MR-BEN: A Comprehensive Meta-Reasoning Benchmark for Large Language Models
- URL: http://arxiv.org/abs/2406.13975v1
- Date: Thu, 20 Jun 2024 03:50:23 GMT
- Title: MR-BEN: A Comprehensive Meta-Reasoning Benchmark for Large Language Models
- Authors: Zhongshen Zeng, Yinhong Liu, Yingjia Wan, Jingyao Li, Pengguang Chen, Jianbo Dai, Yuxuan Yao, Rongwu Xu, Zehan Qi, Wanru Zhao, Linling Shen, Jianqiao Lu, Haochen Tan, Yukang Chen, Hao Zhang, Zhan Shi, Bailin Wang, Zhijiang Guo, Jiaya Jia,
- Abstract summary: Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.
We present a process-based benchmark that demands a meta reasoning skill.
MR-BEN is a comprehensive benchmark comprising 5,975 questions collected from human experts.
- Score: 55.20845457594977
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. However, it has been increasingly challenging to evaluate the reasoning capability of LLMs. Concretely, existing outcome-based benchmarks begin to saturate and become less sufficient to monitor the progress. To this end, we present a process-based benchmark MR-BEN that demands a meta reasoning skill, where LMs are asked to locate and analyse potential errors in automatically generated reasoning steps. MR-BEN is a comprehensive benchmark comprising 5,975 questions collected from human experts, covering various subjects such as physics, chemistry, logic, coding, and more. Through our designed metrics for assessing meta-reasoning on this benchmark, we identify interesting limitations and weaknesses of current LLMs (open-source and closed-source models). For example, open-source models are seemingly comparable to GPT-4 on outcome-based benchmarks, but they lag far behind on our benchmark, revealing the underlying reasoning capability gap between them. Our dataset and codes are available on https://randolph-zeng.github.io/Mr-Ben.github.io/.
Related papers
- A Critical Review of Causal Reasoning Benchmarks for Large Language Models [2.1311710788645617]
We present a comprehensive overview of LLM benchmarks for causality.
We derive a set of criteria that a useful benchmark or set of benchmarks should aim to satisfy.
arXiv Detail & Related papers (2024-07-10T20:11:51Z) - Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM
Evaluation [51.99752147380505]
This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs)
We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence.
Our framework widens performance discrepancies both between different models and within the same model across various tasks.
arXiv Detail & Related papers (2024-02-18T03:40:06Z) - NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language
Models via Complexity Classes [32.154637177467684]
NPHardEval is designed to evaluate the reasoning abilities of Large Language Models (LLMs) across a broad spectrum of 900 questions.
It is meticulously chosen to represent a wide range of complexity class below the NP-hard complexity class.
It is designed with a dynamic update mechanism, where the datapoints are refreshed on a monthly basis.
arXiv Detail & Related papers (2023-12-22T18:07:44Z) - CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark.
In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship.
We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
arXiv Detail & Related papers (2023-11-29T08:29:54Z) - InfiMM-Eval: Complex Open-Ended Reasoning Evaluation For Multi-Modal
Large Language Models [50.03163753638256]
Multi-modal Large Language Models (MLLMs) are increasingly prominent in the field of artificial intelligence.
Our benchmark comprises three key reasoning categories: deductive, abductive, and analogical reasoning.
We evaluate a selection of representative MLLMs using this rigorously developed open-ended multi-step elaborate reasoning benchmark.
arXiv Detail & Related papers (2023-11-20T07:06:31Z) - Investigating Data Contamination in Modern Benchmarks for Large Language Models [27.479260572913724]
Recent observations have underscored a disparity between the inflated benchmark scores and the actual performance of LLMs.
We study data contamination by proposing two methods tailored for both open-source and proprietary LLMs.
We find that certain commercial LLMs could surprisingly guess the missing option in various test sets.
arXiv Detail & Related papers (2023-11-16T11:03:04Z) - FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models [79.62191017182518]
FollowBench is a benchmark for Fine-grained Constraints Following Benchmark for Large Language Models.
We introduce a Multi-level mechanism that incrementally adds a single constraint to the initial instruction at each increased level.
By evaluating 13 popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work.
arXiv Detail & Related papers (2023-10-31T12:32:38Z) - LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Most LLMs struggle on SummEdits, with performance close to random chance.
The best-performing model, GPT-4, is still 8% below estimated human performance.
arXiv Detail & Related papers (2023-05-23T21:50:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.