Summary of Human Alignment Of Large Language Models Through Online Preference Optimisation, by Daniele Calandriello et al.
Human Alignment of Large Language Models through Online Preference Optimisation
by Daniele Calandriello, Daniel Guo, Remi Munos, Mark Rowland, Yunhao Tang, Bernardo Avila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot
First submitted to arxiv on: 13 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper demonstrates the equivalence between two human alignment methods for language models: Identity Policy Optimisation (IPO) and Nash Mirror Descent (NMD). It also introduces a generalization of IPO, called IPO-MD, which combines the regularized sampling approach from NMD. These methods aim to ensure that language model outputs align with human preferences, leading to a more pleasant user experience. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows that two alignment methods for language models are actually the same thing! It also creates a new way to do this alignment that uses a special kind of sampling. This is important because it helps make sure language models produce helpful and safe results that people will like using. |
Keywords
* Artificial intelligence * Alignment * Generalization * Language model