Papers by Rohan Surana

2 papers
Image Difference Captioning via Adversarial Preference Optimization (2025.emnlp-main)

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Challenge: Existing supervised approaches to image difference captioning overfit to dataset-specific language patterns and fail to capture accurate preferences.
Approach: They propose an adversarial direct preference optimization framework that aligns captioning policy with pairwise difference preferences via Direct Preference Optimization.
Outcome: The proposed approach outperforms baselines on benchmark IDC datasets in generating fine-grained and accurate difference descriptions.
Evaluating Language Model Pluralism through In-the-wild Crowd Discussions (2026.acl-long)

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Challenge: Existing evaluation methods focus predominantly on multiple-choice and question-answering tasks, leaving open-ended generation largely unaddressed.
Approach: They propose an evaluation framework that assesses LLM pluralism in open-ended generation by comparing outputs against free-form crowd responses.
Outcome: The proposed evaluation framework decomposes ground-truth responses into atomic, non-overlapping claims and evaluates whether LLMs adequately cover this diverse claim space.

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