Regression-aware Inference with LLMs
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912100449779712 |
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| author | Lukasik, Michal Narasimhan, Harikrishna Menon, Aditya Krishna Yu, Felix Kumar, Sanjiv |
| author_facet | Lukasik, Michal Narasimhan, Harikrishna Menon, Aditya Krishna Yu, Felix Kumar, Sanjiv |
| contents | Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model's output distribution. We show that this inference strategy can be sub-optimal for common regression and scoring evaluation metrics. As a remedy, we build on prior work on Minimum Bayes Risk decoding, and propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. We show that our proposal significantly improves over baselines across datasets and models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04182 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Regression-aware Inference with LLMs Lukasik, Michal Narasimhan, Harikrishna Menon, Aditya Krishna Yu, Felix Kumar, Sanjiv Computation and Language Artificial Intelligence Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model's output distribution. We show that this inference strategy can be sub-optimal for common regression and scoring evaluation metrics. As a remedy, we build on prior work on Minimum Bayes Risk decoding, and propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. We show that our proposal significantly improves over baselines across datasets and models. |
| title | Regression-aware Inference with LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2403.04182 |