Do LLMs Make More Mistakes If They Do Not Believe the Input Data?

2026-09-08

AI Summary

研究探讨了大型语言模型在面对不同可信度输入时的准确性问题，通过使用多种语言和本地知识数据生成文本的方法来分析模型对事实、反事实和虚构信息的处理情况。

Abstract

Large language models (LLMs) are prone to hallucinating or misinterpreting facts, which impairs their usability in retrieval-augmented generation or data-to-text systems. We analyse how faithfulness of LLMs to provided context depends on how plausible they perceive the context to be (context-memory conflict). To better identify error patterns, we make use of the increased difficulty of non-English and low-resource language text generation and input data based on local knowledge, only partially captured in models' parametric knowledge. We let the models generate text in English, Czech, Slovak and Upper Sorbian from factual (FA), counterfactual (CFA) and fictional (FI) RDF triples containing local Czech and Slovak data. Contrary to our expectations, we observe only a weak context-memory conflict on the human-annotated sample. For Kimi K3 as an LLM judge, which agrees well with human annotations on the sample, counterfactual inputs receive only slightly lower faithfulness scores than factual ones (-0.05 on a 1-5 scale). We also find that a suboptimal choice of LLM judge would lead to overestimating the strength of the context-memory conflict.

Problem

• Large language models
• hallucinating
• misinterpreting facts
• context-memory conflict
• faithfulness

Innovation

• context-memory conflict
• faithfulness
• low-resource languages
• local knowledge
• LLM judge