Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions

• URL: http://arxiv.org/abs/2507.13773v1
• Date: Fri, 18 Jul 2025 09:31:43 GMT
• Title: Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions
• Authors: Pu Jian, Donglei Yu, Wen Yang, Shuo Ren, Jiajun Zhang,
• Abstract summary: In visual question answering (VQA) context, users often pose ambiguous questions to visual language models (VLMs) due to varying expression habits.<n>We introduce bftextClearVQA benchmark, which targets three common categories of ambiguity in VQA context.
• Score: 17.905632446959007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In visual question answering (VQA) context, users often pose ambiguous questions to visual language models (VLMs) due to varying expression habits. Existing research addresses such ambiguities primarily by rephrasing questions. These approaches neglect the inherently interactive nature of user interactions with VLMs, where ambiguities can be clarified through user feedback. However, research on interactive clarification faces two major challenges: (1) Benchmarks are absent to assess VLMs' capacity for resolving ambiguities through interaction; (2) VLMs are trained to prefer answering rather than asking, preventing them from seeking clarification. To overcome these challenges, we introduce \textbf{ClearVQA} benchmark, which targets three common categories of ambiguity in VQA context, and encompasses various VQA scenarios.

Related papers

• Losing Visual Needles in Image Haystacks: Vision Language Models are Easily Distracted in Short and Long Contexts [65.04791072532106]
  We present LoCoVQA, a benchmark generator for evaluating long-context extractive reasoning in vision language models (VLMs) LoCoVQA augments test examples for mathematical reasoning, VQA, and character recognition tasks with increasingly long visual contexts. This test assesses how well VLMs can ignore irrelevant information when answering queries.
  arXiv  Detail & Related papers  (2024-06-24T17:58:03Z)
• Aligning Language Models to Explicitly Handle Ambiguity [22.078095273053506]
  We propose Alignment with Perceived Ambiguity (APA), a novel pipeline that aligns language models to deal with ambiguous queries. Experimental results on question-answering datasets demonstrate that APA empowers LLMs to explicitly detect and manage ambiguous queries. Our finding proves that APA excels beyond training with gold-standard labels, especially in out-of-distribution scenarios.
  arXiv  Detail & Related papers  (2024-04-18T07:59:53Z)
• Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations [70.6395572287422]
  Self-alignment method is capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. We conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired.
  arXiv  Detail & Related papers  (2024-02-23T02:24:36Z)
• Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs [58.620269228776294]
  We propose a task-agnostic framework for resolving ambiguity by asking users clarifying questions. We evaluate systems across three NLP applications: question answering, machine translation and natural language inference. We find that intent-sim is robust, demonstrating improvements across a wide range of NLP tasks and LMs.
  arXiv  Detail & Related papers  (2023-11-16T00:18:50Z)
• Improving Zero-shot Visual Question Answering via Large Language Models with Reasoning Question Prompts [22.669502403623166]
  We present Reasoning Question Prompts for VQA tasks, which can further activate the potential of Large Language Models. We generate self-contained questions as reasoning question prompts via an unsupervised question edition module. Each reasoning question prompt clearly indicates the intent of the original question. Then, the candidate answers associated with their confidence scores acting as answer integritys are fed into LLMs.
  arXiv  Detail & Related papers  (2023-11-15T15:40:46Z)
• Dynamic Clue Bottlenecks: Towards Interpretable-by-Design Visual Question Answering [58.64831511644917]
  We introduce an interpretable by design model that factors model decisions into intermediate human-legible explanations. We show that our inherently interpretable system can improve 4.64% over a comparable black-box system in reasoning-focused questions.
  arXiv  Detail & Related papers  (2023-05-24T08:33:15Z)
• Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering [26.997542897342164]
  We propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues. We test our model on the QuAC and CANARD datasets and illustrate by experimental results that our proposed framework achieves a better F1 score than the standard question rewriting model.
  arXiv  Detail & Related papers  (2023-04-14T13:42:32Z)
• Delving Deeper into Cross-lingual Visual Question Answering [115.16614806717341]
  We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers.
  arXiv  Detail & Related papers  (2022-02-15T18:22:18Z)
• Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding [28.195853603190447]
  A large-scale conversational agent can suffer from understanding user utterances with various ambiguities. We propose a neural self-attentive model that leverages the hypotheses with ambiguities and contextual signals.
  arXiv  Detail & Related papers  (2021-09-25T22:32:10Z)
• Learning from Lexical Perturbations for Consistent Visual Question Answering [78.21912474223926]
  Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. We propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations.
  arXiv  Detail & Related papers  (2020-11-26T17:38:03Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Rephrasing visual questions by specifying the entropy of the answer distribution [0.0]
  We propose a novel task, rephrasing the questions by controlling the ambiguity of the questions. The ambiguity of a visual question is defined by the use of the entropy of the answer distribution predicted by a VQA model. We demonstrate the advantage of our approach that can control the ambiguity of the rephrased questions, and an interesting observation that it is harder to increase than to reduce ambiguity.
  arXiv  Detail & Related papers  (2020-04-10T09:32:37Z)

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.