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1,213 claims from 764 papers are on the record. 45 have been checked so far; the other 1,168 have no check with a result yet.

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Status: Unchecked ×Keyword: few-shot prompting ×Clear all

6 claims from 4 papers

1. Computer Science › Topic Modeling

   Scaling Instruction-Finetuned Language Models

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

   The paper studies instruction finetuning of language models, scaling the number of tasks, model size and chain-of-thought data, and reports large gains across several model families and benchmarks.

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   1. UncheckedFlan-PaLM 540B, instruction-finetuned on 1.8K tasks, scores 9.4% higher on average than the original PaLM 540B.“For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average).”
   2. UncheckedFlan-PaLM 540B reaches state-of-the-art results on several benchmarks, including 75.2% on the five-shot MMLU test.“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…

2. 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.”

3. Computer Science › Spam and Phishing Detection

   Prompting Large Language Models for Malicious Webpage Detection

   Li and Gong · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · 2023

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   1. Unchecked“Experimental results show that our proposed approach achieves comparable or even better performance than deep learning baselines.”

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.”