Summary of Measuring Memorization in Rlhf For Code Completion, by Aneesh Pappu et al.
Measuring memorization in RLHF for code completion
by Aneesh Pappu, Billy Porter, Ilia Shumailov, Jamie Hayes
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
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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 Reinforcement learning with human feedback (RLHF) has become a dominant method for aligning large language models to user preferences. Unlike fine-tuning, the effect of RLHF on training data memorization is unclear. Memorization can raise privacy concerns if real user data is collected and used during RLHF. Alternative methods like Direct Preference Optimization (DPO) and directly learn from human preferences, eliminating the need for intermediate reward models. This study analyzes how training data memorization affects each phase of RLHF and direct preference learning in code completion models. Results show that RLHF reduces memorization risk compared to fine-tuning, but pre-memorized examples remain memorized. In contrast, aligning by directly learning from human preferences via IPO increases the likelihood of regurgitating sensitive data. The study suggests that RLHF is a safer approach for mitigating the risk of regurgitating sensitive preference data when aligning large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how we can make sure large language models are aligned to what people want. Currently, the best way to do this is by using reinforcement learning with human feedback (RLHF). But it’s not clear if RLHF helps or hurts the problem of memorization – when a model remembers specific data instead of just understanding rules. Memorization can be bad because it means sensitive information could be shared. The researchers looked at three ways to align models: RLHF, another method called DPO, and a third one called . They found that RLHF is safer than the other two methods when it comes to memorization. This matters because we might collect real user data to use with large language models in the future. |
Keywords
» Artificial intelligence » Fine tuning » Likelihood » Optimization » Reinforcement learning » Rlhf
