Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices

• URL: http://arxiv.org/abs/2510.07545v2
• Date: Fri, 10 Oct 2025 11:31:25 GMT
• Title: Deploying Tiny LVLM Judges for Real-World Evaluation of Chart Models: Lessons Learned and Best Practices
• Authors: Md Tahmid Rahman Laskar, Mohammed Saidul Islam, Ridwan Mahbub, Mizanur Rahman, Amran Bhuiyan, Israt Jahan, Mir Tafseer Nayeem, Shafiq Joty, Enamul Hoque, Jimmy Huang,
• Abstract summary: Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks.<n>We propose two approaches to ensure cost-efficient evaluation: (i) multi-criteria prompting, which combines separate evaluation criteria into a single query, and (ii) domain-adaptive transfer learning, in which we fine-tune a 2B- parameter LVLM on synthetic judgments in a chart dataset to create the ChartJudge.
• Score: 43.58820960865236
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) with only 7B parameters have shown promise as automated judges in chart comprehension tasks. However, tiny models (<=2B parameters) still perform poorly as judges, limiting their real-world use in resource-constrained settings. To address this, we propose two approaches to ensure cost-efficient evaluation: (i) multi-criteria prompting, which combines separate evaluation criteria into a single query, and (ii) domain-adaptive transfer learning, in which we fine-tune a 2B-parameter LVLM on synthetic judgments in a chart dataset to create the ChartJudge. Experiments show that multi-criteria prompting exposes robustness gaps, which led to a huge drop in performance for 7B models, including specialized LVLM judges like LLaVA-Critic. In addition, we find that our tiny LVLM (ChartJudge) can effectively transfer knowledge from one dataset to another to make it a more specialized model. Our fine-grained analysis across chart types and query complexities offers actionable insights into trade-offs between model size, prompt design, and transferability, enabling scalable, low-cost evaluation for chart reasoning tasks.

Related papers

• IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation [85.56193980646981]
  We propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following.<n>For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses.<n>Experiments on IF-RewardBench reveal significant deficiencies in current judge models.
  arXiv  Detail & Related papers  (2026-03-05T02:21:17Z)
• JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation [13.831735556002426]
  Small language models (SLMs) have shown promise on various reasoning tasks.<n>Their ability to judge the correctness of answers remains unclear compared to large language models (LLMs)
  arXiv  Detail & Related papers  (2025-11-20T01:14:39Z)
• Prompt Tuning Vision Language Models with Margin Regularizer for Few-Shot Learning under Distribution Shifts [13.21626568246313]
  We analyze whether vision-language foundation models can be adapted to target datasets with very different distributions and classes.<n>We propose a novel prompt-tuning method, PromptMargin, for adapting such large-scale VLMs directly on the few target samples.<n>PromptMargin effectively tunes the text as well as visual prompts for this task, and has two main modules.
  arXiv  Detail & Related papers  (2025-05-21T13:26:56Z)
• Judging the Judges: Can Large Vision-Language Models Fairly Evaluate Chart Comprehension and Reasoning? [26.909604648952616]
  We evaluate 13 open-source LVLMs as judges for diverse chart comprehension and reasoning tasks.<n>We design both pairwise and pointwise evaluation tasks covering criteria like factual correctness, informativeness, and relevancy.<n>We focus on cost-effective LVLMs suitable for both research and commercial use.
  arXiv  Detail & Related papers  (2025-05-13T11:50:08Z)
• Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation [6.549143816134529]
  We introduce bftextBi'an, a novel framework featuring a bilingual benchmark dataset and lightweight judge models.<n>The dataset supports rigorous evaluation across multiple RAG scenarios, while the judge models are fine-tuned from compact open-source LLMs.
  arXiv  Detail & Related papers  (2025-02-26T15:12:59Z)
• SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models [74.40683913645731]
  Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications.<n>Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth.<n>Analysis of these prompt scores reveals VLM biases and AND''/OR' signal ambiguities, notably that maximum scores are surprisingly suboptimal compared to second-highest scores.
  arXiv  Detail & Related papers  (2025-02-24T07:15:05Z)
• Ranked from Within: Ranking Large Multimodal Models Without Labels [73.96543593298426]
  We show that uncertainty scores derived from softmax distributions provide a robust basis for ranking models across various tasks.<n>This facilitates the ranking of LMMs on unlabeled data, providing a practical approach for selecting models for diverse target domains without requiring manual annotation.
  arXiv  Detail & Related papers  (2024-12-09T13:05:43Z)
• TALEC: Teach Your LLM to Evaluate in Specific Domain with In-house Criteria by Criteria Division and Zero-shot Plus Few-shot [3.0105173516143395]
  We propose a model-based evaluation method: TALEC.<n>It allows users to flexibly set their own evaluation criteria, and uses in-context learning (ICL) to teach judge model these in-house criteria.<n> TALEC demonstrates a strong capability to accurately reflect human preferences and achieves a correlation of over 80% with human judgments.
  arXiv  Detail & Related papers  (2024-06-25T10:02:42Z)
• Image2Sentence based Asymmetrical Zero-shot Composed Image Retrieval [92.13664084464514]
  The task of composed image retrieval (CIR) aims to retrieve images based on the query image and the text describing the users' intent. Existing methods have made great progress with the advanced large vision-language (VL) model in CIR task, however, they generally suffer from two main issues: lack of labeled triplets for model training and difficulty of deployment on resource-restricted environments. We propose Image2Sentence based Asymmetric zero-shot composed image retrieval (ISA), which takes advantage of the VL model and only relies on unlabeled images for composition learning.
  arXiv  Detail & Related papers  (2024-03-03T07:58:03Z)
• One-Shot Open Affordance Learning with Foundation Models [54.15857111929812]
  We introduce One-shot Open Affordance Learning (OOAL), where a model is trained with just one example per base object category. We propose a vision-language framework with simple and effective designs that boost the alignment between visual features and affordance text embeddings. Experiments on two affordance segmentation benchmarks show that the proposed method outperforms state-of-the-art models with less than 1% of the full training data.
  arXiv  Detail & Related papers  (2023-11-29T16:23:06Z)
• Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs [49.88461345825586]
  This paper proposes a new framework to enhance the fine-grained image understanding abilities of MLLMs. We present a new method for constructing the instruction tuning dataset at a low cost by leveraging annotations in existing datasets. We show that our model exhibits a 5.2% accuracy improvement over Qwen-VL and surpasses the accuracy of Kosmos-2 by 24.7%.
  arXiv  Detail & Related papers  (2023-10-01T05:53:15Z)
• Evaluating Representations with Readout Model Switching [19.907607374144167]
  In this paper, we propose to use the Minimum Description Length (MDL) principle to devise an evaluation metric. We design a hybrid discrete and continuous-valued model space for the readout models and employ a switching strategy to combine their predictions. The proposed metric can be efficiently computed with an online method and we present results for pre-trained vision encoders of various architectures.
  arXiv  Detail & Related papers  (2023-02-19T14:08:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.