Chat dialogues contain considerable useful information about a speaker's interests, preferences, and experiences.Thus, knowledge from open-domain chat dialogue can be used to personalize various systems and offer recommendations for advanced information.This study proposed a novel framework SumRec for recommending information from open-domain chat dialogue.The study also examined the framework using ChatRec, a newly constructed dataset for training and evaluation. To extract the speaker and item characteristics, the SumRec framework employs a large language model (LLM) to generate a summary of the speaker information from a dialogue and to recommend information about an item according to the type of user.The speaker and item information are then input into a score estimation model, generating a recommendation score.Experimental results show that the SumRec framework provides better recommendations than the baseline method of using dialogues and item descriptions in their original form. Our dataset and code is publicly available at https://github.com/Ryutaro-A/SumRec
Parameter-Efficient Conversational Recommender System as a Language Processing Task
Nanyang Technological University（南洋理工大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Singapore University of Technology and Design（新加坡科技设计大学）
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；The Hong Kong University of Science and Technology（香港科技大学）
Considerable progress has been made towards conversational models that generate coherent and fluent responses by training large language models on large dialogue datasets. These models have little or no control of the generated responses and miss two important features: continuous dialogue skills integration and seamlessly leveraging diverse knowledge sources. In this paper, we propose the Adapter-Bot, a dialogue model that uses a fixed backbone conversational model such as DialGPT (Zhang et al., 2019) and triggers on-demand dialogue skills (e.g., emphatic response, weather information, movie …
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Gaoling School of Artificial Intelligence, Renmin University of China, Beijing, China（中国人民大学北京校区人工智能学院）；Beijing Key Laboratory of Research on Large Models and Intelligent Governance（北京大型模型与智能治理研究重点实验室）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）；Huawei Noah’s Ark Lab, China（华为诺亚方舟实验室）
Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems
National University of Singapore（新加坡国立大学）；NUS Graduate School for Integrative Sciences and Engineering（新加坡国立大学综合科学与工程研究生院）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Ecole Polytechnique de Montréal（蒙特利尔综合理工学院）；Element AI（元素人工智能公司）；Microsoft Research Montreal（微软研究院蒙特利尔分院）；Université de Montréal（蒙特利尔大学）；Mila（米拉）；HEC Montréal（蒙特利尔高等商学院）
There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we hav…
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Hefei University of Technology（合肥工业大学）；University of Science and Technology of China（中国科学技术大学）；East China Normal University（华东师范大学）；Pennsylvania State University（宾夕法尼亚州立大学）
McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）
We investigate evaluation metrics for dialogue response generation systems where supervised labels, such as task completion, are not available. Recent works in response generation have adopted metrics from machine translation to compare a model's generated response to a single target response. We show that these metrics correlate very weakly with human judgements in the non-technical Twitter domain, and not at all in the technical Ubuntu domain. We provide quantitative and qualitative results highlighting specific weaknesses in existing metrics, and provide recommendations for future developme…
University of Notre Dame（圣母大学）；IBM Research（IBM研究院）
A question answering (QA) system is a type of conversational AI that generates natural language answers to questions posed by human users. QA systems often form the backbone of interactive dialogue systems, and have been studied extensively for a wide variety of tasks ranging from restaurant recommendations to medical diagnostics. Dramatic progress has been made in recent years, especially from the use of encoder-decoder neural architectures trained with big data input. In this paper, we take initial steps to bringing state-of-the-art neural QA technologies to Software Engineering applications…
