1. Scoring Evaluation: GPT-4V demonstrated the highest similarity to human scoring with a similarity score of 0.557. In contrast, Gemini achieved only 0.332, with LLaVA and CogVLM scoring even lower. This discrepancy is primarily due to Gemini’s tendency to assign scores around 4 points, seldom giving 1 or 2 points. LLaVA and CogVLM show a similar pattern to Gemini, predominantly assigning scores around 4 points. We attribute this to a ‘High-Score’ Bias, akin to the ‘Yes/No’ bias, which may result from an imbalance in positive and negative judging instructions in their training data, severely limits their ability to provide just and varied scores in scoring settings. In comparison, GPT-4V’s scores are more evenly distributed and align closely with human preferences.
2. Pair Comparison: GPT-4V outshines other MLLMs in pair comparison tasks, achieving 0.683 in tie settings and 0.806 in non-tie settings, surpassing 0.8 in many datasets, which indicate a strong alignment with human preferences. Gemini, LLaVA, and CogVLM show a marked preference for declaring a clear winner, possibly due to a lack of tie situations in their training, leading to biased judgments. It’s also interesting that the frequency of ties given by GPT-4V closely mirrors that of human judges, suggesting similar thresholds for tie decisions.
3. Batch Ranking: GPT-4V aligns more closely with human ranking results, indicating a significant lead with a mean Levenshtein Distance of 0.313. However, there is still substantial room for improvement in this task for all MLLMs. Notably, CogVLM is unable to provide a full ranking in this context, offering only the top choice; so it was excluded from this comparison; LLaVA also exhibits position bias influenced by prompt structure, often replicating judgments seen in example prompts, which complicates its ability to produce fair judgments.

To be a reliable judge, consistent decision-making across repeated evaluations of the same query is crucial. For this purpose, we conducted six repeated tests with MLLM judgments and calculated the weighted average consistency scores and Majority Consistency Criterion ratios for GPT-4V and Gemini. Despite a higher temperature setting, GPT-4V substantially outperforms Gemini across all tasks. Particularly in Pair Comparison, GPT-4V achieves a higher consistency score of 0.675, but it encounters difficulties in maintaining similar levels of consistency in Scoring and Batch Ranking tasks, with scores dropping to 0.611 and 0.418, indicating the challenge of producing qualified and convincing judgments.

We explore the feasibility of using LLMs for judging textbased responses without directly analyzing the original images. This involves two approaches: omitting vision information entirely and providing a detailed description of the picture. Surprisingly, we find that LLMs’ performance in multimodal judging tasks significantly improved with picture descriptions, achieving a Pearson similarity of 0.435 in Scoring Evaluation tasks, markedly outperformed judgments made without any vision perception. Notably, in non-tie Pair Comparison, MLLMs with detailed vision descriptions even exceed the standard performance of MLLMs in judging. This suggests that MLLMs may lack certain human-like judging capabilities, while LLMs can effectively judge multimodal tasks when provided with comprehensive task-related descriptions.

Our manual evaluation of MLLMs in judging, focusing on agreement and scoring, revealed notable findings. GPT-4V achieved around 70% human agreement across all settings, excelling in the Pair Comparison task with 79.3% agreement. Specifically, GPT-4V reached 78% in human agreement for Pair Comparison, with Gemini close at 72%, indicating strong performance in most sample pairs and supporting the idea that large models excel in pairwise distinctions (Zheng et al., 2023b), though improvements are needed in other judging settings. In the Scoring Evaluation task, GPT-4V achieved a 70% human agreement rate, peaking at 79.9% in MS-COCO, while Gemini maintained an average rate of 67.7%. To assess the consistency of MLLM judging quality across multiple responses to a single image-instruction pair, we employed the Mean Absolute Deviation (MAD) metric. This measures the average absolute variance between individual scores and the mean, thereby gauging quality variability. Figure 16 demonstrates that GPT-4V exhibits lower variation in quality assessments, indicating more consistent and reliable judgment compared to Gemini, which is further evidenced by its superior performance. However, in Batch Ranking, both models showed reduced human performance. GPT-4V managed 69% human agreement, and Gemini only 47%. Additionally, their analyses received lower scores, especially in complex tasks like Math and graphics information. This suggests that the models’ inherent capabilities may not fully support understanding and completing intricate user instructions to provide accurate judgments.