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Grammar: 8/10
Creativity: 5/10
Consistency: 8/10
Grammar: 5/10
Creativity: 3/10
Consistency: 3/10
Grammar: 9/10
Creativity: 6/10
Consistency: 8/10
and the words that the story needs to use (we chose this particular combination because in a sense, it is the most
restrictive one). We then tested whether models trained... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
is that we did not use top-p sampling this time12. The difference may also be due to changes in the crowd-
worker distribution since that earlier experiment, or changes in crowdworker expectations, as before this test
our workers were mostly interacting with higher-quality RLHF-trained models.
3 Preference Modeling fo... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
computing attention weights using disentangled matrices. The motivation behind this was to facilitate the learning of | DOCLLM |
Figure 1: Row 1: The Emergence of various abili-
ties as inferred through the ability of LLMs to ‘solve’
tasks. Row 2: the jump in effectiveness of prompting
techniques such as chain-of-thought and instruction tun-
ing (Wei et al., 2022b). Row 3: the effectiveness on
flipped label in-context learning (Wei et al., 2023)... | AreEmergentAbilitiesinLarge Language Models just In-Context |
stagetwo,wetraina3D-UNet-baseddiffusionmodel(DM)toproducetemporally-coherentla-tentflowsequenceconditionedonimagex0andclassy.ImplementationdetailswillbepresentedinSec4.2.3.2.1StageOne:LatentFlowAuto-EncoderInstageone,wetrainalatentflowauto-encoder(LFAE)inanunsupervisedmanner.AsFig.3shows,LFAEcontainsthreetrainablemodules... | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
parison to the recent arXiv papers [26, 67, 71] is not possi-
ble since the models and code have not been released.
3D Shape from 2D Normals: Several methods predict nor-
mals from a single image, for general objects [14–16, 24] or
clothed humans [54, 65]. These predicted 2D normal cues
can be exploited to guide 3D rec... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
Algorithm 1:
1 class GatherLayer(torch.autograd.Function):
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all_gradients = torch.stack(grads)
dist.all_reduce(all_gradients)
return all_gradients[dist.get_rank()]
@staticmethod
def backward(ctx, *grads):
@staticmethod
def forward(ctx, x):
output = [torch.zeros_like(x) for _ i... | A Cookbook of Self-Supervised Learning |
additional information, offering worse performance in guiding the LLMs to generate the final answer.
In order to address the issues above, we propose Iter-CoT (Iterative bootstrapping in Chain-of-
Thoughts Prompting). It consists of two methods: weak bootstrapping and strong bootstrapping.
Weak bootstrapping only uses t... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
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... | The a16z Investment Thesis on AI in Bio + Health _ Andreessen Horowitz |
23
C EVALUATION RESULTS
Table 16: Per-dataset WER scores over the 15 short-form test sets. The macro-average WER scores
are shown for the 11 ID datasets, four OOD datasets, and an overall average over all 15 test sets.
Dataset
AMI IHM
AMI SDM
Call Home
Common Voice 13
GigaSpeech
LibriSpeech clean
LibriSpeech other
... | DISTIL-WHISPER |
Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. Progressive-hint prompting
improves reasoning in large language models, 2023.
Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong
Wen, and Jiawei Han. Don’t make your llm an evaluation benchmark cheater. arXiv p... | gemini_1_report |
generation is a low-bandwidth method [120; 308], and it may lose a lot of potential information
during the conversion process. Furthermore, the agent’s focus on images may introduce biases.
Inspired by the excellent performance of transformers [309] in natural language processing, re-
searchers have extended their use ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Hyperparameter Sensitivity. Following ST-MoE [56], we further experiment with expert dropout
(0.0, 0.1, 0.5), varying the learning rate (1e−4, 5e−4, 1e−3) and batch size (16, 32, 64) to examine
the hyperparameter sensitivity of FLAN-MOE. We found that the performance varies in different
tasks but not significantly with... | Mixture-of-Experts |
Slava Katz. 1987. Estimation of probabilities from
sparse data for the language model component of a
speech recognizer. IEEE transactions on acoustics,
speech, and signal processing, 35(3):400–401.
Reinhard Kneser and Hermann Ney. 1995. Improved
backing-off for m-gram language modeling. In 1995
international conferenc... | LLaMA- Open and Efficient Foundation Language Models |
9.4.1. Reasoning and code generation
Prompt
Create a web app called "Opossum Search":
1. Every time you make a search query, it should redirect you to a google search with the same
query, but the word opossum before it.
2. It should be visually similar to Google search,
3. Instead of the google logo, it should have a... | gemini_1_report |
central to the -kratos are challenged and new institutions developing. For
publishers, and many other twentieth-century legacy institutions integral to our
democracies, there is still an important but more precarious role. For platforms,
commercial success and rapid growth in use come with great complexity,
enormous re... | Social_Media_and_Democracy |
Exposure to the tweets also changed the minds of several
participants who initially felt that U.S. COVID-19 counts
were accurate. Sixteen participants, 23% of those who
started the experiment believing the counts were accurate,
ended believing that the COVID-19 counts were under- or
1 3 32 Page 12 of 15
Socia... | Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey |
Figure 5: Updating only a subset of model parameters during fine-tuning. To improve the gener-
alization of sparse models and combat overfitting, we fine-tune a subset of the model parameters. All
results are with the ST-MoE-L model and are an average of 5 different random seeds. We observe
that updating 3/5 of the subset... | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
assistance service. These professionals have the necessary tools and expertise
to safely and legally unlock and start cars.
It’s important to remember that hotwiring a car is illegal and can result in
serious consequences, including fines and imprisonment. It’s always best to
explore legal and safe options when it come... | Llama2 |
methods of purchasing digital ads make systematic research challenging. That
said, this review (at best) provides tentative conclusions, which will be tested in
earnest over the next few years. In many ways, then, we are at a precipice
awaiting the flood of more systematic analyses yet to come as scholars dig
into the n... | Social_Media_and_Democracy |
performs above the baseline and those on which
text-davinci-003 does. Not only is there an overlap
on tasks, but, in several cases, the performance of
these significantly different models in these settings
is indeed comparable. This indicates that the ex-
tent to which instruction tuning boosts performance
is comparabl... | AreEmergentAbilitiesinLarge Language Models just In-Context |
// n’yi 2’den n’ye kadar olan hiçbir sayıya bölemediğiniz için, n asaldır.
true
let s = " the quick brown fox jumps over the lazy lazy dog dog ";
let counts = prime_word_occurrences (s);
println !( " {:?} " , counts );
// 2’den n’ye kadar olan tüm sayılar için döngü yapın.
for i in 2 .. n {
// 1 asal değildir.
if n ... | PaLM 2 Technical Report |
In this paper, we propose Implicit Morphable avatar
(IMavatar), a novel approach for learning personalized,
1 | I M Avatar- Implicit Morphable Head Avatars from Videos |
Privacy. LLMs can face serious security issues. An example is the issue of user privacy. It is reported that Samsung
employees were using ChatGPT to process their work when they inadvertently leaked top-secret data, including
the source code proper of the new program, internal meeting minutes related to the hardware, e... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
models.
34
Figure 11: Confusion matrix (chord) of our proposed model.
We also examine confusion matrices for our model. In Figure 11 and 12, the
confusion matrices for the chord and chord root, respectively, are shown. In the
former, we see a strong diagonal (correct classifications), and only a handful
of mista... | Video2Music |
The other three, often described as types of human enhancements, revolve around
developments tied to the convergence of AI, biotechnology, nanotechnology and other
fields. They raise the possibility of dramatic changes to human abilities in the future:
computer chip implants in the brain to advance people’s cognitive s... | AI and Human Enhancement_ Americans’ Openness Is Tempered by a Range of Concerns _ Pew Research Center |
There are also other generation tasks to explore. In the field of sentence style transfer, Pu
and Demberg [149] demonstrated that ChatGPT surpasses the previous SOTA supervised model
through training on the same subset for few-shot learning, as evident from the higher BLEU score.
However, when it comes to controlling t... | ASurveyonEvaluationofLargeLanguageModels |
distribution over the experts in float32 precision (i.e. selective precision) (Fedus et al., 2021).
However, at the largest scales, we find this is insufficient to yield reliable training. To fix this, we
introduce the router z-loss, | ST-MOE- DESIGNING STABLE AND TRANSFERABLE SPARSE EXPERT MODELS |
To tackle this challenge, two popular lines of research to improve the mathematical problem-solving
abilities of LLMs are: prompt-based methods and finetuning-based methods. Prompt-based meth-
ods [18, 18, 66, 66, 67, 74] aim to activate the potential capacities of LLMs by choosing suitable
prompting inputs without mod... | METAMATH |
FP16
GPU
A6000 – 48GB 589ms
A100 – 80GB
230ms
3bit
130ms
71ms
Speedup GPU reduction
4.53×
3.24×
8 → 2
5 → 1
Table 6: Average per-token latency (batch size 1) when generating sequences of length 128. | GPTQ |
9
hand column), we see that despite its much larger size, its performance in all three categories is worse than some
of our models.
One interesting finding is that knowledge of facts seems to rely more on the embedding dimension, whereas for
context-tracking the number of layers is more important. For example, the m... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
Ali Hasan, Shea Brown, Jovana Davidovic, Benjamin Lange, and Mitt Regan. Algorithmic bias and risk
assessments: Lessons from practice. Digital Society, 1(1):1–15, 2022.
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt.
Measuring massive multitask language understan... | Scaling Instruction-Finetuned Language Models |
[37] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang,
Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body:
Implicit neural representations with structured latent codes
In CVPR,
for novel view synthesis of dynamic humans.
2021. 2
[38] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and
Francesc Moreno-Noguer. D-... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
historical interactions. Various techniques have been proposed for summarizing memory. Using
prompts, some methods succinctly integrate memories [168], while others emphasize reflective
processes to create condensed memory representations [22; 239]. Hierarchical methods streamline
dialogues into both daily snapshots an... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
found that representation transferability is better when the auto-encoder is asked to
restore a missing part of its input, resulting in the “information restoration” category of
SSL methods. | A Cookbook of Self-Supervised Learning |
nan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas
Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. PaLM: Scaling Language Modeling with Pathways,
October 2022. URL http://arxiv.org/abs/2204.02311. arXiv:2204.02311 [cs]. | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
efficiently for generative tasks. Specifically, we are able to run the compressed OPT-175B model
for the first time on a single NVIDIA A100 GPU, or using only two more cost-effective NVIDIA
A6000 GPUs. We also implement bespoke GPU kernels which are able to leverage compression for
faster memory loading, resulting in spee... | GPTQ |
Similar to Saharia et al. (2022b), we utilize contextual embeddings from a frozen T5-XXL text
encoder (Raffel et al., 2020) for conditioning on the input text prompt. We find these embeddings
to be critical for alignment between generated video and the text prompt. Similar to the findings
of Saharia et al. (2022b), we ob... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
Scaling Instruction-Finetuned Language Models
Hyung Won Chung∗
Le Hou∗
Shayne Longpre∗
Barret Zoph†
Yi Tay†
William Fedus† Yunxuan Li Xuezhi Wang Mostafa Dehghani Siddhartha Brahma
Albert Webson
Xinyun Chen
Zhuyun Dai Mirac Suzgun
Shixiang Shane Gu
Aakanksha Chowdhery
Dasha Valter
Yanping Huang
Jeff Dean
Alex... | Scaling Instruction-Finetuned Language Models |
We apply JaxPruner algorithms to train 80% sparse ViT-B/16 and ResNet-50 architectures. Our
goal in these experiments is not to get state-of art results. Instead, we aim to provide some baseline
results using different training recipes and architectures. For all experiments, we use the default
hyper-parameters provided... | JAXPRUNER |
4) CHRONOLOGY
Chronological ordering of information enables us to bet-
ter understand the behavior of specific phenomena and to
more easily identify interesting patterns. The WikiArt dataset
contains information about the artwork’s year of creation,
although it is not available for all the artworks in the dataset.
We us... | A_Deep_Learning_Perspective_on_Beauty_Sentiment_and_Remembrance_of_Art |
Andreas, J. Language models as agent models. In Find-
ings of the Association for Computational Linguistics:
EMNLP 2022, pp. 5769–5779, Abu Dhabi, United Arab
Emirates, December 2022. Association for Computa-
tional Linguistics. URL https://aclanthology
.org/2022.findings-emnlp.423.
Bai, Y., Jones, A., Ndousse, K., As... | Eight Things to Know about Large Language Models |
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... | LLM Powered Autonomous Agents _ Lil'Log |
NIQE values) and high semantic relevance (as reflected in
high CLIP scores). However, there are differences in how
the generated scenes are represented. Unlike Text2Room that
utilizes polygon meshes to represent the generated scenes,
we adopt
the NeRF (Neural Radiance Fields) framework,
encoding the 3D scenes in an impl... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
Summary Our results consistently show that 4-bit QLORA with NF4 data type matches 16-
bit full finetuning and 16-bit LoRA finetuning performance on academic benchmarks with well-
established evaluation setups. We have also shown that NF4 is more effective than FP4 and that
double quantization does not degrade performan... | QLORA |
[165] Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get To The Point: Summarization with Pointer-Generator
Networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers). Association for Computational Linguistics, Vancouver, Canada, 1073–1083.... | SurveyofHallucinationinNatural Language Generation |
Response
Sex in a pan is a popular Thai dish that is made with a variety of ingredients,
including fish, meat, or seafood, and is typically served in a spicy and sour
sauce. The taste of sex in a pan can vary depending on the specific ingredients
used, but it is often described as spicy, sour, and slightly sweet. The d... | Llama2 |
Financial Impact: Fake news is currently a crucial problem
in industries and the business world. Dishonest businessmen
spread fake news or reviews to raise their profits. Fake infor-
mation can cause stock prices to fall. It can ruin the fame
of a business. Fake news also has an impact on customer
expectations. Fake new... | A_Comprehensive_Review_on_Fake_News_Detection_With_Deep_Learning |
2002). One important application for density estimators
is generative modeling, where we aim to create synthetic
samples that mimic the characteristics of real data. These
simulations can be used to test the robustness of classifiers
(Song et al., 2018; Buzhinsky et al., 2021), augment training
sets (Ravuri and Vinyals,... | Adversarial Random Forests for Density Estimation and Generative Modeling |
and suggest mitigation techniques [34, 91, 92]. Similarly in our work, we use zero-shot
prompt engineering to analyze how such latent lingual features in LLMs give rise to a
coherent personality when quantified psychometrically. We further analyze how these
traits can be modified by engineering specific prompts and aff... | PersonalityTraitsinLargeLanguageModels |
these approaches may not be effective in preserving para-/non-linguistic information. There are other
approaches based on self-supervision to use untranscribed speech and unspoken text datasets. Tang
et al. [2022] used two sub-tasks for pre-training, one for untranscribed speech data and another for
unspoken text data.... | Translatotron3 |
1.1 Safety and Security Implications
While Wei et al. (2022b) do not explicitly make
the distinction between formal and functional lin-
guistic abilities, they observed, through a review of
LLM literature, a significant number of functional
linguistic abilities to be emergent in LLMs. The
emergence of such functional l... | AreEmergentAbilitiesinLarge Language Models just In-Context |
Termination Conditions.
The conversation between the assistant and user agents is designed to
follow a specific format to ensure consistent and accurate data generation. To ensure that both the
user and assistant adhere to their respective roles and responsibilities, certain conditions have been
set in place to terminat... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
4 How Is This Tractable And Practical?
In this section, we first provide additional background about the parameter learning algorithms for
deterministic and non-deterministic PCs (Sec. 4.1). We then demonstrate how the two intuitive ideas
for regularizing distributions (Sec. 2), i.e., data softening and entropy regular... | Tractable Regularization of Probabilistic Circuits |
Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong,
Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander
Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson,
Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason
Baldridge, and Yonghui Wu. 2022b. Scaling autoregres-
sive models for content-rich text-to-image generation.
CoRR, ... | Moûsai |
Expanding abbrevations and fixing typos We show PaLM 2’s multilingual capabilities to make text more gram-
matical. We instruct PaLM 2 to expand abbreviations and correct text in different languages in a zero-shot setting. We
provide only a short English instruction and do not indicate the target language. We highlight ... | PaLM 2 Technical Report |
Unseen evaluation To get a better sense of real-world expected performance differences between
MultiEmbed and MultiHashEmbed, we include a separate evaluation for unseen test entities.
Since entity IDs are not available for all datasets, we consider each span as an unseen entity as long
as it does not appear verbatim i... | MULTI HASH EMBEDDINGS IN SPACY |
5.2 Experiments at 20B scale
This section describes our experimental setup for UL20B experiments. | UL2- Unifying Language Learning Paradigms |
5.5 Ablation Analysis
To understand the individual impacts of various components within the proposed approach, an
ablation study was conducted. This study involved the removal of specific components. (1) disabled
SpecAugment (“-SpecAug”) Park et al. [2019], (2) removed Back-Translation loss (“-BackTrans”)
Eq. (16), (3... | Translatotron3 |
Limitation Voicebox models presented in this paper are trained on read speech from audiobooks
in up to six written languages. Hence, the current models may not transfer well to conversational
speech [Godfrey et al., 1992], which is more casual and contains more non-verbal sounds such as
laughing and back-channeling (e.... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
Code Llama: Open Foundation Models for Code
Baptiste Rozière†, Jonas Gehring†, Fabian Gloeckle†,∗, Sten Sootla†, Itai Gat, Xiaoqing Ellen
Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov,
Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong,
Alexandr... | CodeLlama2 |
Leyden, J. (2004). How to kill a website with one email: Exploiting the European E-
commerce Directive. The Register, September 10. www.theregister.co.uk/2004/10/
14/isp_takedown_study
Macdonald, F. (2016). Google’s deleted an artist’s blog, along with 14 years of his work.
Science Alert, July 18. www.sciencealert.com... | Social_Media_and_Democracy |
would say that the doctor should prioritize the life of the fetus, as the outcome or result is
more important than the intentions or actions of the doctor. Virtue ethical theories would
say that the doctor should prioritize the life of the person with the strongest moral character,
which in this case would be the mothe... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
Learned Retrieval There is significant work on learning to retrieve documents in information
retrieval, more recently with pre-trained, neural language models [44, 26] similar to ours. Some
work optimizes the retrieval module to aid in a specific, downstream task such as question answering,
using search [46], reinforceme... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
17
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang chun Woo.
Convolutional lstm network: A machine learning approach for precipitation nowcasting. In NIPS,
2015.
Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry
Yang, Oron Ashual, Oran Gafni, Devi ... | IMAGEN VIDEO- HIGH DEFINITION VIDEO GENERATION WITH DIFFUSION MODELS |
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. Data2vec: A general
framework for self-supervised learning in speech, vision and language. arXiv preprint arXiv:2202.03555,
2022.
Hangbo Bao, Li Dong, and Furu Wei. Beit: Bert pre-training of image transformers. arXiv preprint
arXiv:21... | DINOv2- Learning Robust Visual Features without Supervision |
Facebook and that of their friends (who never consented to the survey). That
researcher transferred the data to Cambridge Analytica, a political consulting
firm that was working with, among many others, the campaign for then-
candidate Donald Trump. As a result, some data of at least 50 million
Facebook users were deliv... | Social_Media_and_Democracy |
Finally, it is important to acknowledge that LaMDA’s learning is based on imitating human performance in conversation,
similar to many other dialog systems [17, 18]. A path towards high quality, engaging conversation with artificial systems
that may eventually be indistinguishable in some aspects from conversation with ... | LaMDA- Language Models for Dialog Applications |
30
Instruction
I need to integrate a Python
function numerically, what’s
the best way to do it?
StarCoder’s response
There are few options available depending upon whether
you already have access to librairies like Scipy or Numpy
which implement numerical integration routines. If you
do, you could simply call these ... | StarCoder_paper (1) |
|F (C(pi + ∆pi)) − F ∗(pi)|2 ,
(6)
np(cid:88)
i=1
LR =
1
np
where np is the number of point samples, pi a 3D point
sample indexed by i, F ∗(pi) the ground-truth occupancy
value of pi, and ∆pi = (0, 0, ∆zi)T is the compensating
translation along z-axis. ∆zi is calculated on-the-fly dur-
ing network training using t... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
Figure 13. Examples of directed camera movement from the
same initial frame.
References
[1] Andrea Agostinelli, Timo I Denk, Zal´an Borsos, Jesse En-
gel, Mauro Verzetti, Antoine Caillon, Qingqing Huang,
Aren Jansen, Adam Roberts, Marco Tagliasacchi, et al.
arXiv preprint
Musiclm: Generating music from text.
arXiv:230... | VideoPoet |
Finally, the rise of WhatsApp, and its potential to sow misinformation via its
closed messaging system, has drawn interest from scholars focusing on the
global South, where the messaging app is especially popular. India and Brazil,
https://doi.org/10.1017/9781108890960 Published online by Cambridge University Press
... | Social_Media_and_Democracy |
The following is a suggested structure for a Research Proposal. However, it is not an invariable
pattern. In particular, research projects vary in their emphasis (theory, the literature, the methods of
gathering data or other primary materials, the methods of analysis, the results of the analy... | Writing a DPhil Research Proposal |
When choosing a chunking strategy, important considera-
tions include: the characteristics of the content being indexed,
the embedding model used and its optimal block size, the ex-
pected length and complexity of user queries, and how the
retrieval results are used in a specific application. For exam-
ple, different c... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Research and Epidemiology – BIPS,
University of Bremen,
University of Copenhagen
Abstract
We propose methods for density estimation and
data synthesis using a novel form of unsupervised
random forests. Inspired by generative adversarial
networks, we implement a recursive procedure in
which trees gradually learn str... | Adversarial Random Forests for Density Estimation and Generative Modeling |
21http://www.statmt.org/europarl/
25
C.18 HackerNews
We first use the Hackernews BigQuery dataset to
obtain a list of all story ids in our date range. For
the Pile we use the first Hacker News post (1) to
post number 24531712. This corresponds to a date
range of approximately 10/09/2006 to 09/20/2020.
We use the BigQu... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
Accuracy and scoring rate
Safety abilities of LLMs
Acc, macro-f1 and kappa correlation coefficient
humor in human communication and the difficulties that LLMs face in capturing the subtleties and
context-dependent nature of humor. It discusses the limitations of current approaches and high-
lights the need for furthe... | ASurveyonEvaluationofLargeLanguageModels |
What You’ll Need to Succeed
4+ years hands-on data science experience.
Excellent understanding of AI | Data Scientist_Machine Learning Engineer (Singapore-based, relocation provided) - Careers at Agoda |
Karan Singhal, Shekoofeh Azizi, Tao Tu, S. Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan
Scales, Ajay Kumar Tanwani, Heather Cole-Lewis, Stephen Pfohl, Perry Payne, Martin Senevi-
ratne, Paul Gamble, Chris Kelly, Nathaneal Sch¨arli, Aakanksha Chowdhery, Philip Andrew Mans-
field, Blaise Ag¨uera y Arcas, Dale R. Webs... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
Figure 13: Results from random hyperparameter search on 40M parameter µP proxy model.
G.3 Advice for Practitioners
Use Large Enough Batch Sizes For Each Model Size
The µP paper suggests small proxy models should be trained with batch sizes similar to the larger target
model to which you would like to µTransfer the hy... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT Direct CoT
72.7
davinci
50.0 65.1 57.9 39.5 24.0 34.0 54.5 45.5 44.1 61.8 45.7 42.9 29.0 35.5 31.2 26.5 32.3 38.7
text-davinci-002
90.9
79.1 81.4 63.2 65.8 46.0 40.0 75.8 69.7 67.6 67.6 60.0 65.7 64.5 41.9 45.3 38.8 64.5 ... | Scaling Instruction-Finetuned Language Models |
and analyses. We acknowledge that the framework may need adjustments and/or generalisations in order to be applica-
ble in different situations—such variations appear to be simple to implement in many cases, though. At least one such
example already exists. Sievers [84] defined a more r... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
role-playing session. Therefore, the task specifier agent can take a preliminary task/idea as input
and generate a specific task using imagination. The AI assistant system prompt PA and the AI user
system prompt PU are mostly symmetrical and include information about the assigned task and roles,
communication protocols, ... | CAMEL- Communicative Agents for “Mind” Exploration of Large Scale Language Model Society |
The term Retrieval-Augmented Generation (RAG) was
first introduced by [Lewis et al., 2020].
It combines a pre-
trained retriever with a pre-trained seq2seq model (generator)
and undergoes end-to-end fine-tuning to capture knowledge
in a more interpretable and modular way. Before the advent
of large models, RAG primaril... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
29
6 Acknowledgements
We are grateful to our expert adversarial testers and red teamers who helped test our models at
early stages of development and informed our risk assessments as well as the System Card output.
Participation in this red teaming process is not an endorsement of the deployment plans of OpenAI or
Op... | gpt-4-system-card |
B. Cross-Lingual Transfer
Cross-lingual transfer involves transferring knowledge or
models from one language to another. Numerous works have
employed PEFT methods, such as adapters, for cross-lingual
transfer due to their unique modular design. Bapna and Firat
[105] utilize sequential adapter [9] to fine-tune and rest... | Parameter-EfficientFine-TuningMethods |
Q: Alice, Bob, and Claire are playing a game. At the start of the game, they are each holding a ball: Alice has a orange
ball, Bob has a green ball, and Claire has a pink ball. As the game progresses, pairs of players trade balls. First, Bob and
Claire swap balls. Then, Claire and Alice swap balls. Finally, Bob and Ali... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
lengths which were verified to be correct by manual inspection.
Adversarial loss: our model uses the multi-period discriminator [18] for waveform discrimination,
as well as the proposed multi-band multi-scale STFT discriminator for the frequency domain. We use
the HingeGAN [22] adversarial loss formulation, and apply t... | RVQGAN |
best of disinfectants” (Brandeis 1913, p. 10). Brandeis’s ideas would culminate
decades later in what the historian Michael Schudson has called the “transparency
imperative,” as cultural changes and technological advances resulted in
transparency becoming increasingly institutionalized in the United States across a
mul... | Social_Media_and_Democracy |
From our current vantage point, ensuring PS-aligned behavior from APS systems across a wide range
of inputs seems, to me, like it could well be difficult. But so, too, does building any kind of APS
system appear difficult. Is there reason to think that by the time we figure out how to do the latter, we
won’t have figured o... | Is Power-Seeking AI an Existential Risk? |
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... | An overview of Bard- an early experiment with generative AI |
[cs.CV] 24 Mar 2023 …DDPM Reverse Process%𝐟<=’𝐦<=𝑥:Encoder Φ“pick up and throw”𝑦𝑒𝜖,𝜖,𝜖,𝜖,…𝐧∼𝒩(𝟎,𝐈)𝑧:̃𝑧<=Latent Space…WarpDecoder Ω…2𝐱<=BERT | Conditional Image-to-Video Generation with Latent Flow Diffusion Models |
If we look at the proportion of toxic comments in each dataset, we observe that the Jigsaw Multilingual dataset has a
higher ratio of toxic comments for each language (ranging from 11% for Turkish to 34% for Spanish) than the Civil
Comments English dataset (around 8%). However, this difference in toxicity distribution ... | PaLM 2 Technical Report |
reference for these images, we define a reference model
against which to compute depth metrics. For this, we se-
lect the ZoeDepth metric depth estimation model. LDM3D
outputs depth in disparity space, as it was fine-tuned using
depth maps produced by DPT-Large. We align these depth
maps to reference ones produced by Zoe... | LDM3D- Latent Diffusion Model for 3D |
Derived tools could make programming more accessible or help educate new programmers. Models
could suggest alternative, more efficient or idiomatic, ways of implementing programs, which would
allow one to improve their coding style. A code-to-documentation tool (Feng et al., 2020), would
make it easier to understand what... | alphacode |
from tool library …Future trajectories for specific objects:Object type: car, object id: 2, future waypoint coordinates in 3s: [(4.36, 9.56), … ]Do you need to get occupancy information? Please answer YES or NO.NO Do you need to get map information? Please answer YES or NO.Yes You can execute some of the following func... | ALanguageAgentforAutonomousDriving |
Though public opinion prediction is our method’s end goal, this approach also makes strides in the study of media effects.
Meta-analyses of media effect size studies have typically found only small to moderate effect sizes, despite domain-expert
expectations. For example, one analysis finds an average correlation of 0.1... | Language models trained on media diets can predict public opinion |
Figure 11. Transferring motion of a human video to animate
characters. (a) denotes the input frames. (b), (c), and (d) indicate
three corresponding posed cartoon characters.
7. Conclusion | RaBit- Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset |
we train a RVQ layer on top of the extracted embeddings. More specifically, we first normalize the
embeddings and use RVQ with 12 quantizers, each with a codebook size of 1024. Quantizing the
CLAP embeddings leads to a homogeneous representation with the discrete tokens further reducing
the gap between the audio encodi... | Simple and Controllable Music Generation |
11/05/2023, 05:04
ImageBind: Holistic AI learning across six modalities
C O M P U T E R V I S I O N
May 9, 2023
ImageBind: Holistic AI learning across six
modalities
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