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Test the full question-to-answer workflow with cases where retrieved documents disagree. Score retrieval separately from the assistant’s response: did the system retrieve the competing evidence, represent the disagreement accurately, attribute claims to sources, and admit what remains unresolved? That separation tells you whether a failure began in search or in the answer built from the evidence.
What should a good conflict test measure?
A reliable evaluation checks more than whether an answer matches one reference sentence. When sources conflict, there may be no single answer the evidence supports. Assess whether the assistant finds the relevant passages, recognizes the disagreement, applies an appropriate source rule when one exists, and communicates uncertainty instead of silently choosing a side.
Keep component results visible rather than collapsing them into one score. A polished answer cannot compensate for a retriever that omitted the passage that contradicts it.
How do you build a useful test set?
Define the expected behavior first
For each question, record the passages needed to answer it, each passage’s source identity, the propositions that conflict, and the response you consider acceptable. Decide in advance whether the assistant should prefer a more authoritative or newer source, present both positions, ask the user to clarify the scope, or say the available evidence does not settle the matter. The right response depends on the cause of the conflict; Google Research’s work on knowledge conflicts likewise distinguishes conflict categories and corresponding desired behaviors.
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Use source rules that make sense for the application. For example, Microsoft Learn’s Azure RAG prompt guidance illustrates preferring official documentation over community forum posts. Treat that as a rule for the illustrated knowledge base, not a universal ranking for every subject: a domain may require different evidence standards.
Include different kinds of disagreement
- Direct contradiction: two passages state incompatible values, dates, or outcomes.
- Implicit contradiction: statements seem compatible until you compare their scope, definitions, or dates. WikiContradict reports particular difficulty with these cases.
- Different source credibility: sources disagree and the application has a defensible reason to trust one more. CONFACT research examines how source credibility affects conflict-focused retrieval and generation.
- Same-source or equal-trust disagreement: test whether the assistant can report the conflict when source ranking cannot resolve it. WikiContradict includes same-source and equal-trust cases.
- Retrieved evidence versus model prior: check both whether the assistant adopts misleading retrieved content and whether it ignores good evidence that corrects its prior answer. ClashEval is designed to probe this tension.
- Insufficient evidence: include cases where the documents do not establish a tie-breaker, so the appropriate response is to say that the evidence is inconclusive rather than invent one.
Make each case auditable
Label sources in the context supplied to the assistant and tell it to attribute material claims, follow the stated priority rule, and flag missing or unresolved evidence. Preserve the exact passages used in each run. A test case should let a reviewer see both what the model could have retrieved and what it actually received.
How should you score retrieval and answers?
Score the retrieval stage using the evidence needed for the question, then score the response against that retrieved context and your expected behavior.
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| Dimension | What to check | Relevant published approach |
|---|---|---|
| Retrieval relevance or recall | Did retrieval return the evidence needed to answer, including the passage that creates the conflict? | NVIDIA’s RAG evaluation documentation describes context recall at top-k cutoffs; TREC RAG has a retrieval task. |
| Answer accuracy | Does the answer match the expected response, including a fair account of the conflict when no single answer is warranted? | NVIDIA documents answer accuracy against reference ground truth. |
| Groundedness | Can each material claim be supported by the retrieved context? | NVIDIA defines response groundedness in terms of support from retrieved contexts. |
| Conflict identification and coverage | Does the answer surface the competing positions and cover the relevant arguments rather than flattening them into one claim? | ConfRAG proposes answer clustering, answer coverage, and reason coverage. |
| Attribution and source priority | Does the assistant show which source supports which claim and follow the priority rule you supplied? | Microsoft’s prompt guidance recommends labeled sources and explicit priority rules. |
| Uncertainty or abstention | Does the response identify unresolved or missing evidence rather than asserting unsupported certainty? | Microsoft’s RAG guidance calls for guardrails around missing or conflicting information. |
Use the score to locate the failure
If the contradictory passage never appears in the retrieved context, investigate retrieval before judging the answer-generation stage. If both passages are present but one is omitted, misattributed, or treated as settled without justification, the answer stage or its instructions need attention. Amazon Bedrock documents retrieve-only and retrieve-and-generate evaluation jobs; TREC RAG also separates retrieval and retrieval-augmented generation tasks.
For a small pilot, manually label a set of realistic conflicts from the target corpus and expand it after reviewing how consistently the rubric distinguishes acceptable answers from failures. Keep a human-reviewed subset: automated scoring can help scale evaluation, but subtle or implicit disagreements can be difficult to judge reliably without inspecting the evidence. WikiContradict reports both human evaluation and a separate automated estimator.
How can you make runs reproducible?
- Freeze the cases: keep questions, source passages, labels, expected behavior, and any authority rules constant across systems or versions.
- Save the retrieval trace: record which passages were returned and their source labels, so retrieval omissions can be distinguished from answer errors.
- Record system settings: retain the prompt version, model and configuration details, and any relevant hyperparameters. Microsoft recommends documenting prompt text, evaluation results across the test set, changes, and the reasons for them.
- Store outputs and component scores: save the answer alongside retrieval results and each metric, rather than keeping only an aggregate score.
- Record evaluation scope: note the benchmark version and relevant language or geography. A result from one domain or locale is not automatically representative of another.
What do published benchmarks tell you?
These figures describe specific datasets and evaluation conditions. They are not estimates of how often deployed assistants fail across real-world use.
- ConfRAG (Association for Computational Linguistics, 2026): the dataset contains 1,814 real-world questions, each paired with an average of 9.58 retrieved paragraphs from heterogeneous online sources. In that dataset, 57.2% of questions contain explicit contradictions. The work proposes answer clustering, answer coverage, and reason coverage for evaluating responses.
- ClashEval (NeurIPS, 2024): the benchmark covers over 1,200 questions across six domains. In its benchmark conditions, tested models adopted incorrect retrieved content that overrode correct prior knowledge more than 60% of the time.
- WikiContradict (NeurIPS, 2024): this evaluation uses 253 human-annotated instances of real-world Wikipedia knowledge conflicts. Its authors report difficulty representing conflicts accurately, especially implicit ones. They also report an F-score of 0.8 for an automated model on this benchmark; that is not a general performance guarantee for automated evaluators.
The reviewed studies do not provide a representative estimate of what share of deployed assistant interactions involve conflicting documents. Use their results to understand the kinds of behavior a test can expose, not to predict a production failure rate.
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Which public resources fit which evaluation?
- ConfRAG: real-world questions paired with retrieved web passages; useful for answer clustering, answer coverage, and reason coverage.
- ClashEval: evaluates tension between retrieved evidence and model prior knowledge, including perturbed evidence.
- WikiContradict: human-annotated Wikipedia conflicts, including implicit and same-source cases.
- CONFACT: conflict-focused fact-checking data and research examining source credibility in retrieval and generation.
- TREC RAG: a research track with distinct retrieval and RAG tasks; its site lists 2026 materials and dates.
- NVIDIA RAG Blueprint and Amazon Bedrock evaluations: examples of vendor evaluation workflows and metrics. Their documentation describes their own features, not neutral comparative evidence that one vendor is superior; check current availability, supported models, and region before adopting a workflow.
- Microsoft Azure RAG prompt engineering guidance: practical examples for labeling sources, handling conflicts, and tracking prompt and evaluation versions.
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