Chandu, Khyathi

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Natural Language Processing TechniquesTopic Modeling

Cited by 159

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Dolma: an open corpus of three trillion tokens for language model pretraining research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 56

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 49

Agent Lumos: Unified and Modular Training for Open-Source Language Agents

Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., & Lin, B. Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 17

RewardBench: Evaluating Reward Models for Language Modeling

Lambert, N., Pyatkin, V., Morrison, J., Miranda, L., Lin, B. Y., Chandu, K., Dziri, N., Kumar, S., Zick, T., Choi, Y., et al. (2025) · Findings of the Association for Computational Linguistics: NAACL 2025

Natural Language Processing TechniquesTopic Modeling

Cited by 13

The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning

Lin, B. Y., Ravichander, A., Lu, X., Dziri, N., Sclar, M., Chandu, K., Bhagavatula, C., & Choi, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research

Soldaini, L., Kinney, R., Bhagia, A., Schwenk, D., Atkinson, D., Authur, R., Bogin, B., Chandu, K., Dumas, J., Elazar, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A. H., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 10

RewardBench: Evaluating Reward Models for Language Modeling

Lambert, N., Pyatkin, V., Morrison, J., Miranda, L., Lin, B. Y., Chandu, K., Dziri, N., Kumar, S., Zick, T., Choi, Y., et al. (2024) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 5

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild

Lin, B. Y., Deng, Y., Chandu, K., Brahman, F., Ravichander, A., Pyatkin, V., Dziri, N., Bras, R. L., & Choi, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4