Datasets:
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
dialogue
hallucination
procedural-hallucination
task-oriented-dialogue
conversational-agents
llm-evaluation
License:
Link the leaderboard Space
Browse files
README.md
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@@ -257,7 +257,7 @@ DialFork comes with three evaluation tasks, each matching a real-world need:
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| [Task 2](https://github.com/idiap/dialfork/blob/main/docs/task2.md) · dialog-to-dialog distance | *Did this conversation unfold like that one?* | two dialogues → distance | procedural retrieval |
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| [Task 3](https://github.com/idiap/dialfork/blob/main/docs/task3.md) · turn prediction | *Where did this conversation go off-track?* | a dialogue → the turn where it first goes off-track | real-time guardrails |
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Scoring is one command (`python evaluate.py --task N predictions.csv`), and the paper's methods
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## License and source data
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| [Task 2](https://github.com/idiap/dialfork/blob/main/docs/task2.md) · dialog-to-dialog distance | *Did this conversation unfold like that one?* | two dialogues → distance | procedural retrieval |
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| [Task 3](https://github.com/idiap/dialfork/blob/main/docs/task3.md) · turn prediction | *Where did this conversation go off-track?* | a dialogue → the turn where it first goes off-track | real-time guardrails |
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Scoring is one command (`python evaluate.py --task N predictions.csv`), and the paper's methods are on GitHub. See how they compare on the 🏆 [leaderboard](https://huggingface.co/spaces/DialFork/leaderboard).
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## License and source data
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