2 citations · 4 across the 6 of their papers we have counts for
6 papers
Improving Conversational Recommendation with Contextual Adaptation of External Recommenders and LLM-based Reranking
Chuang Li, Weida Liang, Hengchang Hu +4
We tackle the challenge of integrating large language models (LLMs) with external recommender systems to enhance domain expertise in conversational recommendation (CRS). Current LL…
Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems
Chuang Li, Yang Deng, Hengchang Hu +2
This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g.…
CrossTune: Black-Box Few-Shot Classification with Label Enhancement
Danqing Luo, Chen Zhang, Yan Zhang +1
Training or finetuning large-scale language models (LLMs) requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstr…
How Well Do Text Embedding Models Understand Syntax?
Yan Zhang, Zhaopeng Feng, Zhiyang Teng +2
Text embedding models have significantly contributed to advancements in natural language processing by adeptly capturing semantic properties of textual data. However, the ability o…
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State Tracking
Chuang Li, Yan Zhang, Min-Yen Kan +1
Previous zero-shot dialogue state tracking (DST) methods only apply transfer learning, ignoring unlabelled data in the target domain. We transform zero-shot DST into few-shot DST b…
A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems
Chuang Li, Hengchang Hu, Yan Zhang +2
Conversational recommender systems (CRS) generate recommendations through an interactive process. However, not all CRS approaches use human conversations as their source of interac…