Over-Reasoning and Redundant Calculation of Large Language Models

Anonymous

Abstract:

Large language models (LLMs) can solve problems step-by-step.
While this chain-of-thought (CoT) reasoning boosts LLMs' performance, it is unclear if LLMs know when to use CoT and whether those CoT are always necessary to answer the question.
This paper shows that LLMs tend to generate redundant calculations and reasoning on a manually constructed math QA dataset, GSM8K-Zero.
GSM8K-Zero is constructed such that the questions can be answered without any calculations, but LLMs, including Llama-2 models and Claude-2, tend to generate lengthy and unnecessary calculations to answer the questions.
We also conduct experiments to explain why LLMs generate redundant calculations and reasonings.

Paper Type: short

Research Area: Interpretability and Analysis of Models for NLP

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

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