Persuading across Diverse Domains: a Dataset and Persuasion Large Language Model (2024)

Abstract: Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multiple domains. In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion. Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user’s utterance and reasons next strategy to respond. Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately. Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation. The code and data are available at https://persugpt.github.io.

## introduction persuasive dialogue is a frequent and important social interaction in daily life. however, it is difficult to mimic such a general ability for a dialogue system because a good persuader requires many skills, such as understanding users' underlying intents @xcite , applying effective persuasive strategies @xcite according to different situations across domains, and anticipating users' sophisticated reactions @xcite . previous works have studied retrieval-based models @xcite or generative models @xcite on a small-scale dataset of a specific domain, e.g., persuasionforgood @xcite collecting about 1,000 sessions in charity have you considered installing a security system with smart cameras and alarms in your home? i think my neighborhood is safe and don't' see the need for a security system. while our neighborhood is generally safe, there have been instances of break-ins and theft in the past. a security system can act as a deterrent and help keep your property secure. really? i didn't know that. but aren't security systems expensive and complicated to use? actually, modern security systems are designed to be user-friendly and easy to install. plus, the peace of mind you'll gain is well worth the investment. intent to strategy reasoning: mike is showing interest now. i'll address his concerns about cost and complexity by explaining the smart features and ease of use (not displayed). response: actually, modern security systems are quite affordable and easy to use. they come with smart features like remote access, motion sensors, and alerts, which make it simple to monitor your home from anywhere. plus, i can help you set everything up! ours gpt-4 figure 1 : an example of generating persuasive dialogue in the scenario of persuading mike to install a security system in home. the strategy used is in bold. domain. recent advances in large language models (llms) @xcite shed light on developing a general persuasion model, but it is still challenging even for stateof-the-art llms due to the scarcity of large-scale multi-domain datasets, as well as the need for multiturn following and planning abilities to achieve the goal of persuading users @xcite . in this paper, we aim to address the problem of learning a general persuasion model across domains. first, we construct a multi-domain persuasive dialogue dataset named dailypersuasion, with the assistance of gpt-4 and a well-designed solution. it comprises 78,000 dialogue sessions spanning 35 domains in daily lives. second, to enhance multi-turn following and planning ability, we gener-ate user intents and next strategies while collecting the data and integrate an intent-to-strategy reasoning process into our proposed persugpt. a strategy reasoning case is shown in fig. 1 , persugpt captures the user's intent "showing interest", and then chooses the "explaining the smart features" strategy. third, we propose using a learned user model and our persugpt to simulate the next following turns and evaluate rewards, and then use the composed preference to directly optimize persugpt. extensive experiments on two datasets indicate that our persugpt, equipped with 13 billion parameters, outperforms all baselines including gpt-4 in terms of automatic metric win-rate and human ratings. our main contributions are as follows: (1) to our best knowledge, we construct the first largescale multi-domain persuasive dialogue dataset dailypersuasion, comprising 78,000 dialogue sessions across 35 domains; (2) we propose a crossdomain persuasion model persugpt, which integrates the proposed intent-to-strategy reasoning and multi-turn simulation-based preference optimization; and (3) extensive experiments verify that our model has significant superiority over the stateof-the-art llms including gpt-4, and strong generalization abilities across domains. ## constructing dailypersuasion dataset to ensure the diversity and naturalness of persuasive conversations, as shown in fig. 2 , we adopt a three-step data collection framework to build our dailypersuasion dataset:{(c i , s i , d i )} n @xmath0, where c i is a persuasion scenario containing a background and a goal, s i is a strategy set related to the scenario, and d i is a session of dialogues corresponded to the scenario and strategies. we evaluate samples to ensure quality and diversity at each step. ## training persugpt based on our dailypersuasion dataset, we build a large language model named persugpt, which possesses high persuasion ability across multiple domains. given a base large language model, our goal is to train the model so that it can 1) persuade more effectively by explicitly reasoning about the user intent and persuasion strategy; and 2) better anticipate user responses and future rewards through simulated interactions. to this end, we develop a two-phase training procedure described as follows. ## experiments we conduct experiments to assess our model's performance on unseen persuasion scenarios and its adaptability to new domains. ## conclusion in this paper, we build the first large-scale crossdomain persuasive dialogue dataset named dai-lypersuasion, encompassing 76,000 sessions across 35 domains, annotated with user intents and persuader strategies. then we propose a cross-domain persuasive dialogue model persugpt, which incorporates proposed intent-to-strategy reasoning and simulation-based preference optimization. extensive experiments demonstrate that persugpt surpasses gpt-4 with isr in terms of win-rate (60.4%) and human ratings (4.35 vs. 4.17) on dai-lypersuasion and all baselines on persuasionfor-good. our proposed persugpt also has good generalization abilities from a training domain to a new test domain. we plan to further optimize persugpt through higher-quality automatic interactions and more accurate feedback. incorporating retrievalaugmented methods for persuasive fact referencing is also within the scope of our future work.