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Dive into the research topics where Danqi Chen is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
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Building Next-Generation Language Models based on Retrieval
Chen, D. (PI)
NSF - National Science Foundation
2/15/23 → 1/31/28
Project: Research project
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WORKSHOP: Doctoral consortium at Student Research Workshop at the North American Chapter of the Association for Computational Linguistics
Chen, D. (PI)
NSF - National Science Foundation
7/1/22 → 6/30/23
Project: Research project
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BRIGHT: A REALISTIC AND CHALLENGING BENCHMARK FOR REASONING-INTENSIVE RETRIEVAL
Su, H., Yen, H., Xia, M., Shi, W., Muennighoff, N., Wang, H. Y., Liu, H., Shi, Q., Siegel, Z. S., Tang, M., Sun, R., Yoon, J., Arık, S., Chen, D. & Yu, T., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 99001-99051 51 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
9 Link opens in a new tab Scopus citations -
FANTASTIC COPYRIGHTED BEASTS AND HOW (NOT) TO GENERATE THEM
He, L., Huang, Y., Shi, W., Xie, T., Liu, H., Wang, Y., Zettlemoyer, L., Zhang, C., Chen, D. & Henderson, P., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 36670-36697 28 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
2 Link opens in a new tab Scopus citations -
HELMET: HOW TO EVALUATE LONG-CONTEXT LANGUAGE MODELS EFFECTIVELY AND THOROUGHLY
Yen, H., Gao, T., Hou, M., Ding, K., Fleischer, D., Izsak, P., Wasserblat, M. & Chen, D., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 3473-3524 52 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
4 Link opens in a new tab Scopus citations -
How to Train Long-Context Language Models (Effectively)
Gao, T., Wettig, A., Yen, H. & Chen, D., 2025, Long Papers. Che, W., Nabende, J., Shutova, E. & Pilehvar, M. T. (eds.). Association for Computational Linguistics (ACL), p. 7376-7399 24 p. (Proceedings of the Annual Meeting of the Association for Computational Linguistics; vol. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Open Access -
SORRY-BENCH: SYSTEMATICALLY EVALUATING LARGE LANGUAGE MODEL SAFETY REFUSAL
Xie, T., Qi, X., Zeng, Y., Huang, Y., Sehwag, U. M., Huang, K., He, L., Wei, B., Li, D., Sheng, Y., Jia, R., Li, B., Li, K., Chen, D., Henderson, P. & Mittal, P., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 98469-98505 37 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
11 Link opens in a new tab Scopus citations