| How far can camels go? exploring the state of instruction tuning on open resources Y Wang, H Ivison, P Dasigi, J Hessel, T Khot, K Chandu, D Wadden, ... Advances in Neural Information Processing Systems 36, 74764-74786, 2023 | 213 | 2023 |
| The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 137 | 2021 |
| A survey of code-switched speech and language processing S Sitaram, KR Chandu, SK Rallabandi, AW Black arXiv preprint arXiv:1904.00784, 2019 | 130 | 2019 |
| The unlocking spell on base llms: Rethinking alignment via in-context learning BY Lin, A Ravichander, X Lu, N Dziri, M Sclar, K Chandu, C Bhagavatula, ... The Twelfth International Conference on Learning Representations, 2023 | 79 | 2023 |
| " Answer ka type kya he?" Learning to Classify Questions in Code-Mixed Language KC Raghavi, MK Chinnakotla, M Shrivastava Proceedings of the 24th International Conference on World Wide Web, 853-858, 2015 | 76 | 2015 |
| Rewardbench: Evaluating reward models for language modeling N Lambert, V Pyatkin, J Morrison, LJ Miranda, BY Lin, K Chandu, N Dziri, ... arXiv preprint arXiv:2403.13787, 2024 | 63 | 2024 |
| Grounding'grounding'in NLP KR Chandu, Y Bisk, AW Black arXiv preprint arXiv:2106.02192, 2021 | 60 | 2021 |
| Dolma: An open corpus of three trillion tokens for language model pretraining research L Soldaini, R Kinney, A Bhagia, D Schwenk, D Atkinson, R Authur, ... arXiv preprint arXiv:2402.00159, 2024 | 58 | 2024 |
| Olmo: Accelerating the science of language models D Groeneveld, I Beltagy, P Walsh, A Bhagia, R Kinney, O Tafjord, AH Jha, ... arXiv preprint arXiv:2402.00838, 2024 | 52 | 2024 |
| Code-mixed question answering challenge: Crowd-sourcing data and techniques K Chandu, E Loginova, V Gupta, J Genabith, G Neumann, M Chinnakotla, ... Third Workshop on Computational Approaches to Linguistic Code-Switching, 29-38, 2019 | 45 | 2019 |
| " My Way of Telling a Story": Persona based Grounded Story Generation S Prabhumoye, KR Chandu, R Salakhutdinov, AW Black arXiv preprint arXiv:1906.06401, 2019 | 39 | 2019 |
| Tackling biomedical text summarization: Oaqa at bioasq 5b K Chandu, A Naik, A Chandrasekar, Z Yang, N Gupta, E Nyberg BioNLP 2017, 58-66, 2017 | 27 | 2017 |
| Lumos: Learning agents with unified data, modular design, and open-source llms D Yin, F Brahman, A Ravichander, K Chandu, KW Chang, Y Choi, BY Lin arXiv preprint arXiv:2311.05657, 2023 | 25 | 2023 |
| Storyboarding of recipes: grounded contextual generation K Chandu, E Nyberg, AW Black Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 25 | 2019 |
| Language informed modeling of code-switched text K Chandu, T Manzini, S Singh, AW Black Proceedings of the Third Workshop on Computational Approaches to Linguistic …, 2018 | 24 | 2018 |
| WebShodh: A Code Mixed Factoid Question Answering System for Web KR Chandu, M Chinnakotla, AW Black, M Shrivastava Experimental IR Meets Multilinguality, Multimodality, and Interaction: 8th …, 2017 | 24 | 2017 |
| Speech Synthesis for Mixed-Language Navigation Instructions. KR Chandu, SK Rallabandi, S Sitaram, AW Black INTERSPEECH, 57-61, 2017 | 22 | 2017 |
| Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E L Soldaini, R Kinney, A Bhagia, D Schwenk, D Atkinson, R Authur, ... Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell …, 2024 | 20 | 2024 |
| Comparative Analysis of Neural QA models on SQuAD S Wadhwa, KR Chandu, E Nyberg Association for Computational Linguistics, 89–97, 2018 | 19 | 2018 |
| How far can camels go Y Wang, H Ivison, P Dasigi, J Hessel, T Khot, KR Chandu, D Wadden, ... Exploring the State of Instruction Tuning on Open Resources. CoRR, abs/2306 …, 2023 | 18 | 2023 |
Alan W BlackProfessor, Language Technologies Institute, Carnegie Mellon UniversityVerified email at cs.cmu.edu
