Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning (2024)

Abstract: We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes, Motivational Interviewing (MI). Addressing such a task requires a system that could infer how to motivate the user effectively. We propose DIIR, a framework that is capable of learning and applying conversation strategies in the form of natural language inductive rules from expert demonstrations. Automatic and human evaluation on instruction-following large language models show natural language strategies descriptions discovered by DIIR can improve active listening skills, reduce unsolicited advice, and promote more collaborative and less authoritative conversations, outperforming in-context demonstrations that are over 50 times longer.

## introduction we study motivational interviewing (mi), where a conversational agent assists users in finding motivation and reducing ambivalence in adopting positive lifestyle changes such as quitting drug abuse and seeking medical assistance @xcite . a successful mi dialogue system must be able to infer how to best motivate a user to adopt positive changes, which is a nontrivial task. for example, as shown in figure 1 , one way to convince a user to workout is to educate the user about its benefits, yet immediately offering suggestions without understanding the context can risk creating resistance and confronting users @xcite . while prior work studied rule-based dialogue systems for mi @xcite and mi dialogue response rephrasing @xcite , another important problem is to build dialogue systems that automatically discover and learn mi skills. in this work, we introduce diir (dialogue strategy learning via inductive reasoning) 1 , a framework for aligning llms to dialogue behavior of 1 https://github.com/zhouhanxie/diir [dialogue history] user: i want to start working out, but i'm really busy at work gpt3.5: i understand (…) however, it is important to exercise (…) [open question] what are some particular tasks that are blocking you from working out (…)? ## dialogue strategy learning via inductive reasoning the goal of an mi interviewer is to convince a client to make a positive change of interest, such as to start working out. given a dialogue history h consisting of previous user-system turns, an ideal mi dialogue system produces the next system response a that best convinces the user to make a positive change. we solve this optimal response generation problem by using llms to analyze demonstration dialogues and generate dialogue strategy descriptions stating how to respond in various situations, such as "when the user is hesitant about change, ask open questions." these descriptions can then be reused to improve an instructionfollowing llm to promote demonstration-aligned dialogue behavior, such as asking open questions algorithm 1 enhancestrategy input: dialogue history h, gold response a * , generator llm g, discriminator llm d, executor llm e, maximum number of trials n output: strategy e in natural language, description of its applicable situation k 1: e ← "" 2: for i in n do 3: a ′ ← e.generate_response (h, e) 4: if d.confirms_is_similar (a ′ , a * ) then 5: break 6: e ← g.improve_strategy (h, e, a * , a ′ ) 7: k ← g.describe_situation (h) 8: return e, k when the client is hesitant. we now describe diir's learning and inference process (see appendix d for step-by-step concrete examples). ## results and analysis our main results are as shown in overall, our method produces fewer miinconsistent responses, provides more in-depth reflections, and better leverages active listening skills and the mi spirit of not assuming expert roles. these trends are coherent with human evaluation results, as shown in llms for generating and reusing natural language strategies generating hypothesis from observations and reusing the generated hypothesis with llms have shown a promising direction for various tasks, such as planning @xcite @xcite , prompt-generation @xcite , and code generation @xcite . this is a form of inductive reasoning @xcite where llms draw conclusion from obvervations. however, prior works require interactive environments for feedback which are unsuitable for learning from static datasets, while our work use an llm for feedback and lifts the requirement for interactive environments. ## conclusion we propose diir, a method for few-shot aligning large language models to mi techniques. our method infers natural language strategies from expert demonstrations and reuses these strategies at inference time. automatic and human evaluation show our method is better aligned with the behavior of motivational interviewing experts, produces more in-depth reflections, and better leverages active listening skills, outperforming a variety of demonstration-based methods.