Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
arXiv:2606.20245v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning ability, enabling them to incorporate external information provided in the input prompt. However, the integration of external knowledge can introduce conflicts, not only between the model's internal parametric knowledge and the external information, but also among multiple pieces of external contexts. Existing approaches typically assume that either the model or the pro
The rapid advancement and deployment of Large Language Models (LLMs) are highlighting critical challenges in managing information consistency and reliability, making robust conflict resolution a pressing research area.
Improving LLM inference reliability by addressing knowledge conflicts is crucial for their expanded use in sensitive applications, impacting trust and adoption across industries.
Enhanced methods for conflict resolution will make LLMs more robust and trustworthy, moving them closer to reliable autonomous operation in complex information environments.
- · AI developers
- · Enterprises deploying LLMs
- · Sectors requiring high information accuracy
- · AI agents
- · AI products with poor reliability
- · Legacy knowledge management systems
- · Manual factual verification processes
LLMs become more reliable and less prone to misinformation or hallucination.
Increased adoption of LLMs in critical decision-making and automated workflows across various industries.
The development of highly autonomous AI agents that can consistently reason with conflicting information, reshaping white-collar work and SaaS layers.
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Read at arXiv cs.AI