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3.2.4 TCNN Model Variants
The architecture of TCNN is based upon two principles:(1) There is no information “leakage” from
future to past;(2) the architecture can map an input sequence of any length to an output sequence
of the same length, similar to RNN. TCN consists of dilated, causal 1D fully-convolutional layers
w... | AReviewofDeepLearningTechniquesforSpeechProcessing |
reduces memory fragmentation, a common issue in traditional LLM memory allocation strategies. As a result, it allows the
LLM to process longer sequences within the constraints of limited memory resources. | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
We present IMAGEBIND, an approach to learn a joint
embedding across six different modalities - images, text, au-
dio, depth, thermal, and IMU data. We show that all combi-
nations of paired data are not necessary to train such a joint
embedding, and only image-paired data is sufficient to bind
the modalities together. ... | IMAGEBIND- One Embedding Space To Bind Them A |
The dominant approach for performing open-domain question answering (ODQA) is the retrieve–read
framework (Chen et al., 2017; Lee et al., 2019; Karpukhin et al., 2020), also referred to as open-book
question answering. Given a question, this approach first employs a retriever over a large evidence
corpus (e.g. Wikipedia... | STANDING ON THE SHOULDERS OF GIANT FROZEN LANGUAGE MODELS |
Our work is structured as follows. First, our work offers a brief introduction to LLMs by discussing the most important
models, such as GPT-style and BERT-style architectures. Then, we delve into the critical factors that influence model
performance from the data perspective, including pre-training data, training/tunin... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Ruther-
ford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric
Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero,
Karen Simonyan, Erich Elsen, Jack W... | gemini_1_report |
Learning (DRL), has emerged [68; 69]. This allows agents to learn intricate policies from high-
dimensional inputs, leading to numerous significant accomplishments like AlphaGo [70] and DQN
[71]. The advantage of this approach lies in its capacity to enable agents to autonomously learn in
unknown environments, without ... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
[47] Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu,
and Alexander Miller. Language models as knowledge bases? In Proceedings of the 2019
Conference on Empirical Methods in Natural Language Processing and the 9th International
Joint Conference on Natural Language Processing (... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
Plagiarism
Make sure that you acknowledge the authors of ALL publications you use to write your proposal. Failure to do so will be
considered as plagiarism. Do not copy word for word what an author has said. You may think that the original author has
presented the information using the best possible words in the best... | research proposal guidance |
Inspired by models such as NExT-GPT [71], the M2UGen
model incorporates specialized audio tokens of the form
[AU Di], where i ∈ {0, 1,··· , 7}, to distinguish between
music question answering and generation tasks. The num-
ber of audio tokens is a hyper-parameter that determines
the dimension of the input to the music ... | M2UGen |
How did people encounter this information? In the following section, we
explore research findings on the spread and dissemination of misinformation
online in general. Given the prominent role of social media in narratives about
fake news, we also consider existing evidence on its prevalence on these
platforms. Facebook-... | Social_Media_and_Democracy |
C. Feichtenhofer, H. Fan, Y. Li, and K. He. Masked autoencoders as spatiotemporal learners.
arXiv preprint arXiv:2205.09113, 2022. 38
T. Furlanello, Z. Lipton, M. Tschannen, L. Itti, and A. Anandkumar. Born again neural
networks. In International Conference on Machine Learning, pages 1607–1616. PMLR,
2018. 13
T. Gao... | A Cookbook of Self-Supervised Learning |
9.4 Appropriateness as a concept and a metric | LaMDA- Language Models for Dialog Applications |
Attention in Transformers. Attention mechanism, first proposed by Bahdanau et al. [28],
has revolutionized sequence modeling and transduction models in various tasks of NLP, speech,
and computer vision [60, 80, 148, 566]. Broadly, it allows the model to focus on specific parts of the
input or output sequence, without b... | AReviewofDeepLearningTechniquesforSpeechProcessing |
We also manually analyze 50 randomly sampled outputs of the model that were incorrect on GSM8K
for LaMDA 137B. There are many ways that a chain of thought can be incorrect, making the design
of error categorization non-trivial. We decided to categorize errors into what changes are needed to
make the chain of thought co... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
The BEGIN Benchmark. Findings of ACL (2021).
[43] Mihail Eric and Christopher Manning. 2017. A Copy-Augmented Sequence-to-Sequence Architecture Gives Good
Performance on Task-Oriented Dialogue. In Proceedings of the 15th Conference of the European Chapter of the Association
for Computational Linguistics: Volume 2, Sho... | SurveyofHallucinationinNatural Language Generation |
Future AI systems might actively reduce human control _________________________ 26
Conclusion _______________________________________________________________ 28
Glossary _________________________________________________________________ 29 | Capabilities and risks from frontier AI |
influence of power (Gaonkar and McCarthy 1994). Bentham is often identified
as the forefather of modern transparency as used in the political sense (Hood
2006; Baume 2018). He famously wrote that “the more strictly we are watched,
the better we behave,” an edict that inspired his approach to open government,
arguing that... | Social_Media_and_Democracy |
Tal Schuster, Adam Fisch, and Regina Barzilay.
2021. Get your vitamin C! Robust fact verifica-
tion with contrastive evidence. In Proceedings
of the 2021 Conference of the North American
Chapter of the Association for Computational
Linguistics: Human Language Technologies,
pages 624–643, Online. Association for Com-
pu... | ProoFVer- Natural Logic Theorem Proving for Fact Verification |
Journal of Mathematical and Statistical Psychology, 61(1):29–48, 2008.
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, and Ece Kamar. Toxigen: A
large-scale machine-generated dataset for adversarial and implicit hate speech detection. In Proceedings
of the 60th Annual Meeting of the Associa... | Llama2 |
11
R´emi Coulom. Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. In H. Jaap
van den Herik, Paolo Ciancarini, and H. H. L. M. (Jeroen) Donkers (eds.), Computers and Games,
Lecture Notes in Computer Science, pp. 72–83, Berlin, Heidelberg, 2007. Springer. ISBN 978-3-
540-75538-8. doi: 10.1007/978-... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Niko-
lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher,
Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy
Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Veda... | CHAIN-OF-THOUGHTREASONING IS APOLICY IMPROVEMENTOPERATOR |
3
Figure 2: Diagram of the MultiHashEmbed algorithm. First, from the orthographic form of
“Apple” various features are extracted. Then each feature is hashed four times and modded to
their feature-specific tables. The four vectors per table summed up and the resulting pooled feature
vectors are fed to a Maxout layer t... | MULTI HASH EMBEDDINGS IN SPACY |
[21] Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021. Improving Faithfulness in Abstractive Summarization with
Contrast Candidate Generation and Selection. In Proceedings of the 2021 Conference of the North American Chapter of
the Association for Computational Linguistics: Human Language Technologies. 5935–5941.
... | SurveyofHallucinationinNatural Language Generation |
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Appendix Table A6 | Codeforces per-contest results. Results of running AlphaCode on all Codeforces
competitions. Top XX% percentile ranks are given, where the number indicates the percentage of
competitors who performed better than our system. Result... | alphacode |
executing complex tasks across different appli-
cations. To demonstrate the practicality of our
agent, we conducted extensive testing over 50
tasks in 10 different applications, including so-
cial media, email, maps, shopping, and sophis-
ticated image editing tools. The results affirm
our agent’s proficiency in handli... | AppAgents |
models solve complex requests, which allows for better interpretability and transparency. Users can easily
understand why certain tools are called and how they contribute to the final output, which can improve trust
and facilitate human-machine collaboration. (4) Improved Robustness. Foundation models are susceptible
to... | Tool Learning with Foundation Models |
14 Written comments and a transcript of the hearings are available at: www.fec.gov/updates/june-
27-28-2018-public-hearing/.
15 This last point is a contestable one, to be sure. Some have argued, as the June 2018 debate at the
FEC made clear, that expanded disclaimer mandates for online ads could be burdensome and th... | Social_Media_and_Democracy |
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4
Table 2: Mining patterns and input-output templates. {VERBAL} is replaced with the verbalizers
in Table 3. For mining, {WORD} captures a single word, and {SENT} captures a single sentence.
Each input-output template is paraphrased into multiple variations. We also turn the task around—
exchanging the question and a... | ADAPTINGLARGELANGUAGEMODELSVIA READINGCOMPREHENSION |
*These authors contributed equally to this work.
Figure 1: Key elements of DocLLM. (1) Input documents contain text tokens and their bounding boxes. (2) Attention
mechanism of LLMs are extended to capture dependencies between text semantics and spatial layouts. (3) Infilling text
blocks is used as pre-training object... | DOCLLM |
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao 1 , Yun Xiong 2 , Xinyu Gao 2 , Kangxiang Jia 2 , Jinliu Pan 2 , Yuxi Bi 3 , Yi
Dai1 , Jiawei Sun1 , Qianyu Guo4 , Meng Wang 3 and Haofen Wang 1,3 ∗
1 Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University
2 Shan... | RAG forLargeLanguageModels-ASurvey |
3.3.3 Human Evaluation
We follow the human evaluation protocol given by
Wang et al. (2022a), which categorizes the quality
of the generated text into four levels:
• Rate-A: The generated text is of high quality;
• Rate-B: The generated text is acceptable but
has minor errors;
• Rate-C: The generated text has signific... | LaMini-LM- A Diverse Herd of Distilled Models from Large-Scale Instructions |
at producing economically valuable systems.
Concretely, consider these three superficially very differ-
ent systems: OpenAI’s original GPT can perform simple
text-labeling tasks but cannot generally produce coherent
text (Radford et al., 2018). GPT-2 adds the ability to pro-
duce text of reasonably high quality, as well... | Eight Things to Know about Large Language Models |
Visualization
Expected effect
Use when
Description
Visualization
- The human agent saves cognitive load by outsourcing the options set creation to
the AI. Thus, the quality of the human’s primary work increases.
- Inexperienced human agents improve their solution deduction capabilities.
- The quality of the AI’s r... | DevelopingTeamDesignPatternsfor HybridIntelligenceSystems |
#Given Prompt#:
<Here is instruction.>
6
#Created Prompt#:
Response Generation. While using the large language model prompted with the evolving prompts
to rewrite instructions, we also use the same LLM to generate the corresponding responses for the
evolved instructions and add them to the instruction pool. To ensu... | WizardLM- Empowering Large Language Models to Follow Complex Instructions |
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... | Principal-agent VCG contracts - ScienceDirect |
5 Bias, Toxicity and Misinformation
Large language models have been showed to re-
produce and amplify biases that are existing in
the training data (Sheng et al., 2019; Kurita et al.,
2019), and to generate toxic or offensive con-
tent (Gehman et al., 2020). As our training dataset
contains a large proportion of data ... | LLaMA- Open and Efficient Foundation Language Models |
Beyond our own products, we think it’s important to make it easy, safe and scalable for others to benefit from
these advances by building on top of our best models. Next month, we’ll start onboarding individual developers,
creators and enterprises so they can try our Generative Language API, initially powered by LaMDA ... | Google AI updates_ Bard and new AI features in Search |
6 . 3
P E R F O R M A N C E I M P R O V E M E N T T H R O U G H T H E T R A I N I N G P R O C E S S
We evaluate the performance of StarCoderBase at several training checkpoints after every 200B
tokens seen out of the total 1000B. Figure 2 (right) shows how performance (pass@1) changes
23
10−1100101102Sizeafterdedup... | StarCoder_paper (1) |
3. We show FILM can easily adapt to newly in-
jected and modified facts without retraining.
2 Fact Injected Language Model Model
The Fact Injected Language Model (FILM) model
(see Figure 1) extends the Transformer (Vaswani
et al., 2017) architecture of BERT (Devlin et al.,
2019) with additional entity and facts memori... | Adaptable and Interpretable Neural Memory Over Symbolic Knowledge |
2) Pretrained Weight Masking: Pretrained weight masking
employs pruning criteria like threshold and Fisher information
to measure the importance of pretrained weight to construct
a binary mask matrix for weight masking. Threshold-Mask
[36] utilizes the threshold to construct a binary mask ma-
trix to select pretrained ... | Parameter-EfficientFine-TuningMethods |
intelligence (EAI).
These advances highlight the growing potential of LLMs to exhibit emotional intelligence, a crucial
facet of achieving AGI. Bates et al. [537] explored the role of emotion modeling in creating more
believable agents. By developing socio-emotional skills and integrating them into agent architectures,... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
Figure 6: The classification accuracy of
BiomedGPT, fine-tuned with both seen (x-ray) and
unseen (ultrasound and CT) modalities, is depicted
in this comparison. Here, ResNet-50, trained from
scratch according to the protocol in (Yang et al.,
2021), serves as a reference baseline.
4 Discussion | BiomedGPT |
Rather than storing a separate vector for each symbol, hash embeddings apply the hashing trick in
order to reduce the memory footprint. This method is inspired by Bloom filters (Bloom, 1970), a
simple probabilistic data structure to solve the membership problem, i.e., to answer the question of
whether we have seen an el... | MULTI HASH EMBEDDINGS IN SPACY |
Retrieve-and-Edit approaches Our method shares some similarities with retrieve-and-edit style
approaches, where a similar training input-output pair is retrieved for a given input, and then edited
to provide a final output. These approaches have proved successful in a number of domains including
Machine Translation [18,... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
172–173
Badaan, Vivianne, 74
bag-of-communities technique, hate speech
detection, 60
bag-of-words method, hate speech detection, 59
Bail, Christopher A., 45, 48
Bakshy, Eytan, 43
Balkin, Jack, 323
Ballard, Andrew O., 132–133
banning of content by social media platforms,
71–73, see also content takedown
Barberá, Pa... | Social_Media_and_Democracy |
the denoising procedure of MCG [11], consisting of the fol-
lowing repeated steps:
3DMM Fitting and Texture Initialization. We rely on
3DMMs to recover a rough 3D shape of the face from a 2D
image as a mesh S ∈ Rn×3 with n vertices. Specifically,
we employ a linear 3DMM:
S(ps, pe) = m + Usps + Uepe
(8)
consisting of ... | Relightify-Relightable3DFacesfromaSingleImageviaDiffusionModels |
(NeurIPS) Workshop on Deep Learning.
Or Honovich, Uri Shaham, Samuel R Bowman, and
Omer Levy. 2022.
Instruction induction: From
few examples to natural language task descriptions.
arXiv preprint arXiv:2205.10782.
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu,
Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2022.
Large la... | SELF-INSTRUCT- Aligning Language Model with Self Generated Instructions |
Barbu, A., Mayo, D., Alverio, J., Luo, W., Wang, C., Gut-
freund, D., Tenenbaum, J., and Katz, B. Objectnet: A
large-scale bias-controlled dataset for pushing the lim-
its of object recognition models. Advances in neural
information processing systems, 32, 2019.
Caruana, R. Multitask learning. Machine learning, 28(1):... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
RHO: To handle the hallucination challenge in
dialogue response generation, (Ji et al., 2023a)
proposes a framework called RHO that utilizes
the representations of linked entities and relation
predicates from a KG to generate more faithful re-
sponses. To improve faithfulness, they introduce
local and global knowledge-... | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
𝑃 must be odd, so 𝑃 mod 2 and 𝑃 mod (𝑃 − 1) both equal 1.
We sampled from our model, first with the tag “brute force”, and then with the tag “number theory”.
These tags changed the sample distribution as demonstrated by the first successful samples in the
two sampling runs, shown in Figure 12. The “brute force” appro... | alphacode |
null | Denoising Diffusion Probabilistic Models |
CREATE TABLE class (
class_code text ,
crs_code text ,
prof_num number ,
primary key ( class_code ) ,
foreign key ( prof_num ) references professor ( emp_num ) ,
foreign key ( crs_code ) references course ( crs_code )
)
insert into class (class_code, crs_code, prof_num) values (10012, ACCT-211,
105);
CREATE TABLE empl... | Teaching Large Language Models to Self-Debug |
OPT (Zhang et al., 2022), LLaMA (Touvron et al., 2023), and InCoder models (Fried et al., 2022)
under a non-commercial license and only provided high-level details about the data collection and
filtering process. Notable exceptions are PolyCoder (Xu et al., 2022) and SantaCoder (Ben Allal et al.,
2023): open-access Cod... | StarCoder_paper (1) |
using these experiences stored in the multimodal memory.
In summary, JARVIS-1 pilots the effort towards a human-
like multi-task and autonomous agent in an open-world,
embodied environment like Minecraft. We would like to
share the key takeaways of what we have learned during its
development as follows: | JARVIS-1 |
Memory Footprint. Memory footprint refers to the amount of Random Access Memory (RAM) required to load and run a
model during inference or training. This metric is crucial for understanding the model’s operational demands, especially in
resource-constrained environments like edge devices or servers with limited memory ... | TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey |
Section 5.3, although only trained on three paired joint generation tasks (i.e, Text+Audio, Text+Image,
and Video+Audio), CoDi is capable of generating assorted combinations of modalities simultaneously
that are unseen in training, e.g., joint image-text-audio generation in Fig. 5.
4 Experiments
4.1 Training Objectiv... | Any-to-Any Generation via Composable Diffusion |
[32] A. Páez, The pragmatic turn in explainable artificial intelligence (xai), Minds Mach. 29 (3) (2019) 441–459.
[33] R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, N. Elhadad, Intelligible models for healthcare: predicting pneumonia risk and hospital 30-day
readmission, in: Proceedings of the 21st ACM SIGKDD Inter... | Knowledge graphs as tools for explainable machine learning: A survey |
Inference-time Mention Detection We intro-
duce a mention detection layer to avoid dependence
at inference on an external mention detector. The
mention detection layer applies a BIO1 classifier
to the first transformer block’s output. We decode
the entire BIO sequence, ensuring that inconsistent
sequences are disallowed.... | Entities as Experts- Sparse Memory Access with Entity Supervision |
2.19 PhilPapers
The PhilPapers7 dataset consists of open-access
philosophy publications from an international
database maintained by the Center for Digital Phi-
losophy at the University of Western Ontario. We
included PhilPapers because it spans a wide body
of abstract, conceptual discourse, and its articles
contain h... | The Pile- An 800GB Dataset of Diverse Text for Language Modeling |
A Question Answering
We evaluate LLaMA on Natural Questions and TriviaQA. For Natural Questions we use the test split used
for open-domain question answering containing 3610 questions. For TriviaQA we evaluate on the dev set
of the filtered set. This differs from GPT-3 and PaLM, which evaluate on the test set of the unfi... | LLaMA- Open and Efficient Foundation Language Models |
reference implementations, training setups, hyperparameters, or pre-trained models.
• Limitations: Due to financial and compute budgets, Cerebras-GPT models were only trained and evaluated following
the approaches described in this document.
• Out-of-scope uses: Further safety-related testing and mitigations should b... | Cerebras-GPT- Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
Faithful chain-of-thought reasoning. CoRR,
abs/2301.13379, 2023.
[258] Huang, W., P. Abbeel, D. Pathak, et al. Language models as zero-shot planners: Extracting ac-
tionable knowledge for embodied agents. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári,
G. Niu, S. Sabato, eds., International Conference on Machine... | TheRiseandPotentialofLargeLanguageModel BasedAgents |
3
Image
3D Pose
Estimator
⋮
⋮
left shoulder H36M
right shoulder H36M
left shoulder 3DHP
Lpose
Lreconstr
Wenc
Lsparse
Wdec
Lsparse
Final Output
Image
3D Pose
Estimator
Lcons
Wdec
Wenc
Lpose
Frozen
(a) Step 1: Train initial model.
(b) Step 2: Train the autoencoder.
(c) Step 3: Fine-tune the model f... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
Figure 3: Comparison between the three paradigms of RAG
• Task Adaptable Module:
from a
pre-constructed
UPRISE[Cheng et al., 2023a]
for given zero-shot
pool,
Focused on trans-
forming RAG to adapt
to various downstream
tasks,
automati-
cally retrieves prompts
task
inputs
en-
hancing
and models.
PROMPTAGATOR[Dai e... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Online Political Advertising in the United States
127
targeting
One of the biggest benefits of online advertising is the more specific targeting it
enables. Campaigns can choose audiences based on key features (e.g.,
demographics, interests, locations, or behaviors), based on their own lists, or
based on lookalike aud... | Social_Media_and_Democracy |
The Next Level
GPT-3 has generated a lot of discussion on Hacker News. One comment I found particularly
intriguing compares human brain with where we are with the language models: A typical
human brain has over 100 trillion synapses, which is another three orders of magnitudes
larger than the GPT-3 175B model. Given i... | OpenAI's GPT-3 Language Model_ A Technical Overview |
Robust Speech Recognition via Large-Scale Weak Supervision
4
Figure 1. Overview of our approach. A sequence-to-sequence Transformer model is trained on many different speech processing tasks,
including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. Al... | RobustSpeechRecognitionviaLarge-ScaleWeakSupervision |
2.3
Iterative Prompting Mechanism
We introduce an iterative prompting mechanism for self-improvement through three types of feedback:
(1) Environment feedback, which illustrates the intermediate progress of program execution
(Fig. 5, left). For example, “I cannot make an iron chestplate because I need:
7 more iron i... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
15
and PipeDream [74] adopt an approach where a batch is divided into smaller micro-
batches. PMP is then executed independently on each micro-batch, and gradient
updates occur asynchronously across these micro-batches. BPipe [75] aims to achieve
memory balance among accelerators during the training of PMP by transfe... | Beyond Efficiency |
Speech resynthesis is a vital research area with various applications, including speech enhance-
ment and voice conversion, and recent advancements have revolutionized the field by incorporating
self-supervised discrete representations. These techniques enable the generation of high-quality
speech that maintains or deg... | AReviewofDeepLearningTechniquesforSpeechProcessing |
Two figures capture in a qualitative way what I think we has been going on in recent
years, and what we should be going after. The first and most important point of these
figures is simply this: the space of potential AI (and machine learning) models is vast,
and only a tiny bit of what could exist has been explored... | The Next Decade in AI- |
38.5
38.2
37.4
35.3
31.3
32.3
35.3
28.7
52.0
38.5
38.5
13.9
13.9
24.2
26.1
15.1
dates. During fine-tuning inference, we consider the top-5
candidates, and the entire model can be run on a single ma-
chine with a 12GB GPU. We reuse all hyperparameters from
ORQA except we increase the number of training epochs to
4, 60,... | REALM |
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
3
Fig. 1. The evolutionary tree of modern LLMs traces the development of language models in recent years and highlights some of the
most well-known models. Models on the same branch have closer relationships. Transformer-based models are shown i... | Harnessing the Power of LLMs in Practice- A Survey on ChatGPT and Beyond |
Dataset
accidents
ad
baudio
bbc
bnetflix
book
c20ng
cr52
cwebkb
dna
HCLT
Best PSDD Best SPN Dataset
HCLT
Best PSDD Best SPN
-26.74±0.03
-16.07±0.06
-39.77±0.01
-251.04±1.19
-56.27±0.01
-33.83±0.01
-153.40±3.83
-86.26±3.67
-152.77±1.07
-79.05±0.17
-28.29
-16.52
-41.51
-258.96
-58.53
-35.77
-160.43
-92.38
-160.5
-82... | Tractable Regularization of Probabilistic Circuits |
In this section, we explore the aspect of cross-domain transferability.
Specifically, we fine-tune the
BiomedGPT model, pre-trained solely with X-Ray (Radiology) data as described in Section 3.6, using
datasets from other domains such as Microscopy. Additionally, we select MRI-only and CT-only image-
text pairs from SL... | BiomedGPT |
54
PALMS Prompt → When should I propose?
RLHF Response → I think the ideal time to propose is when you feel confident that you have established a strong
relationship with the person, you are financially stable and ready to get married, and you have
the ring and the perfect moment. Basically I would say propose when you... | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
[64] Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and
Josh Tenenbaum. Learning a probabilistic latent space of
object shapes via 3D generative-adversarial modeling. Ad-
vances in Neural Information Processing Systems, 29:82–90,
2016. 2
[65] Yuliang Xiu,
Jinlong Yang, Dimitrios Tzionas,
and
Michael J. Black.... | AG3D- Learning to Generate 3D Avatars from 2D Image Collections |
Yichong Leng, Zehua Chen, Junliang Guo, Haohe Liu,
Jiawei Chen, Xu Tan, Danilo P. Mandic, Lei He, Xiang-
Yang Li, Tao Qin, Sheng Zhao, and Tie-Yan Liu. 2022.
Binauralgrad: A two-stage conditional diffusion prob-
abilistic model for binaural audio synthesis. CoRR,
abs/2205.14807.
Manling Li, Ruochen Xu, Shuohang Wang, ... | Moûsai |
The comparison results are presented in Table 2. We
evaluate a model across the four tasks within the zero-
shot evaluation benchmark:
the text-to-video (T2V) task
on MSR-VTT [71] and UCF 101 [56], frame predic-
tion (FP) on Kinetics 600 (K600) [9], as well as inpaint-
ing/outpainting on Something-Something V2 (SSV2) [... | VideoPoet |
respondents paying very close attention to news. The media diet score achieves a statistically significant lower error and greater R2. Attention
to news is also an significant feature (see Table 1). (C) Correlations based on manually translated prompts (Orig) and automatically-generated
paraphrases of those prompts (SynS... | Language models trained on media diets can predict public opinion |
3.2. Pseudo–Ground Truth Generation
To characterize how the joints of the different skeletons
relate to one another, we need pose labels according to all
skeleton formats for the same examples, to function as a
“Rosetta Stone”. Since no such ground truth is available
(datasets only provide one type of skeletons, rarel... | Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats |
[64] Andrea Petróczi and Eugene Aidman. 2009. Measuring explicit attitude toward doping: Review of the psychometric properties of the
Performance Enhancement Attitude Scale. Psychology of Sport and Exercise 10, 3 (2009), 390–396. https://doi.org/10.1016/j.psychsport.
2008.11.001
[65] Domenico Prattichizzo, Maria Pozzi... | Society’sAttitudesTowardsHumanAugmentation |
1.0T
1.0T
1.4T
1.4T
Table 2: Model sizes, architectures, and optimization hyper-parameters.
Overall, our entire training dataset contains
roughly 1.4T tokens after tokenization. For most of
our training data, each token is used only once dur-
ing training, with the exception of the Wikipedia
and Books domains, over w... | LLaMA- Open and Efficient Foundation Language Models |
interaction. Recent works leverage LLMs as a high-level planner in Minecraft by decomposing
a high-level task into several subgoals following Minecraft recipes [56, 54], thus lacking full
exploration flexibility. Like these latter works, VOYAGER also uses LLMs as a high-level planner by
prompting GPT-4 and utilizes Min... | VOYAGER- An Open-Ended Embodied Agent with Large Language Models |
candidate will extend the state-of-the-art in AI research and have the opportunity to apply this to
real-world UK rail network problems. | informatics-phd-projects-2022-23 |
Task-Oriented Dialogue Systems. arXiv:2008.06239 [cs.CL]
[122] Andrea Madotto, Chien-Sheng Wu, and Pascale Fung. 2018. Mem2Seq: Effectively Incorporating Knowledge Bases
into End-to-End Task-Oriented Dialog Systems. In Proceedings of the 56th Annual Meeting of the Association for
Computational Linguistics (Volume 1: L... | SurveyofHallucinationinNatural Language Generation |
[Agent’s Summary Description]
It is February 13, 2023, 4:56 pm.
John Lin’s status: John is back home early from
work.
Observation: John saw Eddy taking a short walk
around his workplace.
Summary of relevant context from John’s memory:
Eddy Lin is John’s Lin’s son. Eddy Lin has been
working on a music composition for hi... | Generative Agents- Interactive Simulacra of Human Behavior |
2. Straight Through Estimator with weight magnitude criteria (STE). In sparse training
with straight through gradients (Bengio et al., 2013), parameters are projected into a sparse
sub-space before the forward pass. Then gradients are calculated for all parameters and
applied to the original set of dense parameters. ST... | JAXPRUNER |
QLORA reduces the average memory requirements
of finetuning a 65B parameter model from >780GB
of GPU memory to <48GB without degrading the
runtime or predictive performance compared to a 16-
bit fully finetuned baseline. This marks a significant
shift in accessibility of LLM finetuning: now the
largest publicly availab... | QLORA |
Among our select workers, MTurk workers usually accounted for 80-85% of comparison data collected in
a given week, compared to 15-20% for workers hired through Upwork. Although the size of these groups | Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback |
Antagonism Online. Cambridge: Polity Press.
Plantin, J.-C., Lagoze, C., Edwards, P. N., & Sandvig, C. (2016). Infrastructure studies
meet platform studies in the age of Google and Facebook. New Media & Society, 20
(1), 293–310. https://doi.org/10.1177/1461444816661553
Prior, M. (2007). Post-Broadcast Democracy: How M... | Social_Media_and_Democracy |
Precursor to text-conditioned audio synthesis are the text-
conditioned image generation models, which made signifi-
cant progress in quality due to architectural improvements
and the availability of massive, high-quality paired train-
ing data. Prominent Transformer-based autoregressive ap-
proaches include Ramesh et a... | MusicLM |
Helpfulness reward model is eventually trained on all Meta Helpfulness data, combined with an equal
parts of the remaining data uniformly sampled from Meta Safety and from the open-source datasets. The
Meta Safety reward model is trained on all Meta Safety and Anthropic Harmless data, mixed with Meta
Helpfulness and op... | Llama2 |
8 Conclusions
We have explored chain-of-thought prompting as a simple and broadly applicable method for enhanc-
ing reasoning in language models. Through experiments on arithmetic, symbolic, and commonsense
reasoning, we find that chain-of-thought reasoning is an emergent property of model scale that allows
sufficiently... | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
et al., 2021, Borsos et al., 2022a] and optionally text guidance [Bai et al., 2022, Borsos et al., 2022b,
Wang et al., 2023]. Instead of learning an explicit embedding to control style, infilling models
predict speech coherent to the context. In other words, these models perform in-context learning
similar to Large Lan... | Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale |
(2)
where yc is the output of the ControlNet block. In the first
training step, since both the weight and bias parameters of
a zero convolution layer are initialized to zero, both of the
Z(·;·) terms in Equation (2) evaluate to zero, and
yc = y.
(3)
In this way, harmful noise cannot influence the hidden states
of t... | AddingConditionalControltoText-to-ImageDiffusionModels |
Figure 6: Average (±std) test LL over 5
trials on the protein dataset.
9
achieved higher log-likelihood on 18, 19, 10, and 17 datasets compared to EinSumNet, LearnSPN,
ID-SPN, and RAT-SPN, respectively.
5 Conclusions
This paper proposes two model-agnostic distribution regularization techniques: data softening and
... | Tractable Regularization of Probabilistic Circuits |
pieces, candy bars, or other small treats. > Qwen-VL: Flying unicorns, free tickets to Hawaii, apersonal chef for a day, and a lifetime supply ofchocolate. > Qwen-VL+CLoT (Ours): The survey reward is yourown personal information.她忍不住放了个屁，现在尴尬地沉默着，给她一句贴心的话吧。@ She let out a fart involuntarily and iscurrently maintaining ... | Let’sThinkOutsidetheBox |
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