Papers with CoBB
Learning to Correct for QA Reasoning with Black-box LLMs (2024.emnlp-main)
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| Challenge: | Existing approaches to improve reasoning capability of large language models rely on accessibility or require significantly increased train- and inference-time costs. |
| Approach: | They propose a method to improve QA reasoning of large language models in a black-box setting by using a trained adaptation model to perform a seq2seq mapping from the often-imperfect reasonings of the original LLM to the correct or improved reasonings. |
| Outcome: | The proposed approach significantly improves reasoning accuracy across various QA benchmarks compared to the best-performing adaptation baselines. |