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Benchmarks for procedural hallucination in conversational agents
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Benchmarks for procedural hallucination in conversational agents
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LLM agents are increasingly asked to carry out procedures: book the appointment, verify the identity, file the report. They can stay fluent, polite and factually correct while quietly stopping to follow those procedures. We call this procedural hallucination, and DialFork measures it on raw dialogue text, with labels that need no manual annotation.
| dataset | what it is |
|---|---|
| dialfork | 36,278 task-oriented dialogues: 2,134 conformant ones and 34,144 forks of them that drift away from the workflow at controlled points, across 24 tasks and 12 domains. Validated by 30 annotators. |
The code to evaluate your methods, the paper's methods and the generation pipeline are on GitHub.
Made with 馃挍 at Idiap Research Institute 路 Happy forking! 馃敱
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