Papers by Shirish Karande
Understanding Advertisements with BERT (2020.acl-main)
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| Challenge: | Recent results have shown that the embedded scene-text in the image holds a vital cue for this task. |
| Approach: | They propose to use the embedded scene-text as a cue for a sentence-pair classification task based on CVPR 2018 challenge dataset on advertisement understanding to rank valid and negatively sampled invalid interpretations of an image. |
| Outcome: | The proposed model achieves 89.69% accuracy, an improvement of 4.7% on the previous model. |
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training (D19-61)
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Mayur Patidar, Surabhi Kumari, Manasi Patwardhan, Shirish Karande, Puneet Agarwal, Lovekesh Vig, Gautam Shroff
| Challenge: | Recent research on cross-lingual transfer shows state-of-the-art results on benchmark datasets using pre-trained language representation models like BERT. |
| Approach: | They propose a method to augment an annotated dataset with machine translations in target languages and fine-tune the PLRM jointly. |
| Outcome: | The proposed approach provides consistent gains on multiple benchmark datasets while requiring a single model for multiple languages. |
POLARIS: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair (2026.findings-acl)
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| Challenge: | Gödel agent Polaris makes policy level changes with small, auditable patches that persist in the policy and are reused on unseen instances within each benchmark. |
| Approach: | They propose a Gödel agent that performs policy repair via experience abstraction . Polaris makes policy level changes with small, auditable patches that persist in the policy . |
| Outcome: | The proposed agent improves on MGSM, DROP, GPQA, and LitBench models over the base policy and competitive baselines. |
Nine Ways to Break Copyright Law and Why Our LLM Won’t: A Fair Use Aligned Generation Framework (2025.findings-emnlp)
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Aakash Sen Sharma, Debdeep Sanyal, Priyansh Srivastava, Sundar Athreya H, Shirish Karande, Mohan Kankanhalli, Murari Mandal
| Challenge: | Large language models (LLMs) often risk copyright infringement by reproducing protected content verbatim or with insufficient transformative modifications. |
| Approach: | They propose a legally-grounded framework to align LLM outputs with fair-use doctrine . LAW-LM uses a dataset containing 18,000 expert-validated examples . |
| Outcome: | The proposed framework aligns outputs with fair-use doctrine and is validated by 18,000 experts. |