Summary of Bi-factorial Preference Optimization: Balancing Safety-helpfulness in Language Models, by Wenxuan Zhang et al.
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models
by Wenxuan Zhang, Philip H.S. Torr, Mohamed Elhoseiny, Adel Bibi
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 paper proposes a novel supervised learning framework called Bi-Factorial Preference Optimization (BFPO) to fine-tune large language models (LLMs) while ensuring their safety. Existing methods use reinforcement learning from human feedback, which can lead to conflicts between safety and helpfulness. BFPO re-parameters the joint objective into a single supervised learning problem, using a labeling function to capture global preferences. The framework is evaluated on a comprehensive benchmark of discriminative and generative tasks for helpfulness and harmlessness. Results show that BFPO outperforms existing approaches in both safety and helpfulness, while requiring less computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make language models safer without needing humans to help train them. Currently, people use reinforcement learning from human feedback to fine-tune these models, but this can cause problems because it’s hard to balance keeping the model safe with helping it be helpful. The new method, called Bi-Factorial Preference Optimization (BFPO), uses a single goal that combines safety and helpfulness. It then trains the model using labeled data and achieves the same level of safety as other methods, but much faster. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning from human feedback » Supervised
