Year: 2025
Type: conference-paper
Abstract: Large language models (LLMs) have exhibited impressive zero-shot performance on inference tasks.However, LLMs may suffer from spurious correlations between input texts and output labels, which limits LLMs' ability to reason based purely on general language understanding.For example, in the natural language inference (NLI) task, LLMs may make predictions primarily based on premise or hypothesis, rather than both components.To address this problem that may lead to unexpected performance degradation, we propose task calibration (TC), a zero-shot and inference-only calibration method inspired by mutual information which recovers LLM performance through task reformulation.In NLI, TC encourages LLMs to reason based on both premise and hypothesis, while mitigating the models' overreliance on individual premise or hypothesis for inference.Experimental results show that TC achieves a substantial improvement on 13 different benchmarks in the zero-shot setup.We further validate the effectiveness of TC in fewshot setups and various natural language understanding tasks.Further analysis indicates that TC is also robust to prompt templates and has the potential to be integrated with other calibration methods.We publicly release our code to facilitate future research 1 .
Authors: Yingjie Li, Yun Luo, Xiaotian Xie, Yue Zhang
Institutions: Westlake University, Central South University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412888524