2 citations · 2 across the 4 of their papers we have counts for
8 papers
Evaluating Chinese Ambiguity Understanding in Large Language Models
Junwen Mo, Yuanzhi Lu, Yifang Xue +2
Linguistic ambiguity is critical to the robustness of Large Language Models (LLMs), yet existing research focuses mostly on English, with limited attention devoted to Chinese. Exis…
Post Persona Alignment for Multi-Session Dialogue Generation
Yi-Pei Chen, Noriki Nishida, Hideki Nakayama +1
Multi-session persona-based dialogue generation presents challenges in maintaining long-term consistency and generating diverse, personalized responses. While large language models…
Exploring and Controlling Diversity in LLM-Agent Conversation
KuanChao Chu, Yi-Pei Chen, Hideki Nakayama
Controlling diversity in LLM-agent simulations is essential for balancing stability in structured tasks with variability in open-ended interactions. However, we observe that dialog…
Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialogues
KuanChao Chu, Yi-Pei Chen, Hideki Nakayama
This paper investigates the quality of multi-agent dialogues in simulations powered by Large Language Models (LLMs). Analyzing dialogues and memory over multiple sessions revealed…
Enhanced Data Transfer Cooperating with Artificial Triplets for Scene Graph Generation
KuanChao Chu, Satoshi Yamazaki, Hideki Nakayama
This work focuses on training dataset enhancement of informative relational triplets for Scene Graph Generation (SGG). Due to the lack of effective supervision, the current SGG mod…
A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization
KuanChao Chu, Yi-Pei Chen, Hideki Nakayama
This research investigates prompt designs of evaluating generated texts using large language models (LLMs). While LLMs are increasingly used for scoring various inputs, creating ef…