arXiv subjects

Kelaiti Xiao

Publications and source records attributed to Kelaiti Xiao.

3 recordsLinked to original sources

arXiv2026

VisualQuest: A Benchmark for Abstract Visual Reasoning in MLLMs

We introduce VisualQuest, a novel dataset designed to rigorously evaluate multimodal large language models (MLLMs) on abstract visual reasoning tasks that require the integration of symbolic, cultural, and linguistic knowledge. Unlike existing benchmarks that focus on direct image captioning or classification of realistic images, VisualQuest comprises 3,551 non-photographic, stylized images spanning four categories: Public Figures, Popular Culture, Linguistic Expressions, and Literary Works. Each image is paired with targeted questions to probe complex reasoning. We benchmark ten state-of-the-art MLLMs and find that only Gemini-2.5-flash and GPT-4o achieve strong overall performance, while 3.7 percent of the images remain unrecognized by any model, underscoring persistent challenges in multimodal understanding. Fine-grained analysis shows that Gemini excels at recognizing stylized public figures, whereas GPT-4o leads in linguistic reasoning tasks such as visual puns and emoji combinations. VisualQuest provides a comprehensive and challenging resource for advancing research in abstract visual reasoning and highlights key areas for future model improvement. The dataset is available at https://github.com/xkt88/VISUALQUEST.

cs.CV↗

arXiv2025

Visual Puns from Idioms: An Iterative LLM-T2IM-MLLM Framework

We study idiom-based visual puns--images that align an idiom's literal and figurative meanings--and present an iterative framework that coordinates a large language model (LLM), a text-to-image model (T2IM), and a multimodal LLM (MLLM) for automatic generation and evaluation. Given an idiom, the system iteratively (i) generates detailed visual prompts, (ii) synthesizes an image, (iii) infers the idiom from the image, and (iv) refines the prompt until recognition succeeds or a step limit is reached. Using 1,000 idioms as inputs, we synthesize a corresponding dataset of visual pun images with paired prompts, enabling benchmarking of both generation and understanding. Experiments across 10 LLMs, 10 MLLMs, and one T2IM (Qwen-Image) show that MLLM choice is the primary performance driver: GPT achieves the highest accuracies, Gemini follows, and the best open-source MLLM (Gemma) is competitive with some closed models. On the LLM side, Claude attains the strongest average performance for prompt generation.

cs.CL↗

arXiv2025

Is LLM an Overconfident Judge? Unveiling the Capabilities of LLMs in Detecting Offensive Language with Annotation Disagreement

Large Language Models (LLMs) have become essential for offensive language detection, yet their ability to handle annotation disagreement remains underexplored. Disagreement samples, which arise from subjective interpretations, pose a unique challenge due to their ambiguous nature. Understanding how LLMs process these cases, particularly their confidence levels, can offer insight into their alignment with human annotators. This study systematically evaluates the performance of multiple LLMs in detecting offensive language at varying levels of annotation agreement. We analyze binary classification accuracy, examine the relationship between model confidence and human disagreement, and explore how disagreement samples influence model decision-making during few-shot learning and instruction fine-tuning. Our findings reveal that LLMs struggle with low-agreement samples, often exhibiting overconfidence in these ambiguous cases. However, utilizing disagreement samples in training improves both detection accuracy and model alignment with human judgment. These insights provide a foundation for enhancing LLM-based offensive language detection in real-world moderation tasks.

cs.CL↗