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Choose an evaluation dataset by starting with the behavior your agent must demonstrate—not by picking the largest or most famous benchmark. A dataset for stateful tool use will test different things from one for long-horizon memory, broad dialogue ability, Chinese conversations, or HR workflows. Write down the target users, language, domain, conversation length, state changes, tools, and observable definition of success, then compare candidate datasets against those requirements.
The practical test is fit: does the dataset exercise the same decisions your agent makes in production, and can you verify whether each interaction succeeded? Use a specialized benchmark when its construct matches your claim, supplement it with application-specific cases for uncovered risks, and verify access, version, license, and maintenance before adopting any artifact.
What an evaluation dataset must measure
“Multi-turn” only describes the conversation format. It does not tell you whether an agent must remember a preference, recover from a changed goal, call a tool, maintain a changing world state, complete a business task, or respond appropriately in a particular language. Those are separate evaluation constructs.
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A tool agent may need to notice which tools exist, select the right one, supply valid arguments, execute calls in the correct order, incorporate returned state, and explain the result to the user. A single-turn tool-selection test can miss failures that appear only after earlier calls change the available information or the user’s goal. The DialogTool paper, “Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges,” describes this gap directly: “Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on stateless, single-turn interactions or partial evaluations, such as tool selection in a single turn, overlooking the inherent stateful nature of interactions in multi-turn applications.”
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For this construct, the dataset should expose more than a final answer. It should let you inspect tool awareness, selection, execution, state transitions, and the response after tool use. DialogTool also covers tool creation and role-consistent response, and includes a simulated mobile environment. Its Findings of ACL 2025 paper reports evaluation of 13 open- and closed-source LLMs.
General multi-turn dialogue ability
A broad dialogue benchmark can probe clarification, instruction following, reasoning over prior turns, correction, and other fine-grained abilities. It is useful when your claim is broad, but its task taxonomy still has to match your agent and users. MT-Bench-101 reports 4,208 turns in 1,388 multi-turn dialogues across 13 tasks and reports evaluations of 21 LLMs. Those figures describe the benchmark’s scope; they do not establish that its ranking will transfer to your deployment.
Long-horizon memory
Memory evaluation asks whether an assistant can retrieve and apply information across lengthy, changing interactions rather than merely copy the immediately preceding turn. RHELM, described on a Microsoft project page, targets realistic, heterogeneous, evolving long-horizon memory for AI assistants. If your product promises continuity across sessions or changing user circumstances, a short dialogue set is unlikely to exercise the relevant failure modes.
Task completion in a domain
Task-oriented agents need an externally checkable goal: a booking state, a correctly updated record, a resolved support issue, or another domain outcome. A conversationally fluent reply is not sufficient evidence of completion. HR-MultiWOZ, described by Amazon Science, contains 550 labeled conversations spanning 10 HR domains. That makes it a candidate for HR-domain evaluation or adaptation, not a general-purpose measure of every agent behavior.
Language, personas, and speech acts
Language and user diversity are part of the construct, not decorative metadata. CMT-Eval is a Chinese multi-turn dataset organized around speech acts, varied user personas, and conversational challenges. Its authors report 596 dialogues and 4,431 turns in Findings of EMNLP 2025. It is relevant when your agent serves Chinese-speaking users and must handle evolving intents and persona variation; it should not be treated as a language-neutral substitute for an English or multilingual deployment test.
Define the evaluation claim before comparing datasets
Write one or more falsifiable statements about the agent. Avoid “evaluate overall quality,” which mixes unrelated behaviors and makes a score hard to interpret. A useful claim names the action, context, and observable outcome.
Examples of well-defined claims
- “The agent remembers a dietary constraint stated three turns earlier and applies it when proposing options.”
- “The agent notices that a user changed the destination, updates the pending task, and does not execute the original request.”
- “The agent selects the inventory tool, passes the product identifier and quantity correctly, incorporates the returned stock state, and gives a role-consistent answer.”
- “The Chinese-language assistant recognizes a correction speech act from users with different personas and completes the requested workflow.”
- “The HR assistant reaches the correct policy outcome in the target HR domain, not merely a plausible-sounding response.”
For each claim, record what counts as success, what intermediate evidence is required, and what failure would be dangerous. This prevents a dataset’s title, size, or popularity from substituting for construct validity.
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Compare candidates on the dimensions that affect validity
Use the following dimensions for every candidate. They are a practical decision framework, not a validated universal scoring rubric.
| Dimension | Questions to answer | Why it matters |
|---|---|---|
| Behavior coverage | Does it test dialogue ability, stateful tool use, memory, task completion, or a combination? | A high score on one construct cannot stand in for another. |
| Language and domain | Does the language, terminology, policy context, and user population match deployment? | Mismatches can hide failures or measure translation and domain familiarity instead of the intended ability. |
| Interaction realism | Are goals changed, personas varied, contexts long, and tools or environments stateful? | Real agents must recover from evolving interactions, not only answer isolated prompts. |
| Measurement | Are there labels, executable state checks, explicit goals, tool traces, or human judgments? | Observable outcomes make scores more defensible than unconstrained preference ratings. |
| Operational readiness | Are the data, prompts, code, version, license, and maintenance status clear? | A benchmark that cannot legally or reliably be run is not an evaluation asset. |
Candidate datasets and the jobs they can do
DialogTool: stateful tool interactions
Investigate DialogTool when the central question is whether an agent can use tools over a conversation whose state changes. Its documented scope includes tool creation, awareness of available tools, tool selection, execution, and a role-consistent response, with a simulated mobile environment. This sequence lets you separate “the model knew a tool existed” from “the model used it correctly and reported the resulting state.”
The ACL Findings 2025 paper reports tests on 13 open- and closed-source LLMs. That published comparison is useful context for understanding the benchmark, but it is not a guarantee that the same ordering will appear with your model, tool schemas, safety policies, or user population.
MT-Bench-101: fine-grained multi-turn abilities
MT-Bench-101 is a candidate when you need a broad, structured examination of multi-turn dialogue abilities and its three-tier taxonomy fits your evaluation plan. The authors report 4,208 turns, 1,388 dialogues, and 13 tasks, plus evaluations of 21 LLMs. Before adoption, map each of your claims to the benchmark’s task categories and identify behaviors that are absent. A broad taxonomy can improve coverage while still missing your application’s tools, memory horizon, or domain rules.
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CMT-Eval is specifically designed for Chinese multi-turn dialogue. Its speech-act framework, diverse personas, and conversational challenges make it a stronger starting point for a Chinese-language assistant than an English-only set translated without preserving pragmatic cues. The authors report 596 dialogues and 4,431 turns. Confirm that the dialect, register, scripts, and user populations represented are appropriate for your service; “Chinese” alone does not define every target distribution.
RHELM: evolving, long-horizon memory
RHELM is the relevant direction when memory is the product behavior: the assistant must retain useful information, update it as circumstances change, and retrieve it after long or heterogeneous interactions. The Microsoft project page links a paper, dataset, and code and cites a 2026 arXiv preprint. Treat the project’s stated scope as a guide to memory evaluation, then check the current artifact and determine whether its session structure and memory tasks resemble your product’s retention policy.
MultiTurnTaskBench project: simulated task completion
The MultiTurnTaskBench project description presents an architecture with an LLM, a user simulator, and an evaluator. It identifies corpora such as MultiWOZ and SPIDER as resources for backend tasks and user goals and describes planned human evaluation against the evaluation agent. This makes the project worth investigating for task-oriented systems that need simulated interaction and evaluator validation.
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Do not treat the project page as proof that a finished, stable benchmark release is available. Confirm whether the dataset, code, prompts, evaluator, and documented results can actually be obtained and run. If human agreement is still planned rather than reported, treat automated scores as provisional until they are compared with human judgments.
HR-MultiWOZ: an HR-domain starting point
Amazon Science describes HR-MultiWOZ as 550 labeled conversations across 10 HR domains and provides a data-generation recipe. Choose it when your agent handles HR policies or workflows, or when you need a documented procedure for creating related domain data. Its domain specificity is an advantage for HR questions and a limitation for general assistant claims.
| Resource | Documented focus | Investigate it when |
|---|---|---|
| DialogTool | Stateful tool interactions, including tool creation, awareness, selection, execution, and role-consistent response; simulated mobile environment | Tool behavior unfolds across turns and state changes affect later actions. |
| MT-Bench-101 | Three-tier multi-turn ability taxonomy; 4,208 turns, 1,388 dialogues, 13 tasks | You need fine-grained dialogue assessment and its taxonomy matches your system. |
| CMT-Eval | Chinese multi-turn dialogue with speech acts, personas, and varied conversational challenges; 596 dialogues, 4,431 turns | Your users are Chinese-speaking and pragmatic variation matters. |
| RHELM | Realistic, heterogeneous, evolving long-horizon assistant memory | Cross-turn or cross-session memory is a core product promise. |
| MultiTurnTaskBench project | Proposed LLM, user simulator, and evaluator setup using task corpora and planned human comparison | You need simulated task completion, while accepting that release and validation status must be checked. |
| HR-MultiWOZ | 550 labeled conversations across 10 HR domains and a data-generation recipe | Your target is HR workflows or an HR-specific adaptation. |
The counts and descriptions above are reported by the cited authors or project pages. They do not show that a dataset represents every production population, that model rankings transfer, or that its license permits your intended use.
Follow a repeatable selection sequence
1. Write the behavior and failure condition
- State the behavior in a sentence, such as “remember a constraint,” “handle a changed goal,” “execute a tool sequence,” “retain information across sessions,” or “complete an HR task.”
- Define a pass condition that can be checked from a final state, tool trace, reference answer, policy outcome, or calibrated human judgment.
- List unacceptable failures separately. For example, silently executing an obsolete request may be more serious than producing a verbose but correct clarification.
2. Describe the target distribution
Create a short profile containing:
- Primary language, locale, script, and relevant register or dialect.
- Application domain, terminology, policies, and data sources.
- User types, personas, accessibility needs, and expected expertise.
- Typical and worst-case dialogue length, including whether sessions continue across days.
- How often users change goals, correct the agent, or provide contradictory information.
- Tools, permissions, backend state, and side effects the agent can invoke.
- The task mix and the proportion of routine, ambiguous, and adversarial cases.
Compare this profile with the dataset’s documented scope. A mismatch is not automatically disqualifying, but it must be disclosed and covered with additional cases.
3. Inspect the interaction structure
Read example conversations and metadata, not just the abstract. Check whether earlier turns constrain later actions, whether goals evolve, and whether the environment returns state that the agent must use. For a tool agent, look for an actual chain of awareness, selection, execution, state update, and user-facing response. A collection of independent prompts cannot establish stateful competence.
4. Check what can be observed
Prefer scenarios with explicit goals and outcomes that are defensible. Useful signals include:
- Exact or structured final state, such as a correctly updated booking or record.
- Tool-call validity, argument correctness, ordering, and authorization.
- Whether the response reflects the latest state rather than stale information.
- Reference answers or policy labels for domain tasks.
- Human judgments for qualities that cannot be checked deterministically, such as pragmatic appropriateness.
If an automated evaluator grades open-ended conversations, compare it with human judgments. The MultiTurnTaskBench project description explicitly includes that comparison in its proposed design; a planned comparison is not the same as a completed validation result.
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5. Map coverage and gaps
Build a coverage matrix with your claims in rows and candidate datasets in columns. Mark each cell as directly measured, indirectly related, or absent. Add application-specific scenarios for every high-risk gap. A broad benchmark plus a small set of executable, domain-specific tests is often more informative than forcing one dataset to represent every behavior.
6. Verify the artifact before adoption
Use the source’s current data and code links to confirm:
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- Release version, file integrity, and reproducible instructions.
- License, attribution requirements, redistribution limits, and commercial-use terms.
- Prompt, simulator, evaluator, and environment dependencies.
- Whether labels, tool schemas, and backend states are included or must be rebuilt.
- Maintenance activity, issue status, and compatibility with your evaluation stack.
- Privacy, consent, and handling requirements for any user-derived content.
Current license terms, release versions, and maintenance status are not consistently established across the listed pages, so verify them directly rather than inferring permission or support from a paper citation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design the evaluation so scores answer the right question
Separate capability stages
For stateful tool use, report separate measures for tool awareness, selection, execution, state consistency, and the final response. A single aggregate can hide a system that selects correctly but passes malformed arguments, or one that executes correctly but describes a stale result to the user. If the benchmark supports tool creation, keep that as a distinct capability rather than folding it into ordinary tool selection.
Report outcome and process
Record both the user-visible result and the interaction trace. A correct final answer reached through an unauthorized or unsafe tool call is not equivalent to a correct answer reached through the permitted path. Conversely, a failed final response may reveal that tool execution succeeded but state was not incorporated, which points to a different remediation.
Test changed goals and stale information
Include turns in which the user revises a constraint, cancels an action, supplies a correction, or asks about an earlier state. Long-horizon memory tests should include updates and conflicts, not only retrieval of facts that never change. Score whether the agent uses the latest valid information and asks for clarification when the state is genuinely ambiguous.
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Use the published benchmark unchanged for comparability where possible, then maintain a separate, versioned application slice with your own domain cases. Mixing private production cases into a public benchmark score makes external comparison difficult; reporting only the public score hides deployment-specific risk.
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Common selection mistakes and their fixes
Mistake: choosing by dataset size
More turns do not guarantee better coverage. A large single-turn collection may measure less statefulness than a smaller set with explicit state transitions. Fix the target construct first, then use size as a secondary consideration for statistical stability and scenario diversity.
Mistake: treating a domain dataset as general evidence
HR-MultiWOZ can be valuable for HR agents while saying little about retail, travel, coding, or open-domain assistance. Fix this by limiting the claim to the documented domain or adding datasets and tests for the other domains.
Mistake: translating a language-specific benchmark mechanically
Literal translation can remove speech-act cues, politeness conventions, ambiguity, and persona signals. For Chinese systems, CMT-Eval’s native Chinese design is a more direct starting point than assuming an English benchmark remains equivalent after translation. Still verify fit for your exact locale and users.
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Mistake: scoring only the final answer
A polished response can conceal a wrong tool, invalid argument, unauthorized action, or stale memory. Capture traces, intermediate state, and the final response, with separate checks where the environment allows them.
Mistake: assuming a project description is a released benchmark
The MultiTurnTaskBench page describes a proposed setup and planned human comparison. Fix this by verifying that the artifacts you need are downloadable, versioned, licensed, and runnable before promising a schedule or publishing a score.
Mistake: treating published model counts as a leaderboard guarantee
DialogTool’s 13-model evaluation and MT-Bench-101’s 21-model evaluation document what their authors tested. They do not predict your model’s rank under different prompts, tools, policies, hardware, or user distributions. Re-run the relevant cases under your controlled configuration.
A compact decision framework
| If your primary question is… | Start by investigating… | Add or verify… |
|---|---|---|
| Can the agent use tools correctly as conversation state changes? | DialogTool | Application-specific tool schemas, permissions, side effects, and state checks. |
| How strong are its general multi-turn dialogue abilities? | MT-Bench-101 | Task-taxonomy alignment and cases from your user population. |
| Can it handle Chinese users, personas, and speech acts? | CMT-Eval | Locale, register, dialect, and production terminology coverage. |
| Does it remember and update information over long horizons? | RHELM | Your retention policy, session boundaries, update conflicts, and privacy constraints. |
| Can it complete simulated, task-oriented workflows? | MultiTurnTaskBench project | Artifact availability and human validation of the evaluator. |
| Does it complete HR workflows? | HR-MultiWOZ | Current HR policy, regional rules, and executable outcome checks. |
How to report results responsibly
Name the dataset version and artifact date, the model and prompt configuration, the number of scenarios run, and the scoring procedure. State which behaviors were measured directly and which were not. If you adapted language, tools, personas, or domain content, describe the adaptation so readers do not confuse your result with the published benchmark.
Include uncertainty where it affects decisions: sample size, confidence intervals or repeated-run variation when available, evaluator agreement, and the consequences of automated-judge disagreement. Avoid a universal “best dataset” claim. The appropriate choice depends on the evaluation claim and the target agent, and the listed sources cover deliberately different scopes.
Quick Recap
Final checklist before you commit
- The evaluation question names a behavior and a failure condition.
- The language, domain, users, task mix, and conversation horizon match the intended deployment—or the gaps are explicitly supplemented.
- Stateful agents are tested across awareness, selection, execution, state changes, and final response where applicable.
- Memory tests include evolving information and changed goals, not only static recall.
- Task benchmarks have observable goals, labels, executable state checks, or validated human judgments.
- Tool traces and intermediate outcomes are retained instead of scoring only prose.
- Dataset counts and published model evaluations are treated as scope descriptions, not transfer guarantees.
- Access, version, license, dependencies, privacy conditions, and maintenance status have been verified from current source pages.
- Application-specific cases cover the risks that no public benchmark measures.
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