Summary of Logo — Long Context Alignment Via Efficient Preference Optimization, by Zecheng Tang et al.
LOGO – Long cOntext aliGnment via efficient preference Optimization
by Zecheng Tang, Zechen Sun, Juntao Li, Qiaoming Zhu, Min Zhang
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Long-context models (LCMs) have made significant progress in processing long input sequences, accurately locating salient information within context. However, their generation performance is still limited, often resulting in misaligned responses like hallucinations. To improve LCMs’ generation capabilities, researchers have investigated the effects of data size and quality for pre-training and instruction tuning. While previous methods achieved some improvement, they fell short in either effectiveness or efficiency. This paper introduces LOGO (Long cOntext aliGnment via efficient preference Optimization), a training strategy that optimizes long-context alignment using reference-free preference optimization and position synthesis to construct training data. By training with 0.3B data on a single GPU machine for 16 hours, LOGO enables the Llama-3-8B-Instruct-80K model to match GPT-4’s performance in real-world long-context tasks while preserving its original capabilities. Additionally, LOGO extends the model’s context window size and enhances generation performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research focuses on improving a type of artificial intelligence called long-context models. These models are great at understanding very long pieces of text, but they can also make mistakes when generating new text. The team has developed a new approach called LOGO that helps the models generate more accurate and helpful responses. They tested their method using 0.3 billion pieces of data on a single computer for 16 hours. This allowed the model to perform as well as other, more advanced models in certain tasks while still being able to understand long pieces of text. |
Keywords
» Artificial intelligence » Alignment » Context window » Gpt » Instruction tuning » Llama » Optimization
