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Janet Pierrehumbert

As seen in: arXiv, PNAS
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Articles

Derivational morphology reveals analogical generalization in large language models

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By J. Bybee, Janet Pierrehumbert, Kelsey L Crutchfield-Peters, Daniella Rempe
| dx.doi.org
Contributed by Janet B. Pierrehumbert; received November 20, 2024; accepted February 18, 2025; reviewed by Joan Bresnan and Gary Dell This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2019. In the recent past, large language models (LLMs) such as Chinchilla (1), Gemini (2), GPT-4 (3), LLaMA (4), Mistral (5), OLMo (6), and PaLM (7) have reached an unprecedented level of linguistic capability.
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Derivational morphology reveals analogical generalization in large language models

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By J. Bybee, Janet Pierrehumbert, Kelsey L Crutchfield-Peters, Daniella Rempe
| PNAS Verified
A. Chowdhery et al., PaLM: Scaling language modeling with pathways. J. Mach. Learn. Res. 24, 1–113 (2023). E. M. Bender, A. Koller, “Climbing towards NLU: On meaning, form, and understanding in the age of data” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, D. Jurafsky, J. Chai, N. Schluter, J. Tetreault, Eds. (Association for Computational Linguistics, 2020), pp. 5185–5198. R.
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When Dimensionality Hurts: The Role of LLM Embedding Compression for Noisy Regression Tasks

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By Janet Pierrehumbert, Stefan Zohren
| arXiv
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