Papers by Karan Taneja
Can Active Label Correction Improve LLM-based Modular AI Systems? (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are powerful zero or few-shot learners and can generalize to a wide range of tasks without any model fine-tuning. |
| Approach: | They propose to use LLM annotations to train smaller task-specific improved models that can replace LLMs. |
| Outcome: | The proposed method can improve oracle performance with feedback on 17-24% fewer examples than the number of noisy examples in the dataset across three different NLP tasks. |