Edit Discovery in Human-AI Interactions
Human-AI interactions are becoming a rich source of data for improving AI systems: while many model responses are already satisfactory, others expose failures that could become valuable supervision if identified and corrected.
ProofPaper ↗
Key points
- We study this setting through the notion of an editor: a more capable evaluation process that can inspect a response and either confirm it or modify it, but whose invocation is costly.
- We formulate this problem as Edit Discovery Optimization (EDO), which seeks to minimize edit opportunities missed among skipped interactions while controlling unnecessary confirmations and respecting an editor-cost budget.
- Building on this structure, we develop EDIT (Edit Discovery via Iterative Thresholding), an online algorithm for the partial-feedback setting in which edit labels are revealed only for selected interactions.
- Across four reasoning and factual question-answering benchmarks, EDIT achieves a better trade-off between editor cost, Type-I error, and Type-II error than direct LLM-based batch selection.
Sources (1)
- [1]Edit Discovery in Human-AI InteractionsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 09:24 PM
Human-AI interactions are becoming a rich source of data for improving AI systems: while many model responses are already satisfactory, others expose failures that could become valuable supervision if identified and corrected.
We study this setting through the notion of an editor: a more capable evaluation process that can inspect a response and either confirm it or modify it, but whose invocation is costly.
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