Summary of Conditional Language Policy: a General Framework For Steerable Multi-objective Finetuning, by Kaiwen Wang et al.
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
by Kaiwen Wang, Rahul Kidambi, Ryan Sullivan, Alekh Agarwal, Christoph Dann, Andrea Michi, Marco Gelmi, Yunxuan Li, Raghav Gupta, Avinava Dubey, Alexandre Ramé, Johan Ferret, Geoffrey Cideron, Le Hou, Hongkun Yu, Amr Ahmed, Aranyak Mehta, Léonard Hussenot, Olivier Bachem, Edouard Leurent
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 framework called Conditional Language Policy (CLP) is proposed to fine-tune language models on multiple objectives while trading off conflicting goals. This method, building upon multi-task training and parameter-efficient finetuning techniques, learns steerable models that adaptively balance different objectives during inference. The approach does not require maintaining or training separate models for distinct trade-offs between objectives. Experimental evaluations on two summarization datasets demonstrate the effectiveness of CLP in outperforming existing methods for multi-objective fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to make language models work better by combining different goals. This is useful because we want AI systems to be creative, safe, and follow rules. The authors create a framework called Conditional Language Policy (CLP) that can handle multiple conflicting objectives. This means the model learns to balance different goals, like being creative and following rules. The approach doesn’t require training many models for different scenarios. |
Keywords
* Artificial intelligence * Fine tuning * Inference * Multi task * Parameter efficient * Summarization
