Papers by Shengjie Zhao
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
From Imitation to Introspection: Probing Self-Consciousness in Language Models (2025.findings-acl)
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| Challenge: | Existing language models demonstrate impressive abilities in areas like natural language understanding, content creation, and reasoning. |
| Approach: | They propose a definition of self-consciousness for language models and refine ten core concepts by leveraging structural causal games. |
| Outcome: | The proposed definitions are based on structural causal games and ten core concepts. |
AdapThink: Adaptive Thinking Preferences for Reasoning Language Models (2026.findings-acl)
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| Challenge: | Recent research has highlighted a significant inefficiency associated with the slow thinking paradigm . models often overthink simple tasks while underthinking complex challenges . |
| Approach: | They propose a framework for adaptive reasoning preference control that dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. |
| Outcome: | The proposed framework reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets. |