From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning

• URL: http://arxiv.org/abs/2501.11877v1
• Date: Tue, 21 Jan 2025 04:11:59 GMT
• Title: From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning
• Authors: Yafu Li, Zhilin Wang, Tingchen Fu, Ganqu Cui, Sen Yang, Yu Cheng,
• Abstract summary: Scaling data and model size has been proven effective for boosting the performance of large language models. In this work, we introduce Aggregation Fine-Tuning (AFT), a supervised finetuning paradigm. Empirical evaluations on benchmark datasets show that AFT-trained models substantially outperform standard SFT.
• Score: 31.95005389919542
• License:
• Abstract: Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that increasing test-time computational resources can further improve performance. In this work, we introduce Aggregation Fine-Tuning (AFT), a supervised finetuning paradigm where the model learns to synthesize multiple draft responses, referred to as proposals, into a single, refined answer, termed aggregation. At inference time, a propose-and-aggregate strategy further boosts performance by iteratively generating proposals and aggregating them. Empirical evaluations on benchmark datasets show that AFT-trained models substantially outperform standard SFT. Notably, an AFT model, fine-tuned from Llama3.1-8B-Base with only 64k data, achieves a 41.3% LC win rate on AlpacaEval 2, surpassing significantly larger LLMs such as Llama3.1-405B-Instruct and GPT4. By combining sequential refinement and parallel sampling, the propose-and-aggregate framework scales inference-time computation in a flexible manner. Overall, These findings position AFT as a promising approach to unlocking additional capabilities of LLMs without resorting to increasing data volume or model size.

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