BBH Dataset

BIG-Bench Hard (BBH) is a subset of the BIG-Bench, a diverse evaluation suite for language models. BBH focuses on a suite of 23 challenging tasks from BIG-Bench that were found to be beyond the capabilities of current language models. These tasks are ones where prior language model evaluations did not outperform the average human-rater. The BBH tasks require multi-step reasoning, and it was found that few-shot prompting without Chain-of-Thought (CoT), as done in the BIG-Bench evaluations, substantially underestimates the best performance and capabilities of language models. When CoT prompting was applied to BBH tasks, it enabled PaLM to surpass the average human-rater performance on 10 of the 23 tasks, and Codex to surpass the average human-rater performance on 17 of the 23 tasks.BIG-Bench Hard (BBH) 是 BIG-Bench 的一个子集，BIG-Bench 是一个针对语言模型的多样化评估套件。BBH 专注于 BIG-Bench 中的23个具有挑战性的任务，这些任务被发现超出了当前语言模型的能力范围。这些任务是在先前的语言模型评估中，未达到平均人类评分者水平的工作。BBH 任务需要多步骤推理，研究发现，在 BIG-Bench 评估中采用的少样本提示（无思维链提示）显著低估了语言模型的最佳性能和能力。当在 BBH 任务中应用思维链提示时，它使得 PaLM 在23个任务中的10个任务上超越了平均人类评分者的表现，而 Codex 在23个任务中的17个任务上也超越了平均人类评分者的表现。

搜集汇总

背景与挑战

背景概述

BBH（BIG-Bench Hard）是BIG-Bench中23个高难度任务的子集，专注于评估语言模型的多步推理能力。该数据集揭示了传统少样本提示会低估模型性能，而链式思维（CoT）提示能显著提升模型表现，使PaLM和Codex在多数任务上超越人类平均评分。