Papers by Koyel Mukherjee
From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)
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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley
| Challenge: | Large Language Models (LLMs) have been used for selection and training of data for active learning. |
| Approach: | They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop. |
| Outcome: | The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances. |
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from related Example Banks (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive few-shot learning capabilities through in-context learning. |
| Approach: | They propose a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. |
| Outcome: | The proposed approach outperforms existing frameworks for retrieving examples on low-resource Indic languages. |
FiRST: Finetuning Router-Selective Transformers for Input-Adaptive Latency Reduction (2025.findings-emnlp)
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| Challenge: | Existing approaches to improve latency via skipping layers have limitations . fiRST is a model-agnostic framework that reduces inference latency while maintaining quality . |
| Approach: | They propose a model-agnostic framework that skips transformer layers during decoding . it is fully compatible with KV caching, enabling faster decoding while maintaining quality . |
| Outcome: | a new framework reduces inference latency by using layer-specific routers to skip transformer layers during decoding. |
Masking or Mitigating? Deconstructing the Impact of Query Rewriting on Retriever Biases in RAG (2026.findings-acl)
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| Challenge: | Query enhancement techniques are now standard in retrieval-augmented generation systems, yet their impact on these biases remains unexplored. |
| Approach: | They evaluate query enhancement techniques that improve retrieval quality . they find that simple rewriting reduces bias through increased score variance . no technique uniformly addresses all biases, and effects vary substantially across retrievers . |
| Outcome: | The proposed method achieves strongest aggregate reduction, but fails under adversarial conditions where multiple biases combine. |
TTD-SQL: Tree-Guided Token Decoding for Efficient and Schema-Aware SQL Generation (2025.emnlp-industry)
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Chetan Sharma, Ramasuri Narayanam, Soumyabrata Pal, Kalidas Yeturu, Shiv Kumar Saini, Koyel Mukherjee
| Challenge: | Large language models (LLMs) have achieved state-of-the-art accuracy on benchmarks like Spider and BIRD, but inference latency due to sequential autoregressive decoding remains a challenge for real-time deployments. |
| Approach: | a new framework integrates SQL grammar and database schema constraints into the decoding process . tree-Guided Token Decoding (TTD-SQL) precomputes token-level decision trees over SQL keywords, table names, and column identifiers . |
| Outcome: | a new framework reduces schema hallucinations and inference latency due to autoregressive decoding . tree-Guided Token Decoding achieves 19.96% token-rate speedups . |
An Answer is just the Start: Related Insight Generation for Open-Ended Document-Grounded QA (2026.findings-acl)
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| Challenge: | Existing QA benchmarks do not explicitly support document-grounded related insight generation . Existing document-based QA efforts focus on answering fact-based questions . |
| Approach: | They propose a task to generate additional insights from a document collection that improves, extends or rethinks an initial answer to an open-ended question. |
| Outcome: | The proposed task improves, extends, or rethinks an answer to an open-ended question. |