1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…
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…
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-…
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…