Multi-Turn and Single Turn Visual Question Answering Dataset Analysis

Published on 2025-11-12 • Avichala Research

Multi-Turn and Single Turn Visual Question Answering Dataset Analysis – Research Summary for Avichala

Abstract: This paper systematically analyzes the performance of Large Language Models (LLMs) across a diverse range of Visual Question Answering (VQA) datasets, focusing on both single-turn and multi-turn interactions. The study provides a granular comparison of model effectiveness across different dataset characteristics, identifying key factors influencing performance and highlighting the challenges in building robust, multi-turn VQA agents.

Problem Statement: Visual Question Answering remains a critical challenge in AI, requiring models to seamlessly integrate visual and textual information. Existing VQA models often struggle with the inherent complexity of real-world scenarios, particularly multi-turn dialogues where contextual understanding and memory play crucial roles. This research directly addresses the gap in understanding how dataset characteristics—such as dialogue structure and content—impact LLM performance in VQA, informing the development of more reliable and adaptable visual agents for tasks like robotics, assistive technology, and interactive information retrieval. The paper seeks to quantify the impact of “turn-ness” on the task, moving beyond simple benchmark comparisons and towards a deeper understanding of how interactions shape the process.

Methodology: The researchers conducted an extensive experimental evaluation utilizing 10 datasets encompassing both single-turn and multi-turn VQA paradigms. The datasets analyzed include: CIRR, HatefulMemes, MSCOCO, MSCOCO_i2t, MSCOCO_t2i, N24News, SUN397, VOC2007, Visual7W, WebQA and a collection of derived datasets. Crucially, the experiment focuses on LLMs including CLIP, OpenCLIP, GME (Qwen2-VL), UNITE (Qwen2-VL), VLM2Vec (Qwen2.5-VL), E5-V (LLaVA-1.6), MMRet (LLaVA-1.6), CAFe (LLaVA-OV), UNITE (Qwen2-VL), mmE5 (Llama-3.2-Vision), UniME (Phi3.5-V), MoCa (Qwen2.5-VL), CoMa (Qwen2.5-VL) and E5-V (LLaVA-1.6) and UniME (Phi3.5-V) with varying parameter sizes. The models were evaluated across three key meta-tasks: Classification, Retrieval and Grounding. The team assessed performance using metrics such as accuracy, precision, recall, and F1-score. Furthermore, the research meticulously tracked the number of parameters per model for comparative analysis. The researchers explicitly categorized experiments into single turn vs. multi-turn assessments, observing discrepancies in performance related to dialogue history.

Findings & Results: The analysis reveals a strong correlation between multi-turn VQA and model size. Larger LLMs consistently outperformed smaller models, particularly on multi-turn datasets like WebQA and CIRR, indicating that memory capacity and contextual understanding are vital for maintaining coherence across dialogue turns. Datasets exhibiting complex, multi-turn structures – notably those involving conversational components – saw a more pronounced performance boost with increasing model size. The study identifies that datasets containing a higher number of “turns” – as quantified by the dataset’s inherent turn count – directly influenced performance gains. Furthermore, the research consistently observed that single turn datasets (like MSCOCO) benefitted less from model scaling, suggesting that data quality and pre-training matters substantially, though still not to the degree of multi-turn performance. The results also highlight the importance of carefully selecting a dataset reflecting the intended deployment scenario. The classification tasks showed a more linear correlation between model size and accuracy.

Limitations: The research acknowledges limitations including the relatively small scope of datasets considered and the inherent biases present within many VQA datasets. The analyses primarily focus on commercially available models and the evaluation largely relies on standard benchmark metrics, potentially masking nuances in model reasoning. Additionally, the paper doesn’t explore fine-tuning strategies or more sophisticated dialogue management techniques, representing a potential area for future exploration. The reliance on readily available models restricts the investigation of novel architectures or training paradigms.

Future Work & Outlook: Future research should explore the integration of external memory mechanisms – such as retrieval-augmented generation – within LLMs to further enhance their multi-turn capabilities. Investigating the impact of different dialogue history representations – beyond simple turn sequences – is essential. Furthermore, developing more robust methods for evaluating model reasoning processes, rather than solely relying on accuracy scores, will be crucial. The evolution of VQA necessitates exploration of agent-based approaches, where LLMs are coupled with other modules responsible for action planning and execution, representing a significant step toward creating truly intelligent visual agents. Finally, research should extend to exploring how different modalities (audio, video) can be effectively integrated into multi-turn VQA systems.

Avichala Commentary: This study provides a vital empirical foundation for the burgeoning field of visual AI and LLM agents. The emphasis on multi-turn VQA—a critical step toward creating truly interactive agents—is particularly insightful. It echoes the broader trend of LLMs moving beyond simple generation to engaging in sustained, contextualized interactions, mirroring the increasing sophistication of AI agents. The findings contribute to a better understanding of how LLMs’ effectiveness scales with complexity, aligning with the ongoing evolution of the field toward more robust and adaptable AI systems. Given the current rapid development of multimodal AI and the focus on agent-based systems, this research is a crucial stepping stone toward creating truly intelligent and versatile visual agents—a cornerstone of the next generation of AI applications.

Link to the Arxiv: https://arxiv.org/abs/2511.08480v1.pdf