Summary of Abstract2appendix: Academic Reviews Enhance Llm Long-context Capabilities, by Shengzhi Li et al.
Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities
by Shengzhi Li, Kittipat Kampa, Rongyu Lin, Bohang Li, Shichao Pei
First submitted to arxiv on: 7 Nov 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 The paper investigates how to improve large language models’ ability to handle long-context reading tasks by fine-tuning them using high-quality academic peer review data. The researchers compare two methods: Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT). They demonstrate that DPO outperforms SFT in terms of data efficiency, achieving a 4.04-point improvement on the phi-3 benchmark and a 2.6% increase on the Qasper benchmark using only 2000 samples. The study highlights the potential of DPO and high-quality data in advancing LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to make language models better at understanding long texts by training them with good academic reviews. Two ways are tested: one that uses a special optimization method (DPO) and another that uses labeled data (SFT). The results show that DPO is more effective and efficient, making it a promising approach for improving language model performance. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Optimization » Supervised
