Summary of Regression-aware Inference with Llms, by Michal Lukasik et al.
Regression-aware Inference with LLMs
by Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel approach to large language model (LLM) inference is proposed to optimize regression and scoring tasks, building upon Minimum Bayes Risk decoding. The authors demonstrate that the traditional autoregressive sampling strategy can be sub-optimal for common evaluation metrics. Instead, they introduce closed-form estimation methods to compute the Bayes-optimal solution from sampled responses. These alternate strategies are shown to significantly outperform baselines across various datasets and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have been successful in many tasks. Usually, we get answers from these models by taking a guess based on what they think is most likely to happen. This isn’t always the best way to get accurate results, especially for certain types of problems. The authors of this paper suggest new ways to get better answers from these models. They use ideas from something called Minimum Bayes Risk decoding and show that their methods work better than usual approaches on different datasets and using different models. |
Keywords
» Artificial intelligence » Autoregressive » Inference » Large language model » Regression
