1 citations · 1 across the 6 of their papers we have counts for
10 papers · 1 filter
ToSCA: Leveraging Hierarchical Reinforcement Learning on Temporal and Strategic Abstractions of Conversational Agents
Xiaoyu Wang, Qingqing Gu, Yue Zhao +5
Humans naturally exhibit multiple forms of abstraction in reasoning and interaction, including temporal abstraction across decision timescales and strategic abstraction over commun…
Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
Luo Ji, Qi Qin, Ningyuan Xi +3
Conventional LLMs may suffer from corpus heterogeneity and subtle condition changes. While finetuning can create the catastrophe forgetting issue, application of meta-learning on L…
Chain-of-Conceptual-Thought Elicits Daily Conversation in Large Language Models
Qingqing Gu, Dan Wang, Yue Zhao +5
Chain-of-Thought (CoT) is widely applied to enhance the LLM capability in math, coding and reasoning tasks. However, its performance is limited for open-domain tasks, when there ar…
Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling
Yue Zhao, Xiaoyu Wang, Dan Wang +7
World models have been widely utilized in robotics, gaming, and auto-driving. However, their applications on natural language tasks are relatively limited. In this paper, we constr…
Making Language Model a Hierarchical Classifier
Yihong Wang, Zhonglin Jiang, Ningyuan Xi +8
Decoder-only language models, such as GPT and LLaMA, generally decode on the last layer. Motivated by human's hierarchical thinking capability, we propose that a hierarchical decod…
Convert Language Model into a Value-based Strategic Planner
Xiaoyu Wang, Yue Zhao, Qingqing Gu +4
Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations. Although large language models (LLMs) have obtained re…