3 citations · 11 across the 30 of their papers we have counts for
31 papers
RealWorldShop: Benchmarking and Improving Conversational Shopping Agents in Real-World E-commerce
Xinwei Yang, Kelong Mao, Yudong Guo +4
Large language models are reshaping ecommerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users…
Clearing the Fog: Towards Installing and Refining Proactive Exploration Capabilities in LLM Agents
Zhizhao Guan, Chen Huang, Ziming Liu +5
We study proactive exploration in LLM agents, i.e., the ability to explore an environment to acquire information that improves future decision-making. In this regard, we first iden…
Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search
Jincheng Zhang, Chen Huang, Wenqiang Lei +2
We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regard, we propose DREAMS, a novel…
ExpConCAD: Experience-Guided Text-to-CAD Generation from Shape Descriptions with Implicit Spatial Constraints
Jingyao Liu, Jinkang Tang, Chen Huang +2
Text-to-CAD aims to generate executable CAD programs from natural-language descriptions. However, real-world descriptions are often underspecified and omit critical spatial constra…
HippoSpark: An On-Demand Experience System for LLM Reasoning
Jingyao Liu, Danling Meng, Chen Huang +5
Distilling historical trajectories into reusable experience to enhance future problem-solving has become a focal point of recent LLM research. However, existing methods predominant…
Unlocking Biological Workflows for Robust Protein-Text Question Answering: A Dual-Dimensional RAG Framework
Li Ding, Duanyu Feng, Chen Huang +4
Protein-Text Question Answering (QA) is crucial for interpreting biological sequences through natural language. The integration of Large Language Models (LLMs) with Retrieval-Augme…
Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering
Weikang Zhang, Zimo Zhu, Zhichuan Yang +3
Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineeri…
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models
Pengfeng Li, Chen Huang, Chaoqun Hao +4
Contextual causal reasoning is a critical yet challenging capability for Large Language Models (LLMs). Existing benchmarks, however, often evaluate this skill in fragmented setting…
METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues
Haofu Yang, Jiaji Liu, Chen Huang +3
Developing non-collaborative dialogue agents traditionally requires the manual, unscalable codification of expert strategies. We propose \ours, a method that leverages large langua…
Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation
Chen Huang, Zitan Jiang, Changyi Zou +2
Customer service chatbots are increasingly expected to serve not merely as reactive support tools for users, but as strategic interfaces for harvesting high-value information and b…
STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation
Shuai Li, Chen Huang, Duanyu Feng +2
To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution within Graph Retrieval Augmented Generation (GraphRAG) leverages lightweight retriev…
LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment
Jingwei Shi, Shengyu Tao, Xinxiang Yin +3
The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game design for individual patient i…
E2Edev: Benchmarking Large Language Models in End-to-End Software Development Task
Jingyao Liu, Chen Huang, Zhizhao Guan +2
The rapid advancement in large language models (LLMs) has demonstrated significant potential in End-to-End Software Development (E2ESD). However, existing E2ESD benchmarks are limi…
ELABORATION: A Comprehensive Benchmark on Human-LLM Competitive Programming
Xinwei Yang, Zhaofeng Liu, Chen Huang +4
While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding…
Can Large Language Models Understand Internet Buzzwords Through User-Generated Content
Chen Huang, Junkai Luo, Xinzuo Wang +2
The massive user-generated content (UGC) available in Chinese social media is giving rise to the possibility of studying internet buzzwords. In this paper, we study if large langua…
Breaking the Stigma! Unobtrusively Probe Symptoms in Depression Disorder Diagnosis Dialogue
Jieming Cao, Chen Huang, Yanan Zhang +3
Stigma has emerged as one of the major obstacles to effectively diagnosing depression, as it prevents users from open conversations about their struggles. This requires advanced qu…
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond
Chen Huang, Yang Deng, Wenqiang Lei +3
With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with…
GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering
Qianlong Li, Chen Huang, Shuai Li +3
Complex Table Question Answering involves providing accurate answers to specific questions based on intricate tables that exhibit complex layouts and flexible header locations. Des…
Beyond Persuasion: Towards Conversational Recommender System with Credible Explanations
Peixin Qin, Chen Huang, Yang Deng +2
With the aid of large language models, current conversational recommender system (CRS) has gaining strong abilities to persuade users to accept recommended items. While these CRSs…
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models
Tong Zhang, Peixin Qin, Yang Deng +6
Large language models (LLMs) are increasingly used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity rema…
STYLE: Improving Domain Transferability of Asking Clarification Questions in Large Language Model Powered Conversational Agents
Yue Chen, Chen Huang, Yang Deng +4
Equipping a conversational search engine with strategies regarding when to ask clarification questions is becoming increasingly important across various domains. Attributing to the…
ARAIDA: Analogical Reasoning-Augmented Interactive Data Annotation
Chen Huang, Yiping Jin, Ilija Ilievski +2
Human annotation is a time-consuming task that requires a significant amount of effort. To address this issue, interactive data annotation utilizes an annotation model to provide s…
Co-Matching: Towards Human-Machine Collaborative Legal Case Matching
Chen Huang, Xinwei Yang, Yang Deng +3
Recent efforts have aimed to improve AI machines in legal case matching by integrating legal domain knowledge. However, successful legal case matching requires the tacit knowledge…
Concept -- An Evaluation Protocol on Conversational Recommender Systems with System-centric and User-centric Factors
Chen Huang, Peixin Qin, Yang Deng +3
The conversational recommendation system (CRS) has been criticized regarding its user experience in real-world scenarios, despite recent significant progress achieved in academia.…