[2305.17878] Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models
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Computer Science > Computation and Language

arXiv:2305.17878 (cs)
[Submitted on 29 May 2023]

Title:Ask an Expert: Leveraging Language Models to Improve Strategic Reasoning in Goal-Oriented Dialogue Models

Authors:Qiang Zhang, Jason Naradowsky, Yusuke Miyao
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Abstract:Existing dialogue models may encounter scenarios which are not well-represented in the training data, and as a result generate responses that are unnatural, inappropriate, or unhelpful. We propose the "Ask an Expert" framework in which the model is trained with access to an "expert" which it can consult at each turn. Advice is solicited via a structured dialogue with the expert, and the model is optimized to selectively utilize (or ignore) it given the context and dialogue history. In this work the expert takes the form of an LLM. We evaluate this framework in a mental health support domain, where the structure of the expert conversation is outlined by pre-specified prompts which reflect a reasoning strategy taught to practitioners in the field. Blenderbot models utilizing "Ask an Expert" show quality improvements across all expert sizes, including those with fewer parameters than the dialogue model itself. Our best model provides a $\sim 10\%$ improvement over baselines, approaching human-level scores on "engingingness" and "helpfulness" metrics.
Comments: Accepted in Findings of the Association for Computational Linguistics: ACL 2023
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2305.17878 [cs.CL]
  (or arXiv:2305.17878v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2305.17878

Submission history

From: Qiang Zhang [view email]
[v1] Mon, 29 May 2023 04:19:35 UTC (14,416 KB)
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