Papers by Liang Xuefeng
TRACE: Two-Phase RL for Causal Graph Exploration and Deeper Psychological Intervention in Dynamic Counseling Scenarios (2026.findings-acl)
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| Challenge: | Existing models lack the ability to actively explore the underlying causes of psychological distress. |
| Approach: | They propose a two-phase reinforcement learning framework that implements a causal-graph-driven reward scheme across two phases: an exploration phase that rewards the causal graph reconstruction following a surface-to-deep path, and an intervention phase that supports targeted restructuring of irrational beliefs. |
| Outcome: | Extensive experiments show that TRACE outperforms existing models, enabling causal-chain-aware psychological intervention beyond surface-level empathy. |
Interpreting Positional Information in Perspective of Word Order (2023.acl-long)
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| Challenge: | Attention mechanism is a powerful and effective method utilized in natural language processing, but it is insensitive to positional information. |
| Approach: | They propose a weight concatenation operation to evaluate its efficacy in machine translation tasks. |
| Outcome: | The proposed operation can encode positional information and confirms our hypothesis. |