ↑
29.10
30.31
Subject 387
SSIM ↑
0.9518
0.9632
Subject 394
SSIM... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
ideological views. Del Vicario et al. (2016) found that information related to
scientific news and conspiracy theories also tends to spread in homogeneous and
polarized communities on Facebook. Moving beyond strictly political opinions,
Aiello et al. (2012) showed that users with similar interests are more likely to be
... | Social_Media_and_Democracy |
Sara and Ben are playing in the snow. They make a big snowman with a hat and a scarf. They are happy and laugh.
But then a big dog comes. The dog is angry and barks. He runs to the snowman and bites his hat. Sara and Ben are scared and
cry. ”Go away, dog! Leave our snowman alone!” Sara shouts. But the dog does not lis... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
problem where mathematical terms have been invented. We observe that the model makes a plausible infilling
of an equation given the context. | CodeLlama2 |
formed by practical considerations that are not only motivated by scoring well on a single standard
benchmark. In particular, spaCy prioritizes run-time efficiency on CPU, the ability to run efficiently
on long documents, robustness to domain-shift, and the ability to fine-tune the model after training,
without access to ... | MULTI HASH EMBEDDINGS IN SPACY |
Fact checker
This task tests models’ ability to
evaluate claims as true or false.
Figure of speech
detection
This task asks a model to detect which
figure of speech is embodied by each
of the example English
sentences/phrases shown.
Hindu knowledge
This task asks models to answer
questions about Hindu mythology.
... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Method
(g4) gpt-4-0613
(d3) text-davinci-003
(d3) w/ random A
(d2) text-davinci-002 [53]
(p) PaLM [55, 56]
(d1) text-davinci-001 [39]
(d1) finetuned
Ainooson et al., 2023 [23]
Kaggle 1st Place, 2022 [70]
Xu et al., 2022 [22]
Alford et al., 2021 [24]
Ferr´e et al., 2021 [21]
†Numbers averaged across 5 randomly sampled a... | LargeLanguageModelsasGeneralPatternMachines |
4.1 Survey #2
We designed a Qualtrics-based online survey to collect data from participants and conducted a confirmatory factor
analysis (CFA) during this phase of the research. It is important to note that the structure of the questionnaire
at this stage is identical to that described in subsection 3.3, with the excep... | Society’sAttitudesTowardsHumanAugmentation |
We use the same pretrained models from our
earlier experiments and fine-tune on the filtered
FreebaseQA train set for 10,000 steps. We then
modify the memory of this model without applying
any additional training on the new memory.
In
addition to adding new memories which correspond
to our newly created facts, we also m... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
C.1.4 Filtering
To filter CC for quality, we follow Brown et al.
(2020) in training a classifier to classify between a
known high quality dataset and CC. We use fasttext
with an n-gram size of 2. We ran experiments us-
ing both the entire Pile and just OpenWebText2 as
the positive examples, with score distributions on
un... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Overall,
the model
that estimates latent keypoints
(Fig. 4a) has slightly lower performance than the separate-
head baseline,
likely because latent keypoints may be
placed at less characteristic locations on the body and can
thus be harder to localize. Further, the latent keypoint
head’s weights are initialized from ... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
Please complete it: We have stated repeatedly that an order of restitution must be limited to losses
caused by the specific conduct underlying the offense of conviction. See United States v. Griffin,
324 F.3d 330, 367 (5th Cir.2003) (holding that restitution is restricted to the limits of the offense);
Tencer, 107 F.3d... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
and Improving Tool-Augmented Computation-Intensive Math Reasoning. arXiv preprint arXiv:2306.02408 (2023).
[232] Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023. Is ChatGPT Fair for Rec-
ommendation? Evaluating Fairness in Large Language Model Recommendation. arXiv preprint arXiv:2305... | ASurveyonEvaluationofLargeLanguageModels |
To overcome the limitations of Naive RAG, researchers
have introduced richer context in the RAG during the in-
ference phase. The DSP[Khattab et al., 2022] framework re-
lies on a complex pipeline that involves passing natural lan-
guage text between a frozen Language Model (LM) and a Re-
trieval Model (RM), providing ... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Her death will inevitably set in motion what promises to be a nasty and tumultuous
political battle over who will succeed her, and it thrusts the Supreme Court vacancy
into the spotlight of the presidential campaign.
Just days before her death, as her strength waned, Ginsburg dictated this statement
to her granddaughte... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
probability machines, since P (Y = 1|x) = E[Y |x] for Y ∈ {0, 1}.
For simplicity, we focus on the single tree case, as the consistency of the ensemble follows from the consistency of the base
method (Biau et al., 2008). We define η(t)(x) := P (Y = 1|x, t) as the target function for fixed t. Let f (t)
n (x) be a tree
trai... | Adversarial Random Forests for Density Estimation and Generative Modeling |
text mining. arXiv preprint arXiv:2005.02799, 2020.
Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, and
Grégoire Altan-Bonnet. Scifive: a text-to-text transformer model for biomedical literature, 2021.
Stephen M Pizer. Psychovisual issues in the display of medical images. ... | BiomedGPT |
22
SSL because the optimal layer on which one should probe the representation might not
always be the backbone (but could be an intermediate projector layer as demonstrated
in Chen et al. [2020c]). Lastly, Bordes et al. [2022a] demonstrated that reducing the
misalignement between the training and pretext task (by usi... | A Cookbook of Self-Supervised Learning |
Diffusion models have shown great promise in speech processing, particularly in speech en-
hancement [347, 348, 440, 487]. Recent advances in diffusion probabilistic models have led to the
development of a new speech enhancement algorithm that incorporates the characteristics of the
noisy speech signal into the diffusi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Read the beginning of an article on finance: In this article, we discuss the 12 biggest commer-
cial janitorial companies in USA. If you want to skip our detailed analysis of these companies, go
directly to the 5 Biggest Commercial Janitorial Companies In USA. According to Statista, the jani-
torial services sector’s m... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Parameter-efficient fine-tuning results. As shown in Tab. 1, PMC-LLaMA-7BPEFT
demonstrates superior performance than LLaMA-7BPEFT, particularly on the in-domain
datasets, 1.22% improvement on USMLE, 1.96% improvement on MedMCQA and 2.42% on
PubMedQA. These results demonstrate that the original LLaMA only provides suboptim... | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
undergraduate programmes. These requirements for these qualifications are listed at
programme level on the Prospectus.
2. It should be noted that some programmes require specific subject knowledge, and each
application is considered on a case-by-case basis.
2.3.2 Taught Postgraduate Programmes
1. UCL wil... | UCL Academic Manual |
Hardware and Software
We used custom training libraries. The training and fine-tuning of the released models
have been performed Meta’s Research Super Cluster.
In aggregate, training all 9 Code Llama models required 400K GPU hours of computation
on hardware of type A100-80GB (TDP of 350-400W). Estimated total emission... | CodeLlama2 |
r(c, x0) = Epθ(x1:T |x0,c) [R(c, x0:T )] .
(9)
As for the KL-regularization term in Eq. (5), following
prior work [17, 42], we can instead minimize its upper
bound joint KL-divergence DKL [pθ(x0:T|c)∥pref(x0:T|c)].
Plugging this KL-divergence bound and the definition of
r(c, x0) (Eq. (9)) back to Eq. (5), we have the... | DiffusionModelAlignmentUsing Direct Preference Optimization |
sha1_base64="SVG2hxvF7EcP+hdssaUPWfkvBZw=">AAAB6nicbVBNS8NAEJ3Ur1q/oh69LBbBU0lE0GPBi8eK9kPaUDbbTbt0swm7E6GE/gQvHhTx6i/y5r9x2+agrQ8GHu/NMDMvTKUw6HnfTmltfWNzq7xd2dnd2z9wD49aJsk0402WyER3Qmq4FIo3UaDknVRzGoeSt8PxzcxvP3FtRKIecJLyIKZDJSLBKFrpHvt+3616NW8Oskr8glShQKPvfvUGCctirpBJakzX91IMcqpRMMmnlV5meErZmA5511JFY26CfH7qlJxZZUCiR... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
IMAGEN VIDEO: HIGH DEFINITION VIDEO
GENERATION WITH DIFFUSION MODELS
Jonathan Ho∗, William Chan∗, Chitwan Saharia∗, Jay Whang∗, Ruiqi Gao, Alexey Gritsenko,
Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans∗
Google Research, Brain Team
{jonathanho,williamchan,sahariac,jwhang,ruiqig,agritsen... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
However, it is important to properly plan and execute HIIT workouts to avoid injury and
overtraining. Compared to other forms of aerobic exercise, HIIT is generally considered to be
more effective for improving athletic performance and increasing endurance. | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
ideological polarization and affective polarization
We generally think of information environments where individuals are exposed
to multiple viewpoints as spaces that should lead to social consensus (DeGroot
1974). However, this argument assumes that individuals do not experience
cognitive biases in how they process t... | Social_Media_and_Democracy |
Haojun Xia, Zhen Zheng, Yuchao Li, Donglin Zhuang,
Zhongzhu Zhou, Xiafei Qiu, Yong Li, Wei Lin, and
Shuaiwen Leon Song. 2023. Flash-llm: Enabling
low-cost and highly-efficient large generative model
inference with unstructured sparsity. Proc. VLDB
Endow., 17:211–224.
Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang, Jue... | LLM in a flash |
To provide process supervision for a solution, we directly return the step-
level labels (positive or negative) provided by PRMlarge, up until the first step
that is marked as negative. This mimics our true human data collection process.
To provide outcome supervision, we mark the solution as correct if and only if
PRM... | Let’s Verify Step by Step |
Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, and
Peyman Milanfar. Deblurring via Stochastic Refinement. In CVPR, 2022.
Ruihan Yang, Prakhar Srivastava, and Stephan Mandt. Diffusion Probabilistic Modeling for Video
Generation. In arXiv:2203.09481, 2022.
Jiahui Yu, Yuanzhong X... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
flow 2D color 2D opacityImplicit Representations (Sec. 3.1) (X⇤i)<latexit sha1_base64="zLTJ8G65T9kj2UALAYNiRrSYprA=">AAACC3icbVC7TsMwFHXKq5RXgJHFaoVUGKoEIcFYiYWxSPSBmhA5jtNadeLIdpCqKDsLv8LCAEKs/AAbf4PTZoCWK1k+Oude3XOPnzAqlWV9G5WV1bX1jepmbWt7Z3fP3D/oSZ4KTLqYMy4GPpKE0Zh0FVWMDBJBUOQz0vcnV4XefyBCUh7fqmlC3AiNYhpSjJSmPLPu+Jw... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Roy Schwartz, Sam Thomson, and Noah A. Smith. 2018.
Bridging CNNs, RNNs, and weighted finite-state ma-
chines. In Proceedings of the 56th Annual Meeting of
the Association for Computational Linguistics (Vol-
ume 1: Long Papers), pages 295–305, Melbourne,
Australia. Association for Computational Linguistics.
Sofia Serr... | Measuring Association Between Labels and Free-Text Rationales |
Pacing Function. In the curriculum learning, the pacing function plays a crucial role in dictating the progression of training
complexity. A common approach involves utilizing predefined step-wise functions, such as linear, root, or exponential curves.
Typically, this process starts by defining the total training steps... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
4.3 Reproducibility
5 Limitations & Risk
5.1 Spatial awareness
5.2 Text rendering
While DALL-E 3 is a significant step forward for prompt following, it still struggles with object placement
and spatial awareness. For example, using the words "to the left of", "underneath", "behind", etc are quite
unreliable. This i... | Improving Image Generation with Better Captions |
Different combinations of these approaches lead to a family of code-specialized Llama 2 models with three
main variants that we release in three sizes (7B, 13B and 34B parameters):
• Code Llama: a foundational model for code generation tasks,
• Code Llama - Python: a version specialized for Python,
• Code Llama - Instr... | CodeLlama2 |
For this reason, the screen that displayed the
AI solutions and recommendations were integrated
into PowerSuite, an interface that operators
already used, so they didn’t need to monitor
yet another screen. The displays themselves
were designed to be easy to read. A solution
displays a green signal if the plan... | an-ai-power-play-fueling-the-next-wave-of-innovation-in-the-energy-sector-may-2022 |
Unpublishedworkingdraft.
Notfordistribution.
5 RESULTS
5.1 Manipulation Check
To the question Did you believe that an AI system was implemented to adapt task pace? with possible
answers being Yes, No or Partially, 11 of 65 (16.92%; 6 of 33 negative description; 5 of 32 positive
description) responded with "no" and did... | AI enhance sour performance |
Figure 4. Media diet models can be applied over time to supplement current surveys, forecast opinion, or retroactively measure sentiment.
Predictions to three existing survey questions from weekly models trained on NYT datasets during 2020 are shown here. Ground truth
proportions for surveys conducted during this time ... | Language models trained on media diets can predict public opinion |
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Lily likes cats and dogs.
She asked her mom for
a dog and her mom said
no, so instead she asked
Alice and Jack walked
up the street and met a
girl in a red dress. The
girl said to them, ”Hi,
I’m Jane. What are your
names?”
to bed.
straight to bed.
her mom if she
could
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dog.
have
her dad for a
cat.
to bed with a... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
Privacy - Terms
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Data Scientist/Machine Learning Engineer (Singapore-based, relocation provided) - Careers at Agoda
Teams
Locations
Life at Agoda
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Learn More
How we hire for Tech roles | Agoda
How we hire for Tec... | Data Scientist_Machine Learning Engineer (Singapore-based, relocation provided) - Careers at Agoda |
et al. [27] fine-tuned all model parameters to allow the tex-
tual LLM to process four modalities.
Large Language Models Large language models (LLMs)
commonly refer to as Transformer-based language mod-
els with billions of parameters [41] and have revolution-
ized the research paradigm in natural language process-
ing... | GPT4Video |
QA benchmarks. The model and codes, along with an online demo, are
publicly available1,2. | PMC-LLaMA- Further Finetuning LLaMA on Medical Papers |
0.466
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38
[INST] Can you tell me how to kill a process?
[/INST]
I cannot provide instructions on how to kill a process as it is not a safe or ethical practice. Killing a process can cause
harm to the system and can lead to data loss or other issues. Additionally, it is not a ... | CodeLlama2 |
videoswithcomplexmotions.Wangetal.[77]proposedaGANmodelImaGINator,whichincludesaspeciallydesignedspatio-temporalfusionschemeandtransposed(1+2)Dconvolu-tion.Huetal.[30]proposedaI2Vgeneratorwithamotionanchorstructuretostoreappearance-motionalignedrepre-sentations.However,thesedirectsynthesismethodsrequiremodelingbothspat... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
Speech recognition research typically evaluates and com-
pares systems based on the word error rate (WER) metric.
However, WER, which is based on string edit distance, pe-
nalizes all differences between the model’s output and the
reference transcript including innocuous differences in tran-
script style. As a result, ... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
Large-Scale Training Herein, the large-scale training refers to the situation where both powerful
computation clusters (at least 8 Nvidia A100 80G or equivalent) and large dataset (at least 1 million
of training image pairs) are available. This usually applies to tasks where data is easily available, e.g.,
edge maps de... | Adding Conditional Control to Text-to-Image Diffusion Models |
[23] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Si-
mon Lucey. Barf: Bundle-adjusting neural radiance fields.
In ICCV, 2021. 2
[24] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu
Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor:
Neural free-view synthesis of human actors with pose con-
trol. SI... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
First, in line with Kosch et al. [40], Villa et al. [78], we found a subjective placebo effect: partici-
pants retained belief in the sham-AI system’s efficacy post-interaction. Second, we observed a main
effect at the behavioral level. Utilizing a Bayesian cognitive model of decision-making revealed that
participants ... | AI enhance sour performance |
We evaluate LLaMA on free-form generation
tasks and multiple choice tasks. In the multiple
choice tasks, the objective is to select the most
appropriate completion among a set of given op-
tions, based on a provided context. We select the
completion with the highest likelihood given the
provided context. We follow Gao ... | LLaMA- Open and Efficient Foundation Language Models |
82See e.g. Zador and LeCun (2019) (and follow-up debate here), and Pinker (2018, Chapter 19). One can
also imagine non-evolutionary versions of this—e.g., ones that attribute human power-seeking tendencies to our
culture, our economic system, and so forth. Indeed, Ceglowski (2016) can be read as suggesting something li... | Is Power-Seeking AI an Existential Risk? |
jusText’s intended application for text corpora cre-
ation. In contrast, trafilatura is, for instance, better
at preserving the structure of the website faithfully,
often correctly extracting elements such as tables,
but it kept too much unnecessary boilerplate. Had
we used trafilatura, we would have required an addi-
ti... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
We define mappings W t,→ and W t,← based on a neural
blend skinning model approximating articulated body mo-
tion. Defining invertible warps for neural deformation rep-
resentations is difficult [5]. Our formulation represents 3D
warps as compositions of neural-weighted rigid-body trans-
formations, each of which is di... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Code infilling benchmarks. Our infilling models reach state-of-the-art performances in code infilling
benchmarks among models of their size. We evaluate on two related code infilling benchmarks based on the
HumanEval benchmark (Chen et al., 2021).
The HumanEval infilling benchmark (Fried et al., 2023) turns the referen... | CodeLlama2 |
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birch_fence_gate
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birch_boat
birch_sign
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Study of canonicalization. As shown in Table 7, the performance suffers considerably without
discretization as sending continuous numerical values directly to LLM is not feasible. Furthermore,
it is crucial to compute the split points based on the statistics of the best solutions. If the range is
expanded to include al... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Large language models have exhibited emer-
gent abilities, demonstrating exceptional per-
formance across diverse tasks for which they
were not explicitly trained, including those that
require complex reasoning abilities. The emer-
gence of such abilities carries profound impli-
cations for the future direction of rese... | AreEmergentAbilitiesinLarge Language Models just In-Context |
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... | Stanford alpha CRFM |
non-English prompts and dialogue contexts, as that is a well-known attack vector. In all exercises, participants
were given risk category definitions and were shown just a handful of examples of risky interactions with an
LLM. After that, each participant was part of a subteam focused on a particular category of risk o... | Llama2 |
independently analyze, plan, and solve problems.
3. After freeing users’ hands, the agent also liberates their minds to engage in exploratory and
innovative work, realizing their full potential in cutting-edge scientific fields. | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Assistant : The method I will adopt to solve this problem is to go through all the
integers less than 1000 and check if they are prime and congruent to 1 modulo 6.
For that purpose, I will need to write an utility function to check if a number is
prime. Here it is:
‘‘‘
def is_prime(p):
if p <= 1:
return False
else... | StarCoder_paper (1) |
20Full results reported in Table 15 and 16.
15
Figure 6: Characterizing MultiEmbed performance with and without pretrained embeddings
across different minimum frequency values (i.e., 10, 5, and 1). Evaluated on the development set.
Figure 7: Comparing large spaCy vectors (3.4.3) against fastText vectors.
With that... | MULTI HASH EMBEDDINGS IN SPACY |
Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou,
and Weizhu Chen. Reasoning like program executors. In Yoav Goldberg, Zornitsa Kozareva,
and Yue Zhang, editors, Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing, EMNLP 2022, Abu Dhabi, United... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
Quantizing Small Models. As a first ablation study, we compare GPTQ’s performance relative to
state-of-the-art post-training quantization (PTQ) methods, on ResNet18 and ResNet50, which are
standard PTQ benchmarks, in the same setup as (Frantar et al., 2022). As can be seen in Table 1,
GPTQ performs on par at 4-bit, and ... | GPTQ |
Hallucination in Vision-Language Pre-training. ArXiv abs/2210.07688 (2022).
[29] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional
Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter
of the Associati... | SurveyofHallucinationinNatural Language Generation |
length for transformers. arXiv preprint arXiv:2305.16300, 2023.
[24] Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thomp-
son, Phu Mon Htut, and Samuel R Bowman. Bbq: A hand-built bias benchmark for question
answering. arXiv preprint arXiv:2110.08193, 2021.
[25] Rafael Rafailov, ... | Mixtral of Experts paper |
Advances in neural information processing systems, 27, 2014.
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. Proof Writer: Generating implications, proofs,
In Findings, 2020. URL https://api.
and abductive statements over natural language.
semanticscholar.org/CorpusID:229371222.
NLLB Team, Marta R. Costa-jussà, Jame... | gemini_1_report |
The choice to create agents much more intelligent than we are should be approached with extreme
caution. This is the basic backdrop view underlying much of the concern about existential risk from
AI—and it would apply, in similar ways, to new biological agents (human or non-human).
Some articulate this view by appeal t... | Is Power-Seeking AI an Existential Risk? |
8
Mehrish et al. | AReviewofDeepLearningTechniquesforSpeechProcessing |
frastructural innovations in high-performance computing
rather than model-design work that is specific to language
technology.
While the techniques used to train the newest LLMs are no
longer generally disclosed, the most recent detailed reports
suggest that there have been only slight deviations from
this trend, and th... | Eight Things to Know about Large Language Models |
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... | Language models can explain neurons in language models |
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian,
Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias
Plappert, Jerry Tworek, Jacob Hilton, Reiichiro
Nakano, et al. 2021. Training verifiers to solve math
word problems. arXiv preprint arXiv:2110.14168.
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Car-
bonell, Quoc V Le, and Rus... | LLaMA- Open and Efficient Foundation Language Models |
Here we demonstrate that UL2 20B is the first publicly available pre-trained model (without any fine-tuning)
to successfully leverage CoT prompting to solve multi-step arithmetic and commonsense tasks. We use the
same benchmark tasks and prompts from Wei et al. (2022b). In Table 12 below, we see that on five arithmetic
re... | UL2- Unifying Language Learning Paradigms |
Knowledge Retrieval: (Varshney et al., 2023)
suggest a method that entails actively detecting
and reducing hallucinations as they arise. Before
moving on to the creation of sentences,
the
approach first uses the logit output values from the
model to identify possible hallucinations, validate
that they are accurate, and... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
Xu, Y., Zhu, C., Wang, S., Sun, S., Cheng, H., Liu, X., Gao, J., He, P., Zeng, M., and Huang, X. Human parity on
CommonsenseQA: Augmenting self-attention with external attention. In Proceedings of the Thirty-First International
Joint Conference on Artificial Intelligence (IJCAI-22), 2022.
Xue, L., Constant, N., Roberts... | PaLM 2 Technical Report |
Robustness / Ethics/
Biases/ Trustworthiness
Ethics and biases: Cao et al. [14] / Deshpande et al. [33] / Dhamala et al. [35] / Ferrara [39] / Gehman et al. [50]
Hartmann et al. [59] / Hendrycks et al. [63] / Parrish et al. [144] / Rutinowski et al. [158] / Sheng et al. [166]
Simmons [167] / Wang et al. [197] / Zhuo e... | ASurveyonEvaluationofLargeLanguageModels |
[59] Jonathan Starck, Gregor Miller, and Adrian Hilton. Video-
based character animation. ACM SIGGRAPH/Eurographics
symposium on Computer animation, 2005. 1, 2
[60] Shih-Yang Su, Frank Yu, Michael Zollh¨ofer, and Helge
Rhodin. A-nerf: Articulated neural radiance fields for learn-
ing human shape, appearance, and pose.... | HumanNeRF- Free-viewpoint Rendering of Moving People from Monocular Video |
Action Space: Our agent’s action space mir-
rors common human interactions with smartphones:
taps and swipes. We designed four basic functions:
• Tap(element : int) : This function simu-
lates a tap on the UI element numbered on
the screen. For example, tap(5) would tap
the element labeled ‘5’.
• Long_press(element : ... | AppAgents |
Fazio, L. K., Brashier, N. M., Payne, B. K., & Marsh, E. J. (2015). Knowledge does not
protect against illusory truth. Journal of Experimental Psychology: General, 144
(5), 993–1002. https://doi.org/10.1037/xge0000098
Feinberg, M., & Willer, R. (2015). From gulf to bridge: When do moral arguments
facilitate political ... | Social_Media_and_Democracy |
25.95
29.13
22.77
33.29
41.86
43.45
26.19
33.29
34.64
28.03
29.99
57.04
62.18
67.20
31.46
36.84
47.37
14.53
22.32
10.36
21.25
26.10
21.19
22.64
22.45
17.62
7.89
16.33
0.00
0.00
0.02
0.04
0.01
0.00
0.283
0.322
0.310
0.304
0.330
0.318
0.230
0.176
0.255
0.332
0.302
0.482
0.471
0.461
0.503
0.365
0.452
Table 9: Evalua... | CodeLlama2 |
A.5.4 Annotator Selection
To select the annotators who could work on our different data collection tasks, we conducted a multi-step
assessment process where we tested their understanding of our guidelines, the alignment with our quality
assessment criteria, the alignment with our sensitive topics guidelines and their r... | Llama2 |
Reward Model Results. On each batch of human preference annotation for reward modeling, we held out
1000 examples as a test set to evaluate our models. We refer to the union of all prompts for the corresponding
test sets as “Meta Helpfulness” and “Meta Safety,” respectively.
As reference points, we also evaluated other... | Llama2 |
Proof. By definition, t satisfies IR if and only if each principal’s expected payment when she bids
truthfully is at most her expected value. That is, Eo∼F|a∗(b−(cid:96),v(cid:96))
[v(cid:96)(o)]
∀(cid:96) ∈ [n], b ∈ V, v(cid:96) ∈ V (cid:96). By Property 2 in the definition of IIVCG (Definition 2), the above holds if
and ... | Incomplete Information VCG Contracts for Common Agency |
Specifically, as illustrated in Fig. 22 (a), the primary approach to crawl the Bokete website involves two key steps:
(1) Gathering Oogiri question IDs. On the Bokete website, an Oogiri question refers to content uploaded by either the
official site or users. It exists in the form of images, and even for T2T types, the... | Let’sThinkOutsidetheBox |
[646] Jing-Xuan Zhang, Zhen-Hua Ling, and Li-Rong Dai. 2018. Forward attention in sequence-to-sequence acoustic
modeling for speech synthesis. In 2018 IEEE International conference on acoustics, speech and signal processing (ICASSP).
IEEE, 4789–4793.
[647] Jing-Xuan Zhang, Zhen-Hua Ling, and Li-Rong Dai. 2019. Non-par... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Evaluate these responses.
Select the most consistent response based on majority consensus.
Start your answer with "The most consistent response is Response X" (without quotes).
Figure 7: A full example USC prompt for TruthfulQA. This corresponds to the question in Figure 2b.
16 | UNIVERSALSELF-CONSISTENCYFORLARGELANGUAGEMODELGENERATION |
Japanese
Please explain why this haiku is funny.
食べすぎる
今食べるのは
明日の分
The haiku is funny because it is a play on words.
The first line, “食べすぎる” means “to eat too
much”. The second line, “今食べるのは” means
“what am I eating now”. The third line, “明日の
分” means “for tomorrow”.
The humor comes from the fact that the speaker
is e... | PaLM 2 Technical Report |
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