7 papers
GRAPHMOE: Amplifying Cognitive Depth of Mixture-of-Experts Network via Introducing Self-Rethinking Mechanism
Bo Lv, Chen Tang, Zifan Zheng +8
Traditional Mixture-of-Experts (MoE) networks benefit from utilizing multiple smaller expert models as opposed to a single large network. However, these experts typically operate i…
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework
Bohao Yang, Dong Liu, Chenghao Xiao +6
Large Language Models (LLMs) demonstrate remarkable ability to comprehend instructions and generate human-like text, enabling sophisticated agent simulation beyond basic behavior r…
Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation
Bohao Yang, Kun Zhao, Dong Liu +3
Automatic open-domain dialogue evaluation has attracted increasing attention, yet remains challenging due to the complexity of assessing response appropriateness. Traditional evalu…
Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
Shun Wang, Tyler Loakman, Youbo Lei +5
Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on do…
EvolvTrip: Enhancing Literary Character Understanding with Temporal Theory-of-Mind Graphs
Bohao Yang, Hainiu Xu, Jinhua Du +3
A compelling portrayal of characters is essential to the success of narrative writing. For readers, appreciating a character's traits requires the ability to infer their evolving b…
DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation
Kun Zhao, Bohao Yang, Chen Tang +4
Large Language Models (LLMs) excel at many tasks but struggle with ambiguous scenarios where multiple valid responses exist, often yielding unreliable results. Conversely, Small La…