Aligning Predictive Uncertainty with Clarification Questions in Grounded Dialog (2023.findings-emnlp)
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| Challenge: | Previous work bases the timing of questions on supervised models learned from interactions between humans. |
| Approach: | They propose to ground the need for questions in the acting agent's predictive uncertainty by using the T5 encoder-decoder architecture to solve a Minecraft Collaborative Building task. |
| Outcome: | The proposed model can detect ambiguous instructions and predict responses better than previous models. |
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| Challenge: | Using model uncertainty as supervision for deciding when to ask may not be the most effective way to resolve model uncertainty. |
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| Challenge: | Ambiguity is embedded throughout natural language, and even simple utterances can have multiple interpretations when read in isolation. |
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| Challenge: | Ambiguous user queries pose a challenge in task-oriented dialogue systems . Large Language Models (LLMs) rely on the top-k retrieved documents for clarification . traditional approaches lack principled mechanisms to determine when to use broad domain knowledge vs specific retrieved document context for clarification. |
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Alisa Liu, Zhaofeng Wu, Julian Michael, Alane Suhr, Peter West, Alexander Koller, Swabha Swayamdipta, Noah Smith, Yejin Choi
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| Challenge: | Direct prompting fails to detect ambiguity while linear probes can decode ambiguities with high accuracy, sometimes exceeding 90%. |
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| Challenge: | Existing work on Minecraft Corpus Dataset only learns to execute instructions neglecting the importance of asking for clarifications. |
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