Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
arxiv(2023)
摘要
Most existing retrieval-augmented language models (LMs) assume a naive
dichotomy within a retrieved document set: query-relevance and irrelevance. Our
work investigates a more challenging scenario in which even the "relevant"
documents may contain misleading or incorrect information, causing conflict
among the retrieved documents and thereby negatively influencing model
decisions as noise. We observe that existing LMs are highly brittle to the
presence of conflicting information in both the fine-tuning and in-context
few-shot learning scenarios. We propose approaches for handling knowledge
conflicts among retrieved documents by explicitly fine-tuning a discriminator
or prompting GPT-3.5 to elicit its discriminative capability. Our empirical
results on open-domain QA show that these approaches significantly enhance
model robustness. We also provide our findings on incorporating the fine-tuned
discriminator's decision into the in-context learning process, proposing a way
to exploit the benefits of two disparate learning schemes. Alongside our
findings, we provide MacNoise, a machine-generated, conflict-induced dataset to
further encourage research in this direction.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要