Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt

TL;DR

This paper introduces a prompt optimization method that explicitly disambiguates semantic ambiguities in user prompts using small language models, improving reasoning accuracy with minimal cost.

Contribution

It proposes a novel pre-inference prompt disambiguation approach utilizing small language models to enhance LLM reasoning performance by resolving semantic ambiguities.

Findings

Improves reasoning performance by 2.5 points on multiple benchmarks.

Achieves this with a cost of only $0.02 per inference.

Promotes explicit prompt disambiguation as an effective optimization technique.

Abstract

Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. However, the model's performance is highly dependent on the open-ended characteristics of the users' input prompt. Natural prompts often do not follow proper syntactic rules, which creates ambiguous queries that yield multiple interpretations. Such ambiguous prompts confuse the model in choosing the correct reasoning paths to answer questions. Prior works address this challenge by applying query editing during the LLM inference process without explicitly solving the root cause of the ambiguity. To address this limitation, we propose a pre-inference prompt optimization mechanism via explicit prompt disambiguation. Particularly, we identify semantic risks in the prompt, check their multi-perspective consistency, and resolve any semantic conflicts that arise. Finally, we organize the resolved ambiguities in a logically structured manner as a clean input to the LLM. By explicitly resolving semantic ambiguity, our method can produce a more focused attention distribution to the semantically essential tokens. We also leverage small language models (SLMs) as the main executor of prompt disambiguation to benefit from their efficient computation. Through comprehensive experiments on multiple benchmarks, we demonstrate that our method improves reasoning performance by 2.5 points at a cost of only \$0.02. Our study promotes explicit prompt disambiguation as an effective prompt optimization method without disturbing the internal mechanism of LLM inference.