Clarifier Framework for Multimodal Ambiguous Queries
Figure 1: Our Clarifier framework resolves the multimodal ambiguous query, “Is this a good gift?” (a) Multimodal Intent Ambiguity: A standard AI defaults to a guess, making assumptions about the recipient (e.g., a child), their interests, and the user’s budget. This “black box” approach is unhelpful because if the assumptions are wrong, the recommendation is useless. (b) Plug-and-Play Clarifier: Our system avoids guessing. It first identifies that key information (recipient, occasion, budget), missing visual context, and pointing gestures between modalities. It then proactively asks clarifying questions and provides camera feedback (”For who? Move camera upward…”). Once the user provides the necessary context, the system can deliver a relevant and genuinely helpful recommendation.
Paper context
Paper title: Plug-and-Play Clarifier: A Zero-Shot Multimodal Framework for Egocentric Intent Disambiguation Abstract: The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs) struggle to resolve these multimodal ambiguous inputs, often failing silently or hallucinating responses. To address these ambiguities, we introduce the Plug-and-Play Clarifier, a zero-shot and modular framework that decomposes the problem into discrete, solvable sub-tasks. Specifically, our framework consists of three synergistic modules: (1) a text clarifier that uses dialogue-driven reasoning to interactively disambiguate linguistic intent, (2) a vision clarifier that delivers real-time guidance feedback, instructing users to adjust their positioning for improved capture quality, and (3) a cross-modal clarifier with grounding mechanism that robustly interprets 3D pointing gestures and identifies the specific objects users are pointing to. Extensive experiments demonstrate that our framework improves the intent clarification performance of small language models (4--8B) by approximately 30%, making them competitive with significantly larger counterparts. We also observe consistent gains when applying our framework to these larger models. Furthermore, our vision clarifier increases corrective guidance accuracy by over 20%, and our cross-modal clarifier improves semantic answer acc Passages referencing this figure: Introduction Figure 1: Our Clarifier framework resolves the multimodal ambiguous query, “Is this a good gift?” (a) Multimodal Intent Ambiguity: A standard AI defaults to a guess, making assumptions about the recipient (e.
