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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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10 claims from 5 papers

1. 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 scaling, bias, toxicity and memorisation.

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   1. 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.”
   2. 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.”
   3. UncheckedOn some tasks, the 540-billion-parameter PaLM model beat the finetuned state of the art on multi-step reasoning and average human performance on BIG-bench.“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.”

2. Medicine › Artificial Intelligence in Healthcare and Education

   Large Language Models Encode Clinical Knowledge

   Singhal, Azizi, Tao et al. · arXiv (Cornell University) · 2022

   The paper introduces the MultiMedQA benchmark and a human evaluation framework, tests PaLM and Flan-PaLM, and presents Med-PaLM, which performs encouragingly but remains inferior to clinicians.

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   1. UncheckedFlan-PaLM, using combined prompting strategies, reports top accuracy on all MultiMedQA multiple-choice sets, including 67.6% on MedQA, over 17% above prior best.“Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17…”
   2. UncheckedMed-PaLM, a language model tuned for medicine, performs encouragingly on medical questions but is still worse than clinicians, according to the paper.“The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians.”
   3. UncheckedThe paper reports that comprehension, knowledge recall and medical reasoning in language models improve with model size and instruction prompt tuning.“We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine.”

3. 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.”

4. Computer Science › Topic Modeling

   Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

   Hsieh, Li, Yeh et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes.”

5. Computer Science › Topic Modeling

   Transcending Scaling Laws with 0.1% Extra Compute

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

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   1. Unchecked“Impressively, at 540B scale, we show an approximately 2x computational savings rate where U-PaLM achieves the same performance as the final PaLM 540B model at around half its computational budget (i.e., saving $\sim$4.4 million TPUv4 hours).”
   2. Unchecked“Overall, we show that U-PaLM outperforms PaLM on many few-shot setups, i.e., English NLP tasks (e.g., commonsense reasoning, question answering), reasoning tasks with chain-of-thought (e.g., GSM8K), multilingual tasks (MGSM, TydiQA), MMLU and challenging BIG…