Papers by Sanjeev Khudanpur
Benchmarking Language Model Creativity: A Case Study on Code Generation (2025.naacl-long)
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| Challenge: | Recent studies on LLM creativity evaluation focus on open-ended generation tasks . however, the degree to which LLMs possess and utilize creativity for problem-solving remains unclear . |
| Approach: | They propose a framework for quantifying LLM creativity that incorporates design ingredients . they introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions . |
| Outcome: | The proposed framework quantifies creativity in LLMs on Codeforces problems . it also finds that even the most creative model fails to demonstrate human-like creativity . |
ConEC: Earnings Call Dataset with Real-world Contexts for Benchmarking Contextual Speech Recognition (2024.lrec-main)
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Ruizhe Huang, Mahsa Yarmohammadi, Jan Trmal, Jing Liu, Desh Raj, Leibny Paola Garcia, Alexei V. Ivanov, Patrick Ehlen, Mingzhi Yu, Dan Povey, Sanjeev Khudanpur
| Challenge: | Existing work on contextual speech recognition (ASR) systems focuses on recognizing words that are not frequently seen in training data, such as rare words, but word error rate on rare words remains over 20%. |
| Approach: | They propose to use public-domain earnings calls and supplementary materials to evaluate contextual ASR approaches grounded on real-world applications. |
| Outcome: | The proposed frameworks are noisier than artificially synthesized contexts that contain the ground truth, yet still make great room for future improvement of contextual ASR technology. |
Kreyòl-MT: Building MT for Latin American, Caribbean and Colonial African Creole Languages (2024.naacl-long)
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Nathaniel Robinson, Raj Dabre, Ammon Shurtz, Rasul Dent, Onenamiyi Onesi, Claire Monroc, Loïc Grobol, Hasan Muhammad, Ashi Garg, Naome Etori, Vijay Murari Tiyyala, Olanrewaju Samuel, Matthew Stutzman, Bismarck Odoom, Sanjeev Khudanpur, Stephen Richardson, Kenton Murray
| Challenge: | Creole languages are used in much of Latin America, Africa and the Caribbean . a large multilingual bitext like ours has potential to build the best yet or first ever MT models for many languages . |
| Approach: | They present the largest cumulative dataset to date for Creole language MT . they provide MT models supporting all 41 Creoles in 172 translation directions . |
| Outcome: | The proposed model outperforms a genre-specific Creole MT model on its own benchmark for 23 of 34 translation directions. |
Whisper-UT: A Unified Translation Framework for Speech and Text (2025.emnlp-main)
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Cihan Xiao, Matthew Wiesner, Debashish Chakraborty, Reno Kriz, Keith Cunningham, Kenton Murray, Kevin Duh, Luis Tavarez-Arce, Paul McNamee, Sanjeev Khudanpur
| Challenge: | Encoder-decoder models have achieved remarkable success in speech and text tasks, but efficiently adapting them to diverse uni/multimodal scenarios remains a challenge. |
| Approach: | They propose a framework that leverages lightweight adapters to enable seamless adaptation across tasks. |
| Outcome: | The proposed framework improves speech translation performance through a 2-stage decoding strategy without requiring 3-way parallel data. |
CSPB: Conversational Speech Processing Benchmark for Self-supervised Speech Models (2026.eacl-long)
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| Challenge: | Existing benchmarks focus on clean, single-speaker, single channel audio, failing to reflect the complexities of natural human interaction. |
| Approach: | They propose a benchmark to assess the robustness of self-supervised speech models in conversational settings. |
| Outcome: | The proposed benchmark assesses the robustness of self-supervised speech models in conversational scenarios. |