Papers by Javen Qinfeng Shi
Truth as a Trajectory: What Internal Representations Reveal About Large Language Model Reasoning (2026.acl-long)
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| Challenge: | Existing explainability methods for Large Language Models treat hidden states as static points in activation space, but they are saturated with polysemantic features. |
| Approach: | They propose a framework that shifts analysis from static activations to layer-wise geometric displacement. |
| Outcome: | The proposed framework outperforms existing explainability methods on commonsense reasoning, question answering, and toxicity detection benchmarks. |
Semantic Role Labeling Guided Out-of-distribution Detection (2024.lrec-main)
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Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin, Haiyao Cao, Lingqiao Liu, Ehsan Abbasnejad, Javen Qinfeng Shi
| Challenge: | Existing methods for identifying domain-shifted instances are prone to OOD and adversarial inputs. |
| Approach: | They propose an unsupervised method that separates, extracts, and learns the semantic role labeling guided out-of-distribution Detection (SRLOOD) they propose a self-supervised approach to enhance global-local feature learning by predicting SRL extracted role. |
| Outcome: | The proposed method achieves SOTA performance on four OOD benchmarks. |