NoisyEQA: ノイズを含む質問に対する身体性質問応答のベンチマーク
NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries
実世界で発生する4種類のノイズを含む質問に対して、身体性質問応答エージェントのノイズ検出・訂正能力を評価するベンチマークNoisyEQAを提案し、自己訂正プロンプト機構と新評価指標を導入した。
著者: Tao Wu, Chuhao Zhou, Yen Heng Wong, Lin Gu, Jianfei Yang
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
The rapid advancement of Vision-Language Models (VLMs) has significantly advanced the development of Embodied Question Answering (EQA), enhancing agents' abilities in language understanding and reasoning within complex and realistic scenarios. However, EQA in real-world scenarios remains challenging, as human-posed questions often contain noise that can interfere with an agent's exploration and response, bringing challenges especially for language beginners and non-expert users. To address this, we introduce a NoisyEQA benchmark designed to evaluate an agent's ability to recognize and correct noisy questions. This benchmark introduces four common types of noise found in real-world applications: Latent Hallucination Noise, Memory Noise, Perception Noise, and Semantic Noise generated through an automated dataset creation framework. Additionally, we also propose a 'Self-Correction' prompting mechanism and a new evaluation metric to enhance and measure both noise detection capability and answer quality. Our comprehensive evaluation reveals that current EQA agents often struggle to detect noise in questions, leading to responses that frequently contain erroneous information. Through our Self-Correct Prompting mechanism, we can effectively improve the accuracy of agent answers.
