Key Points

1. The survey explores knowledge conflicts for large language models (LLMs) and categorizes them into three types: context-memory conflict, inter-context conflict, and intra-memory conflict.

2. Large language models (LLMs) are renowned for encapsulating vast parametric knowledge and continue to engage with external contextual knowledge after deployment.

3. Context-memory conflicts arise when user prompts, dialogue history, and retrieved documents from the web conflict with the parametric knowledge within LLMs.

4. Inter-context conflicts stem from conflicts among various pieces of contextual knowledge and can complicate LLMs' ability to process and respond accurately.

5. LLMs exhibit both adherence to parametric knowledge and susceptibility to contextual influences, which can be problematic when the external knowledge is factually incorrect.

6. Intra-memory conflicts within LLMs stem from training corpus bias, decoding strategies, and knowledge editing, leading to inconsistent responses to semantically identical queries.

7. The survey highlights the need for fine-grained solutions to address knowledge conflicts, considering factors such as user query nature, sources of conflicting information, and user expectations.

8. The researchers propose exploring the effects of knowledge conflicts on a wider range of applications beyond QA problems to understand their implications on downstream tasks.

9. Areas for future research include evaluating LLMs' performance in real-world scenarios, developing solutions at a finer resolution, exploring the interplay among different types of conflicts, and focusing on explainability, multilinguality, and multimodality.

Summary

The survey paper provides a comprehensive analysis of knowledge conflicts for large language models (LLMs), categorizing them into three main types: context-memory, inter-context, and intra-memory conflicts. The authors highlight the complex challenges LLMs encounter when combining contextual and parametric knowledge and explore the impact of these conflicts on the trustworthiness and performance of LLMs, especially in real-world applications where noise and misinformation are common. The paper also reviews available solutions for improving the robustness of LLMs in the face of these conflicts.

Large language models (LLMs) excel in knowledge-intensive tasks such as question answering, fact-checking, and knowledge generation. However, they face significant challenges when integrating external contextual knowledge with their parametric knowledge. The survey categorizes knowledge conflicts and explores the causes and behaviors of LLMs under these conflicts, shedding light on strategies for improving their robustness. The authors highlight the impact of knowledge conflicts on the trustworthiness and performance of LLMs, especially in real-world applications, where noise and misinformation are common.

Types of Knowledge Conflicts:

Three categories of knowledge conflicts are addressed:

1. Context-memory conflict: This conflict arises when contextual knowledge conflicts with parametric knowledge within LLMs. The discrepancies between these types of knowledge sources can significantly impact the trustworthiness and real-time accuracy of LLMs, posing challenges in handling noise and misinformation in external documents.

Inter-context Conflict
2. Inter-context conflict: This conflict occurs when LLMs integrate external documents from the web, resulting in inconsistencies within the provided context. Misinformation and outdated information in the retrieved documents can challenge the accuracy of LLMs in responding to queries and real-world applications.

3. Intra-memory conflict: LLMs exhibit unpredictable behaviors and generate differing responses to inputs that are semantically equivalent but syntactically distinct. This inconsistency undermines the reliability and utility of LLMs.

Solutions and Future Research Directions
The survey also discusses available solutions for addressing knowledge conflicts in LLMs, such as fine-tuning, knowledge plug-in, and output ensemble strategies to mitigate the impact of knowledge conflicts on LLMs' performance and trustworthiness. Additionally, future directions for research include addressing knowledge conflicts in practical applications, exploring solutions at a finer resolution, evaluating downstream task impacts, and studying the interplay among different types of conflicts. The authors also point to the need for explainable AI and the importance of evaluating knowledge conflicts in multimodal and multilingual contexts.

Overall, the survey provides a detailed and insightful analysis of the complexities, impacts, and potential solutions for knowledge conflicts in large language models, shedding light on the challenges and opportunities in this evolving area of research.

Reference: https://arxiv.org/abs/2403.083...