5 citations · 18 across the 12 of their papers we have counts for
14 papers
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…
ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis
Zezhong Wang, Xingshan Zeng, Weiwen Liu +6
Supervised fine-tuning (SFT) is a common method to enhance the tool calling capabilities of Large Language Models (LLMs), with the training data often being synthesized. The curren…
ToolACE: Winning the Points of LLM Function Calling
Weiwen Liu, Xu Huang, Xingshan Zeng +24
Function calling significantly extends the application boundary of large language models, where high-quality and diverse training data is critical for unlocking this capability. Ho…
DIGAT: Modeling News Recommendation with Dual-Graph Interaction
Zhiming Mao, Jian Li, Hongru Wang +2
News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations.…
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
Lingzhi Wang, Shafiq Joty, Wei Gao +2
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of gene…
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
Zhiming Mao, Xingshan Zeng, Kam-Fai Wong
Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective represen…