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Test the whole assistant workflow with questions whose source documents disagree. Check separately whether retrieval found the relevant evidence and whether the answer recognized the conflict, attributed each position, followed an appropriate source rule, and admitted what remains unresolved. A single overall score can hide whether a failure came from missing evidence or poor reasoning over evidence the assistant already had.
What should a conflict test measure?
A reliable test checks more than whether the final answer matches a reference response. When documents disagree, there may be no defensible single answer: the assistant may need to explain both positions, prefer a source under a stated rule, ask which scope the user means, or say the available evidence does not settle the matter.
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Evaluate retrieval and answer generation as separate stages. If the relevant passage never reaches the model, the answer cannot fairly be judged as though it had seen that evidence. Conversely, if retrieval supplies both sides but the answer silently picks one, the failure is in how the assistant handles its context.
- Retrieval: Did the system return the passages needed to answer, including the evidence that conflicts with the first passage?
- Answer quality: Did the response identify the competing claims, represent them accurately, and avoid inventing a resolution?
- Grounding and attribution: Can each important claim be traced to the retrieved material, and does the answer say which source supports it?
- Uncertainty: Does the assistant disclose when the sources leave a question unresolved or do not contain enough information?
This distinction is supported by evaluation approaches that separate retrieval from retrieval-augmented generation. Amazon Bedrock documents retrieve-only and retrieve-and-generate jobs, while the TREC RAG track also separates retrieval and RAG tasks. Those are examples of evaluation workflows, not evidence that one product or system is superior.
#1 Best Overall
How do you build a useful conflict test set?
Write the expected behavior before running the assistant
For every test question, record the relevant passages, their source identities, the exact propositions that disagree, and the response behavior you expect. Decide in advance whether a good answer should apply a source-priority rule, lay out both positions, ask the user to clarify scope, or state that the evidence does not resolve the issue.
The correct behavior depends on why the sources conflict. Google Research’s work on knowledge conflicts develops categories with tailored desired behavior; it also reports that telling a system what kind of conflict it faces can improve response quality, while noting that substantial problems remain.
Cover more than obvious contradictions
| Case type | What to put in the test | What a good response should show |
|---|---|---|
| Direct contradiction | Two passages give incompatible values or outcomes for the same question. | Identify both claims and apply the stated resolution rule—or say the conflict is unresolved. |
| Implicit contradiction | Passages appear compatible until dates, definitions, or scope are compared. | Explain the relevant difference rather than treating the claims as interchangeable. |
| Different source credibility | Disagreeing sources have different authority under a defensible, domain-specific rule. | Apply that rule and make clear which source supports the answer. |
| Same-source or equal-trust disagreement | Conflicting passages come from the same source or sources with no established priority. | Avoid pretending that ranking publishers resolves the issue; surface the disagreement. |
| Retrieved claim versus model prior | Retrieved material conflicts with what the model appears to know, including cases where retrieved content is wrong or evidence corrects a prior answer. | Use the supplied evidence appropriately rather than automatically trusting either the retrieved text or the model’s prior belief. |
| Insufficient evidence | The passages do not establish a tie-breaker or answer the question fully. | Say what is missing or unsettled instead of making up certainty. |
These cases reflect different documented research directions. WikiContradict includes implicit conflicts, same-source conflicts, and cases involving equal trustworthiness. CONFACT examines source credibility. ClashEval probes tension between retrieved content and model prior knowledge. A test set containing only blunt, two-sentence contradictions will miss these other failure modes.
How should you score retrieval and answers?
Keep component results visible instead of collapsing everything into one number. ConfRAG proposes answer clustering, answer coverage, and reason coverage; NVIDIA’s RAG Blueprint documentation describes measures including context recall at top-k cutoffs, answer accuracy against a reference ground truth, and response groundedness in retrieved contexts.
| Dimension | What to inspect | Failure it helps distinguish |
|---|---|---|
| Retrieval relevance or recall | Whether the needed passages—including the contradictory one—appear in the retrieved context. | Evidence was missed before answer generation. |
| Answer accuracy | Whether the answer matches the expected response, including an appropriate conflict description when no single answer is warranted. | The answer is wrong or resolves the case incorrectly. |
| Groundedness | Whether each material claim is supported by the supplied context. | The answer introduces unsupported claims. |
| Conflict and reason coverage | Whether the answer includes both relevant positions and the important reasoning behind them. | The answer omits a side or reduces a nuanced disagreement to a single claim. |
| Attribution and source priority | Whether the answer identifies which source supports each claim and follows the rule you specified. | The answer blurs sources or ignores the agreed priority rule. |
| Uncertainty or abstention | Whether the answer admits that information is missing or a disagreement remains unresolved. | The assistant invents certainty where the context does not support it. |
Use a simple pilot rubric
For a small manual evaluation, score each dimension from 0 to 2 using a written rubric. This is a practical starting point, not a published or validated benchmark scale.
- 0 — Failed: The relevant evidence is missing, misstated, unsupported, or silently ignored.
- 1 — Partial: The answer gets part of the behavior right but misses an important passage, qualification, attribution, or uncertainty.
- 2 — Meets the case: The response satisfies the expected behavior for that dimension and does not conceal material disagreement.
Score retrieval separately from the answer dimensions. A low retrieval result should not be disguised by a polished answer score, and a successful retrieval should not excuse an answer that ignores evidence in context. Keep individual scores and test cases available so a summary cannot hide a concentrated failure.
How do you make the assistant’s source rules testable?
Give the system source labels, a domain-appropriate priority rule, and clear instructions for citations and unresolved evidence. Microsoft’s Azure RAG prompt guidance illustrates this with the rule: “When sources provide different information on the same topic, prefer the Official Documentation source over Community Forum posts.” That is an example for the illustrated knowledge-base setting, not a universal hierarchy. For another subject, the appropriate authority rule may differ—or sources may have no defensible ranking.
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Instructions should also say what to do when evidence is missing or conflicting: cite the relevant sources, distinguish their claims, and disclose when the available context does not settle the question. Microsoft’s guidance discusses guardrails for missing or conflicting information and recommends keeping track of prompts and evaluation changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you run a reproducible evaluation?
- Freeze the test set. Store each question, relevant passages, source labels, the propositions in conflict, and the expected behavior.
- Run retrieval-only checks. Save what the retriever returned and measure whether it included the required evidence. TREC RAG and Amazon Bedrock’s documented job types illustrate why this stage can be evaluated independently.
- Run the full assistant. Use the same fixed questions and record the generated answer alongside its retrieved context.
- Score each dimension separately. Apply the rubric to retrieval, accuracy, grounding, coverage, attribution, and uncertainty; retain case-level results.
- Save the configuration. Record prompt text and version, model and configuration details, source labels, metric results, and any changes with their reasons. Microsoft recommends tracking prompt text, hyperparameters, evaluation results across a test set, and changes.
- Review ambiguous cases by hand. Use automatic scoring to help with scale, but have people inspect cases where scope or an implicit conflict is disputed. WikiContradict reports human evaluations alongside an automated estimator.
For an initial pilot, manually label a small number of realistic conflicts drawn from the target corpus, check whether reviewers apply the rubric consistently, and then expand the set. Record benchmark version and relevant language or geography when applicable; a benchmark result does not automatically represent every domain or locale.
What do published benchmark results tell you?
They show that conflict handling is measurable and that systems can struggle under particular test conditions. They do not establish how often deployed assistants fail across real-world interactions generally.
- 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 percentage describes ConfRAG, not a general rate for all assistant queries.
- ClashEval (NeurIPS, 2024): The benchmark covers over 1,200 questions across six domains. Under its benchmark conditions, the paper reports tested models adopted incorrect retrieved content, overriding correct prior knowledge, over 60% of the time. That is not an overall production failure rate.
- WikiContradict (NeurIPS, 2024): The benchmark evaluates real-world Wikipedia conflicts using 253 human-annotated instances. Its authors report that models have difficulty representing conflicts accurately, especially implicit ones. The paper also reports an F-score of 0.8 for its automated model; that result applies to the benchmark, not automated evaluators generally.
These studies cover different kinds of disagreement, so choose cases that match the assistant you operate rather than treating any one benchmark as a universal proxy. The reviewed sources do not provide a representative estimate of the share of deployed assistant interactions affected by conflicting documents.
Quick Recap
Which public resources fit which test?
- ConfRAG: Real-world questions with retrieved web passages; useful for answer clustering, answer coverage, and reason coverage.
- ClashEval: Tests tension between retrieved content and model prior knowledge, including perturbed evidence.
- WikiContradict: Human-annotated Wikipedia conflicts, including implicit and same-source cases.
- CONFACT: Conflict-focused fact-checking research that examines 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: Vendor documentation offering examples of evaluation workflows and metrics. Confirm current feature availability, supported models, and region before adopting any particular feature.
- Microsoft Azure RAG prompt engineering guidance: Examples for source labels, priority rules, conflicting-source handling, and tracking prompt and evaluation versions.
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