Papers by Dhruv Madeka
A Study on the Calibration of In-context Learning (2024.naacl-long)
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Hanlin Zhang, YiFan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric Xing, Himabindu Lakkaraju, Sham Kakade
| Challenge: | Prior research has demonstrated improvements in the calibration of language models (LMs) in-context learning is a popular method for adapting static LMs to safety-critical domains. |
| Approach: | They use in-context learning to adapt static language models through tailored prompts to a wide range of tasks and find that miscalibration occurs in low-shot settings. |
| Outcome: | The proposed calibrations show that models exhibit increased miscalibration before achieving better calibration in low-shot settings. |