Summary of Multi-objective Alignment Of Large Language Models Through Hypervolume Maximization, by Subhojyoti Mukherjee et al.
Multi-Objective Alignment of Large Language Models Through Hypervolume Maximization
by Subhojyoti Mukherjee, Anusha Lalitha, Sailik Sengupta, Aniket Deshmukh, Branislav Kveton
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 an algorithm called HaM for learning diverse large language model (LLM) policies that maximize their hypervolume in multi-objective alignment from human feedback (MOAHF). The problem of MOAHF is challenging because human preferences are complex and often conflicting. Recent works have used a-priori multi-objective optimization, but this approach assumes known human preferences at training or inference time. HaM instead covers the Pareto front by multiple diverse solutions when human preferences are unknown or difficult to quantify. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper is about teaching large language models to understand and follow complex human feedback. The goal is to find a balance between different objectives such as being harmless, helpful, humorous, faithful, and not hallucinating. The proposed algorithm, HaM, is efficient and effective in achieving this balance across different datasets. |
Keywords
» Artificial intelligence » Alignment » Inference » Large language model » Optimization
