Papers by Chetan Arora
microCLIP: Unsupervised CLIP Adaptation via Coarse-Fine Token Fusion for Fine-Grained Image Classification (2026.findings-acl)
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| Challenge: | Existing UA methods for fine-grained image classification rely on coarse-grain visual tokens, which misses fine spatial details. |
| Approach: | They propose a label-free self-training framework that adapts visual features and LLMderived text prototypes using fine-grained cues. |
| Outcome: | The proposed framework improves alignment between finegrained visual regions and rich textual descriptions while updating only layer norms and a tiny head. |
LePREC: Reasoning as Classification over Structured Factors for Assessing Relevance of Legal Issues (2026.acl-long)
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Fanyu Wang, Xiaoxi Kang, Paul Burgess, Aashish Srivastava, Chetan Arora, Adnan Trakic, Lay-Ki Soon, Md Khalid Hossain, Lizhen Qu
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities, but their precision remains inadequate. |
| Approach: | They propose a framework that integrates neural generation with statistical reasoning to improve the accuracy of large language models. |
| Outcome: | The proposed framework achieves interpretability through transparent feature weighting while maintaining data efficiency through correlation-based statistical classification. |