8 citations · 12 across the 7 of their papers we have counts for
8 papers
Beyond Outcome Rewards: Constructing and Assigning Retrieval Credit for Search Agents
Wenyu Huang, Xinyu Hou, Pavlos Vougiouklis +2
Search agents enable Large Language Models (LLMs) to iteratively retrieve and use information for complex multi-hop questions. Reinforcement Learning with Verifiable Rewards (RLVR)…
Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
Yiming Du, Baojun Wang, Yifan Xiang +11
Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue hi…
Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges
Hongru Wang, Wenyu Huang, Yufei Wang +7
Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on stateless, single-turn interactions or partial evaluations, such as t…
Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation
Wenyu Huang, Pavlos Vougiouklis, Mirella Lapata +1
Multi-hop Question Answering (MHQA) adds layers of complexity to question answering, making it more challenging. When Language Models (LMs) are prompted with multiple search result…
Rethinking Memory in LLM based Agents: Representations, Operations, and Emerging Topics
Yiming Du, Wenyu Huang, Danna Zheng +5
Memory is fundamental to large language model (LLM)-based agents, but existing surveys emphasize application-level use (e.g., personalized dialogue), while overlooking the atomic o…
Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts
Wenyu Huang, Guancheng Zhou, Mirella Lapata +3
Although Large Language Models (LLMs) are effective in performing various NLP tasks, they still struggle to handle tasks that require extensive, real-world knowledge, especially wh…
UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems
Hongru Wang, Wenyu Huang, Yang Deng +6
Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-…
Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models
Tianyi Li, Wenyu Huang, Nikos Papasarantopoulos +2
We present a system for knowledge graph population with Language Models, evaluated on the Knowledge Base Construction from Pre-trained Language Models (LM-KBC) challenge at ISWC 20…