Decision models classify conflicts in RAG sources
Original title: Do We Need an LLM to Know Why Sources Conflict? Decision Models for Knowledge-Conflict Typing in RAG
Typed decision models classify knowledge conflicts in RAG faster and cheaper than LLMs while preserving response behavior.
Decision models return probabilities over conflict types in a single forward pass for retrieval-augmented generation sources. Using the CONFLICTS benchmark, 10 of 11 models match or exceed Gemini 2.5 Flash accuracy at 4-39x lower cost. Cascading the least confident queries to Gemini yields equivalent end-to-end performance at 25-37% of its cost. Decomposition into yes/no questions lowers accuracy for all models.
- Reaches 50.0--67.0% accuracy directly
- Matches Gemini 2.5 Flash at lower cost
- Cascade tied with Gemini at 25-37% cost
- Dates play no measurable role
From the original article
1 min read at huggingface.co
When the sources retrieved for a query disagree, the right response depends on why they disagree: a question with several valid answers, a contested topic, stale information and a false source each call for different behaviour. Cattan et al. (2025) showed that telling a generator the conflict type improves its responses by about 24 points, but that predicting the type is hard even for frontier LLMs.
By Parv Khatri, Harshul Jain, Mehak Kapoor · huggingface.co
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