From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
arXiv:2606.10298v1 Announce Type: cross Abstract: When large language models generate from retrieved or augmented contexts, conflicts between external context and parametric priors remain a central reliability bottleneck. Existing contrastive decoding methods follow a \emph{context-aware} paradigm that unilaterally amplifies context over parametric priors, overwriting correct priors when the context is erroneous. We generalize this to the \textbf{conflict-aware} paradigm that dynamically allocates authority between prior and context based on conflict signals, rather than presupposing context t
The increasing reliance on retrieved or augmented contexts in LLMs and the inherent challenges in managing knowledge conflicts make this research timely.
This development addresses a critical reliability bottleneck in LLMs, specifically how they handle conflicting information from internal knowledge and external sources, impacting their trustworthiness and applicability.
The paradigm shifts from unilaterally amplifying context to a more nuanced, dynamic allocation of authority between LLM's parametric priors and external context based on conflict signals.
- · AI developers
- · LLM users (e.g., enterprises)
- · Knowledge retrieval systems
- · LLM systems with poor conflict resolution
- · Simple context-aware methods
LLMs can achieve higher accuracy and reduce 'hallucinations' or misinformations stemming from context-prior conflicts.
This improved reliability could accelerate the adoption of LLMs in critical applications where accuracy is paramount.
Enhanced trust in LLM outputs may lead to new benchmarks and evaluation metrics for AI reliability, fostering further innovation in robust AI.
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Read at arXiv cs.CL