Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities
This work addresses the problem of limited long-context handling in LLMs for researchers and developers, though it is incremental as it builds on existing fine-tuning methods.
This study tackled the challenge of enhancing large language models' long-context capabilities by fine-tuning them with academic peer review data, showing that Direct Preference Optimization outperforms Supervised Fine-Tuning with a 4.04-point improvement over phi-3 and a 2.6% increase on the Qasper benchmark using only 2000 samples.
Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compare the Direct Preference Optimization (DPO) method with the Supervised Fine-Tuning (SFT) method, demonstrating DPO's superiority and data efficiency. Our experiments show that the fine-tuned model achieves a 4.04-point improvement over phi-3 and a 2.6\% increase on the Qasper benchmark using only 2000 samples. Despite facing limitations in data scale and processing costs, this study underscores the potential of DPO and high-quality data in advancing LLM performance. Additionally, the zero-shot benchmark results indicate that aggregated high-quality human reviews are overwhelmingly preferred over LLM-generated responses, even for the most capable models like GPT-4o. This suggests that high-quality human reviews are extremely rich in information, reasoning, and long-context retrieval, capabilities that even the most advanced models have not fully captured. These findings highlight the high utility of leveraging human reviews to further advance the field.