Discriminative Finetuning of Generative Large Language Models without Reward Models and Preference Data

• URL: http://arxiv.org/abs/2502.18679v1
• Date: Tue, 25 Feb 2025 22:38:55 GMT
• Title: Discriminative Finetuning of Generative Large Language Models without Reward Models and Preference Data
• Authors: Siqi Guo, Ilgee Hong, Vicente Balmaseda, Tuo Zhao, Tianbao Yang,
• Abstract summary: Supervised fine-tuning (SFT) followed by preference optimization (PO) has become the standard for improving pretrained large language models (LLMs)<n>We introduce Discriminative Fine-Tuning (DFT), a novel approach that eliminates the need for preference data.<n>Our contributions include: (i) a discriminative probabilistic framework for fine-tuning LLMs by explicitly modeling the discriminative likelihood of an answer; (ii) efficient algorithms to optimize this discriminative likelihood; and (iii) extensive experiments demonstrating DFT's effectiveness, achieving performance better than SFT and comparable to if not
• Score: 61.463946150106054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised fine-tuning (SFT) followed by preference optimization (PO) denoted by SFT$\rightarrow$PO has become the standard for improving pretrained large language models (LLMs), with PO demonstrating significant performance gains. However, PO methods rely on either human-labeled preference data or a strong reward model to generate preference data. Can we fine-tune LLMs without preference data or reward models while achieving competitive performance to SFT$\rightarrow$PO? We address this question by introducing Discriminative Fine-Tuning (DFT), a novel approach that eliminates the need for preference data. Unlike SFT, which employs a generative approach and overlooks negative data, DFT adopts a discriminative paradigm that that increases the probability of positive answers while suppressing potentially negative ones, shifting from token prediction to data prediction. Our contributions include: (i) a discriminative probabilistic framework for fine-tuning LLMs by explicitly modeling the discriminative likelihood of an answer among all possible outputs given an input; (ii) efficient algorithms to optimize this discriminative likelihood; and (iii) extensive experiments demonstrating DFT's effectiveness, achieving performance better than SFT and comparable to if not better than SFT$\rightarrow$PO. The code can be found at https://github.com/PenGuln/DFT.

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