2 citations · 2 across the 3 of their papers we have counts for
5 papers
Behaviour analysis for large language model-driven agents
Tianqiang Yan, Lizhen Qu
Understanding language-agent behaviour requires learning how actions are organised across a trajectory and how this organisation relates to context and outcomes. We propose a gener…
Trajectory Abstraction for the Science of Language Agent Behavior
Tianqiang Yan
Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem as learning and testing a hierar…
ReBeCA: Causality-Inspired Behavior Analysis for Understanding the Self-Reflection of Language Models
Tianqiang Yan, Lizhen Qu, Sihan Shang +6
Self-reflection is a basic self-improvement skill of language agents, yet it does not reliably improve their outputs, where the underlying behavioral mechanism remains opaque. We i…
DynaMIC: Dynamic Multimodal In-Context Learning Enabled Embodied Robot Counterfactual Resistance Ability
Tianqiang Yan, Ziqiao Lin, Sicheng Wang +2
The emergence of large pre-trained models based on natural language has breathed new life into robotics development. Extensive research has integrated large models with robots, uti…
Refining the Responses of LLMs by Themselves
Tianqiang Yan, Tiansheng Xu
In this paper, we propose a simple yet efficient approach based on prompt engineering that leverages the large language model itself to optimize its answers without relying on auxi…