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Knowledge Conflicts for LLMs: A Survey

Summary

This survey examines a recurring problem in large language models: the knowledge stored in a model's weights can disagree with information supplied at inference time, and supplied sources can disagree with each other. The authors sort these clashes into three classes. Context-memory conflict pits prompts, dialogue or retrieved documents against parametric knowledge. Inter-context conflict arises among the contextual sources themselves. Intra-memory conflict appears when the model answers differently to rephrased versions of the same question. For each class they trace the origins, such as outdated training data, injected misinformation and inconsistencies in pre-training corpora. They then review how models actually behave and group mitigation methods by intended behaviour and by timing: pre-hoc (before a conflict arises) or post-hoc (afterwards). The paper runs no new experiments. Instead, it compiles results and datasets from literature published mostly after 2021. The work matters because retrieval-augmented systems are spreading, and unresolved conflicts threaten the reliability of LLMs in legal, medical, financial and educational settings.

Key points

  • Two causes of context-memory conflict:
    • Temporal misalignment. Here the newer context is assumed correct. It is usually addressed with knowledge editing (KE), RAG or continual learning (CL), each with known drawbacks.
    • Misinformation pollution. Here the context is assumed wrong. Prompt injection and sycophancy make it worse.
  • Mixed evidence on model behaviour:
    • Longpre et al. (2021) used entity substitution in ODQA and found that models over-rely on memory.
    • Chen et al. (2022), using multiple evidence passages, found that models mostly follow the context.
    • Overall, LLMs tend to accept persuasive, semantically coherent context but also show confirmation bias toward their stored knowledge.
  • Solution objectives include:
    • staying faithful to the context;
    • detecting misinformation and falling back on parametric knowledge;
    • disentangling the different sources.
  • Open directions named by the authors:
    • evaluation on downstream tasks;
    • the interplay among conflict types;
    • explainability (e.g., memory vs. context attention heads, Jin et al. 2024b);
    • non-English and multilingual settings (GPT-4, GLM);
    • multimodal conflicts.
  • Resources: a dataset overview (Table 1) and a companion repository at github.com/pillowsofwind/Knowledge-Conflicts-Survey. The paper is published in EMNLP 2024 Proceedings, pp. 8541–8565.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2024.emnlp-main.486

@inproceedings{xu2024knowledge,
  title = {{Knowledge Conflicts for LLMs: A Survey}},
  author = {Xu, Rongwu and Qi, Zehan and Guo, Zhijiang and Wang, Cunxiang and Wang, Hongru and Zhang, Yue and Xu, Wei},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.486},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.486}
}

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