Summary of Improving Instruction Following in Language Models Through Proxy-based Uncertainty Estimation, by Joonho Lee et al.
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
by JoonHo Lee, Jae Oh Woo, Juree Seok, Parisa Hassanzadeh, Wooseok Jang, JuYoun Son, Sima Didari, Baruch Gutow, Heng Hao, Hankyu Moon, Wenjun Hu, Yeong-Dae Kwon, Taehee Lee, Seungjai Min
First submitted to arxiv on: 10 May 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 proposed Uncertainty-aware Reward Model (URM) addresses the challenge of accurately assessing response quality in language models by introducing a robust uncertainty estimation based on Bayesian approximation. This novel approach scores rewards for responses while also evaluating their inherent uncertainty, enabling more effective training and optimization objectives. Empirical results demonstrate significant benefits, surpassing existing methods on benchmarks such as Vicuna and MT-bench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to improve language models by introducing an uncertainty-aware reward model. This means that the model can better understand when it’s unsure about its answers, which helps it learn from mistakes and become more accurate. The authors tested their approach on some benchmark datasets and found that it significantly outperformed other methods. |
Keywords
» Artificial intelligence » Optimization
