Papers › CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs

CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs

28 Jan 2025arXiv:2501.16629archive 2025-07-28

Jinlan Fu, Shenzhen Huangfu, Hao Fei, Xiaoyu Shen, Bryan Hooi, Xipeng Qiu, See-Kiong Ng

Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct Preference Optimization (DPO) to multimodal scenarios using preference pairs from text-based responses. However, our analysis of representation distributions reveals that multimodal DPO struggles to align image and text representations and to distinguish between hallucinated and non-hallucinated descriptions. To address these challenges, in this work, we propose a Cross-modal Hierarchical Direct Preference Optimization (CHiP) to address these limitations. We introduce a visual preference optimization module within the DPO framework, enabling MLLMs to learn from both textual and visual preferences simultaneously. Furthermore, we propose a hierarchical textual preference optimization module that allows the model to capture preferences at multiple granular levels, including response, segment, and token levels. We evaluate CHiP through both quantitative and qualitative analyses, with results across multiple benchmarks demonstrating its effectiveness in reducing hallucinations. On the Object HalBench dataset, CHiP outperforms DPO in hallucination reduction, achieving improvements of 52.7% and 55.5% relative points based on the base model Muffin and LLaVA models, respectively. We make all our datasets and code publicly available: https://github.com/LVUGAI/CHiP.

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combine_coco_captions lvugai/chip/eval/eval_gpt_obj_halbench.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 25585704d77cafb4 · report
expand_question_into_multimodal lvugai/chip/muffin/muffin/eval/inference.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · adb407bf7ff1f083 · report
parse_object_list lvugai/chip/eval/eval_gpt_obj_halbench.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 887addb3dd50b07e · report
preprocess_coh_results lvugai/chip/eval/eval_gpt_obj_halbench.py official repository ran · our draft was wrong Apache-2.0 (permissive) · f7745bc5cafc18b6 · report
torch_pad_sequence lvugai/chip/muffin/muffin/eval/inference.py official repository ran · honoured contract Apache-2.0 (permissive) · 86450c9db0fb7ad9 · report
chip_get_batch_logps lvugai/chip/llava/train/llava_trainer.py official repository unverified Apache-2.0 (permissive) · 6c5838ce1b68d726 · report

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Hallucination

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ALIGNBASEDPO

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