6 citations · 14 across the 21 of their papers we have counts for
12 papers · 1 filter
RouteRec: Strict Evaluation of Recommender-Agent Selection and Aggregation
Kaiji Zhou, Vladimir Kalmykov, Yue Feng
Recommender systems increasingly face a choice among heterogeneous agents -- collaborative filters, sequential models, content-based retrievers, and LLM-based rerankers -- yet no s…
ProReviewer: Evidence-Driven Investigation for Scientific Peer Review
Haishuo Fang, Yue Feng, Iryna Gurevych
Effective peer review requires adaptive investigation that responds to evidence emerging from each paper. Existing LLM-based reviewers largely organize the review process around pr…
When to Think Deeply: Inhibitory Deliberation for LLM Reasoning
Zhixuan He, Yue Feng
Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is computationally expensive and…
From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation
Mingfei Lu, Yi Zhang, Mengjia Wu +1
Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex t…
ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Ofer Meshi, Krisztian Balog, Sally Goldman +5
The promise of LLM-based user simulators to improve conversational AI is hindered by a critical "realism gap," leading to systems that are optimized for simulated interactions, but…
Robust Bias Evaluation with FilBBQ: A Filipino Bias Benchmark for Question-Answering Language Models
Lance Calvin Lim Gamboa, Yue Feng, Mark Lee
With natural language generation becoming a popular use case for language models, the Bias Benchmark for Question-Answering (BBQ) has grown to be an important benchmark format for…
Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
Zhixuan He, Yue Feng
Large Language Models (LLMs) demonstrate strong performance but often lack interpretable reasoning. This paper introduces the Multi-Agent Collaboration Framework for Diverse Thinki…
UoB-NLP at SemEval-2025 Task 11: Leveraging Adapters for Multilingual and Cross-Lingual Emotion Detection
Frances Laureano De Leon, Yixiao Wang, Yue Feng +1
Emotion detection in natural language processing is a challenging task due to the complexity of human emotions and linguistic diversity. While significant progress has been made in…
Self-Adaptive Cognitive Debiasing for Large Language Models in Decision-Making
Yougang Lyu, Shijie Ren, Yue Feng +4
Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal assistants in the financial, healthcare, and legal domains. W…
JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework
Fan Liu, Yue Feng, Zhao Xu +4
Despite advancements in enhancing LLM safety against jailbreak attacks, evaluating LLM defenses remains a challenge, with current methods often lacking explainability and generaliz…
Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents
Zhengliang Shi, Shen Gao, Lingyong Yan +6
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks. Previous methods ma…
Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals
Zhiyuan Hu, Yue Feng, Yang Deng +4
Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular…