CLR-Bench: Evaluating Large Language Models in College-level Reasoning

TL;DR

CLR-Bench introduces a comprehensive evaluation framework for large language models on complex college-level reasoning tasks, highlighting their limited reasoning capabilities despite high accuracy on final answers.

Contribution

The paper presents a new benchmark with detailed explanations and two novel metrics to better assess LLMs' reasoning and explanation abilities in college-level disciplines.

Findings

LLMs perform poorly on reasoning tasks compared to answer accuracy.

GPT-4 turbo shows a significant drop from 63.31% to 39.00% in reasoning ability.

LLMs tend to guess answers rather than demonstrate true understanding.

Abstract

Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final prediction on multi-choice questions. However, it remains insufficient to verify the essential understanding of LLMs given a chosen choice. To fill this gap, we present CLR-Bench to comprehensively evaluate the LLMs in complex college-level reasoning. Specifically, (i) we prioritize 16 challenging college disciplines in computer science and artificial intelligence. The dataset contains 5 types of questions, while each question is associated with detailed explanations from experts. (ii) To quantify a fair evaluation of LLMs' reasoning ability, we formalize the criteria with two novel metrics. Q$\rightarrow$A is utilized to measure the performance of direct answer prediction, and Q$\rightarrow$AR effectively considers the joint ability to answer the question and provide rationale simultaneously. Extensive experiments are conducted with 40 LLMs over 1,018 discipline-specific questions. The results demonstrate the key insights that LLMs, even the best closed-source LLM, i.e., GPT-4 turbo, tend to `guess' the college-level answers. It shows a dramatic decrease in accuracy from 63.31% Q$\rightarrow$A to 39.00% Q$\rightarrow$AR, indicating an unsatisfactory reasoning ability.