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1,136 claims from 715 papers are on the record. 39 have been checked so far; the other 1,097 have no check with a result yet.

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Topic: Topic Modeling ×Clear all

44 claims from 26 papers, showing 1–20 of 26

1. Computer Science › Topic Modeling

   Deep Contextualized Word Representations

   Peters, Neumann, Iyyer et al. · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · 2018

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   1. Unchecked“We also present an analysis showing that exposing the deep internals of the pre-trained network is crucial, allowing downstream models to mix different types of semi-supervision signals.”

2. Computer Science › Topic Modeling

   BNAI, NO-TOKEN, and MIND-UNITY: Pillars of a Systemic Revolution in Artificial Intelligence

   Jason, Xuezhi, Schuurmans et al. · arXiv (Cornell University) · 2022

   The paper shows that prompting large language models with examples of step-by-step reasoning improves their performance on complex reasoning tasks, with some striking gains.

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   1. UncheckedIn experiments on three large language models, chain of thought prompting improved performance on arithmetic, commonsense and symbolic reasoning tasks.“Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks.”
   2. Unchecked“For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier.”

3. Computer Science › Topic Modeling

   BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

   Lewis, Liu, Goyal et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 2020

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   1. Unchecked“It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.”
   2. Unchecked“BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining.”

4. Computer Science › Topic Modeling

   Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

   池谷, Tinn, Cheng et al. · ACM Transactions on Computing for Healthcare · 2021

   The paper compiles a biomedical NLP benchmark and reports that language models pretrained from scratch on biomedical text reach new state-of-the-art results across a wide range of tasks.

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   1. UncheckedFor fields with plenty of unlabelled text, like biomedicine, training language models from scratch gave substantial gains over adapting general-domain models.“In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models.”
   2. Unchecked“Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition.”

5. Computer Science › Topic Modeling

   PaLM: Scaling Language Modeling with Pathways

   Chowdhery, Narang, Devlin et al. · arXiv (Cornell University) · 2022

   The authors trained PaLM, a 540-billion parameter language model, and report state-of-the-art few-shot results on hundreds of benchmarks, plus analyses of bias, toxicity and memorisation.

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   1. UncheckedMany BIG-bench tasks showed sudden, steep gains in performance when the model reached the largest size the authors trained, PaLM 540B.“A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model.”
   2. UncheckedThe authors report that scaling a language model up to 540 billion parameters gave state-of-the-art few-shot results on hundreds of benchmarks.“We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks.”
   3. Unchecked“On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.”

6. Computer Science › Topic Modeling

   LLaMA: Open and Efficient Foundation Language Models

   Touvron, Lavril, Izacard et al. · arXiv (Cornell University) · 2023

   The paper introduces LLaMA, language models of 7B to 65B parameters trained on public data, which it reports match or beat larger models on most benchmarks, and releases them to researchers.

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   1. UncheckedThe authors say state-of-the-art language models can be trained using only publicly available data, with no proprietary or inaccessible datasets.“We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.”
   2. UncheckedThe paper reports that LLaMA-13B beats the much larger GPT-3 on most benchmarks, and LLaMA-65B is competitive with Chinchilla-70B and PaLM-540B.“In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B.”

7. Computer Science › Topic Modeling

   Sparks of Artificial General Intelligence: Early experiments with GPT-4

   Bubeck, Chandrasekaran, Eldan et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting.”

8. Computer Science › Topic Modeling

   A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development

   Wu, He, Liu et al. · IEEE/CAA Journal of Automatica Sinica · 2023

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   1. Unchecked“Specifically, from the limited open-accessed resources, we conclude the core techniques of ChatGPT, mainly including large-scale language models, in-context learning, reinforcement learning from human feedback and the key technical steps for developing Chat-…

9. Computer Science › Topic Modeling

   Scaling Instruction-Finetuned Language Models

   Chung, Le Hou, Longpre et al. · arXiv (Cornell University) · 2022

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   1. Unchecked“For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average).”
   2. Unchecked“Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU.”
   3. Unchecked“We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generat…

10. Computer Science › Topic Modeling

    Emergent Abilities of Large Language Models

    Jason, Tay, Bommasani et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“Thus, emergent abilities cannot be predicted simply by extrapolating the performance of smaller models.”

11. Computer Science › Topic Modeling

    Structured information extraction from scientific text with large language models

    Dagdelen, Dunn, Lee et al. · Nature Communications · 2024

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    1. Unchecked“This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.”

12. Computer Science › Topic Modeling

    Self-Consistency Improves Chain of Thought Reasoning in Language Models

    Wang, Jason, Schuurmans et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (…

13. Computer Science › Topic Modeling

    Training Compute-Optimal Large Language Models

    Hoffmann, Borgeaud, Mensch et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“By training over 400 language models ranging from 70 million to over 16 billion parameters on 5 to 500 billion tokens, we find that for compute-optimal training, the model size and the number of training tokens should be scaled equally: for every doubling of…
    2. Unchecked“Chinchilla uniformly and significantly outperforms Gopher (280B), GPT-3 (175B), Jurassic-1 (178B), and Megatron-Turing NLG (530B) on a large range of downstream evaluation tasks.”
    3. Unchecked“As a highlight, Chinchilla reaches a state-of-the-art average accuracy of 67.5% on the MMLU benchmark, greater than a 7% improvement over Gopher.”

14. Computer Science › Topic Modeling

    The Pile: An 800GB Dataset of Diverse Text for Language Modeling

    Gao, Biderman, Black et al. · arXiv (Cornell University) · 2020

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    1. Unchecked“Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing.”
    2. Unchecked“Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations.”

15. Computer Science › Topic Modeling

    Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

    Smith, Patwary, Norick et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning accuracies on several NLP benchmarks and establishes new state-of-the-art results.”

16. Computer Science › Topic Modeling

    A Survey on Evaluation of Large Language Models

    Chang, Xu, Wang et al. · arXiv (Cornell University) · 2023

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    1. Unchecked“Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs.”

17. Computer Science › Topic Modeling

    Holistic Evaluation of Language Models

    Liang, Bommasani, Lee et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“We improve this to 96.0%: now all 30 models have been densely benchmarked on the same core scenarios and metrics under standardized conditions.”
    2. Unchecked“Prior to HELM, models on average were evaluated on just 17.9% of the core HELM scenarios, with some prominent models not sharing a single scenario in common.”

18. Computer Science › Topic Modeling

    Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

    Süzgün, Nathan, Schärli et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“We find that applying chain-of-thought (CoT) prompting to BBH tasks enables PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex (code-davinci-002) to surpass the average human-rater performance on 17 of the 23 tasks.”

19. Computer Science › Topic Modeling

    Crosslingual Generalization through Multitask Finetuning

    Muennighoff, Thomas, Sutawika et al. · arXiv (Cornell University) · 2022

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    1. Unchecked“We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus.”
    2. Unchecked“Finetuning on multilingual tasks with English prompts further improves performance on English and non-English tasks leading to various state-of-the-art zero-shot results.”
    3. Unchecked“Surprisingly, we find models are capable of zero-shot generalization to tasks in languages they have never intentionally seen.”

20. Computer Science › Topic Modeling

    RWKV: Reinventing RNNs for the Transformer Era

    Peng, Alcaide, Anthony et al. · arXiv (Cornell University) · 2023

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    1. Unchecked“Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference.”