AI BriefingLAVE, an LLM-based VQA evaluation metric, proposed
Key point
LAVE is proposed as a metric that uses an LLM to evaluate semantic similarity instead of the existing string-matching approach.
Details
Existing VQA (Visual Question Answering) evaluation has relied on the Exact String Matching method, which requires a model's answer to exactly match the correct answer. However, in zero-shot settings using large-scale synthetic datasets such as Docmatix, answers are often semantically correct even when their format differs, meaning existing metrics (CIDER, ANLS, BLEU, etc.) have a limitation in underestimating model performance.
To address this, the LAVE (LLM-Assisted VQA Evaluation) metric has been proposed. LAVE uses an LLM to evaluate the semantic similarity between a model's answer and the reference answer, and is designed to have a higher correlation with human judgment than existing metrics.
During the research process, it was found that when a model was further fine-tuned to raise benchmark scores, its performance actually degraded according to human evaluators. This suggests the need for a new evaluation paradigm that can reflect a model's actual performance, rather than simple numerical optimization.
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