Summary of Api Is Enough: Conformal Prediction For Large Language Models Without Logit-access, by Jiayuan Su et al.
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access
by Jiayuan Su, Jing Luo, Hongwei Wang, Lu Cheng
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study proposes a novel Conformal Prediction (CP) method for large language models (LLMs) without access to logits, which is essential for API-only LLMs. The new approach minimizes prediction set sizes and provides a statistical guarantee of coverage. It combines coarse-grained sample frequency and fine-grained semantic similarity uncertainty notions to formulate nonconformity measures. Experimental results on close-ended and open-ended Question Answering tasks demonstrate that the proposed method outperforms logit-based CP baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study solves a big problem in using large language models (LLMs) without special access. It creates a new way to predict uncertainty, which is important for many applications. This new approach works even when we can’t see inside the model’s thinking. It also makes sure that predictions are accurate and reliable. The test results show that this method does better than previous methods. |
Keywords
* Artificial intelligence * Logits * Question answering
