Papers › Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization

Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization

14 Aug 2024arXiv:2408.07471archive 2025-07-28

Yuxin Jiang, Bo Huang, YuFei Wang, Xingshan Zeng, Liangyou Li, Yasheng Wang, Xin Jiang, Lifeng Shang, Ruiming Tang, Wei Wang

Direct preference optimization (DPO), a widely adopted offline preference optimization algorithm, aims to align large language models (LLMs) with human-desired behaviors using pairwise preference data. However, the winning response and the losing response within pairwise data are generated isolatedly, leading to weak correlations between them as well as suboptimal alignment performance. To address this issue, we propose an effective framework for Bridging and Modeling Correlations in pairwise data, named BMC. Firstly, we increase the consistency and informativeness of the pairwise preference signals through targeted modifications, synthesizing a pseudo-winning response by improving the losing response with the winning response as a reference. Secondly, we identify that DPO alone is insufficient to model these correlations and capture nuanced variations. Therefore, we propose learning token-level correlations by dynamically leveraging the policy model's confidence during training. Comprehensive experiments on QA, math, and instruction-following tasks demonstrate the effectiveness of our approach, significantly surpassing competitive baselines, including DPO. Additionally, our in-depth quantitative analysis reveals the reasons behind our method's superior performance over DPO and showcases its versatility to other DPO variants. We release our repository at https://github.com/YJiangcm/BMC.

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call_llm_completion YJiangcm/BMC/eval/QA/ARC/eval_coh.py official repository ran Apache-2.0 (permissive) · ff720f8898acef89 · report
generate_completions YJiangcm/BMC/eval/math/eval/model_utils.py official repository ran Apache-2.0 (permissive) · 1525d8bc0fc8d5a0 · report
get_json_list YJiangcm/BMC/eval/alpacaeval2/alpaca_eval_inference.py official repository ran Apache-2.0 (permissive) · 7dfbedc27279839f · report
get_json_list2 YJiangcm/BMC/eval/arenahard/arena_hard_inference.py official repository ran Apache-2.0 (permissive) · d18bb47e880446f4 · report
load_demo YJiangcm/BMC/eval/QA/ARC/eval_coh.py official repository ran Apache-2.0 (permissive) · fc47045d127ed8bc · report
load_jsonl_data YJiangcm/BMC/eval/QA/ARC/eval_coh.py official repository ran Apache-2.0 (permissive) · 8281e63b913995ed · report
shuffle YJiangcm/BMC/eval/math/prepare_train_data.py official repository ran Apache-2.0 (permissive) · c35f14527d73a1ed · report
load_hf_lm_and_tokenizer YJiangcm/BMC/eval/math/eval/model_utils.py official repository unverified Apache-2.0 (permissive) · ee71a94d9dd1b8e9 · report

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InformativenessInstruction FollowingMath

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ALIGNDPO

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