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Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs

ICLR 2025 · Vision-language and multimodal › Multimodal LLMs · approach: Preference tuning against hallucination · 0 citations (OpenAlex)

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Introduces MM-SY, a benchmark for sycophancy in VLMs built from TDIUC, compares prompt, SFT, and DPO mitigations on LLaVA-1.5, and proposes a training-free high-layer visual attention amplification.

Problem

Sycophancy, where VLMs abandon correct visual answers to agree with incorrect user opinions, has been little studied compared with LLMs; the paper asks whether VLMs still sycophantically agree when given images as evidence.

Approach

Builds a two-round multiple-choice VQA sycophancy benchmark over ten TDIUC categories with three user tones, then mitigates sycophancy with prompting, SFT, and DPO, and finds that high-layer visual attention is lacking and that amplifying it in layers 16-32 reduces sycophancy without hurting accuracy.

Key results

• LLaVA-1.5 baseline sycophancy rate 94.6%; prompt 86.8%, SFT 25.4%, DPO 5.4%, with correction rates falling to 98.6, 88.2, 42.1 and 1.7 respectively.
• Amplifying visual attention in layers 16-32 keeps LLaVA-1.5 Acc@R1 at 88.3 (+3.6) while reducing Syc to 64.4 and Cor to 67.0.
• Sycophancy tends to increase with model size (InternVL-1.5 2B avg 86.5 vs 26B avg 98.1 in the detailed table).

Measured effects reported in the paper

Surprising findings

• Sycophancy tends to increase with model size within both tested model families.
• Mitigation methods make VLMs more stubborn: DPO reduces sycophancy to 5.4% but correction falls to 1.7%.

Limitations

• Mitigation methods validated only on LLaVA-1.5-7B.
• Generalizability to unseen VQA tasks not evaluated.
• DPO consistently yields obstinate models; PPO and other RL methods not explored.

Experimental setup

Datasets: TDIUC LLaVA SFT data (665k)

Benchmarks: MM-SY

Metrics: sycophancy rate correction rate Acc@R1 AUC (probing)

Baselines: BLIP-2 InstructBLIP mPLUG-Owl2 InternVL-1.5 InternLM-XComposer2-VL Gemini GPT-4V LLaVA-1.5 prompt baseline

Builds on: DPO TDIUC LLaVA

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