Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models

arXiv:2403.13590v1 Announce Type: new
Abstract: Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks. One example is permutation sensitivity, where LLMs' outputs may significantly vary depending on the order of the input options. While debiasing techniques can mitigate these issues, and yield better performance and reliability, they often come with a high computational cost at inference. This paper addresses this inefficiency at inference time. …

abstract arxiv capabilities cs.cl example general however language language models large language large language models llms nlp sensitivity specific tasks tasks training type zero-shot