Question Answering

Multimodal agents can create complex videos in software such as Blender by coding without relying on diffusion models. Yet video understanding benchmarks still evaluate models mainly through question answering. If an age…

Question Answering

Why Is Video Still So Expensive? A Survey of Inference-Efficiency Mechanisms in Video and Audiovisual LLMs

2026-09-09 · Killian Steunou, Yannis Tevissen, Mounîm A. El Yacoubi arxiv

Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their …

Question Answering

From Retrieval to Weights: Parametric Individualization of Small Language Models with Individual Text Corpora

2026-09-09 · Christoph Wigbels, Ali Abusaleh, Markus T. Jansen, Alexander Mehler 외 arxiv

We approach a cognitive simulation perspective on episodic and semantic memory in multiple-choice question answering by incorporating text from individual text corpora (ITC) into retrieval-augmented generation and DoRA f…

Question Answering

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering

2026-09-09 · Zizhen Wang, Bo Feng, Zhengfeng Lai, Shiyu Li 외 arxiv

Evaluating video captioning remains a critical challenge for Visual Large Language Models (VLLMs). Existing metrics primarily rely on matching generated text against ground-truth references. This paradigm suffers from th…

Question Answering Video Captioning

SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia

2026-09-09 · Jingyi Liao, Wenyu Zhang, Zhuohan Liu, Yingxu He 외 arxiv

The rapid advancement of audio and multimodal large language models has unlocked transformative speech understanding capabilities, yet evaluation frameworks remain predominantly English-centric, leaving Southeast Asian (…

Emotion Recognition Speaker Recognition Speech Recognition Question Answering

ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs

2026-09-09 · Yizhan Li, Jianxin You, Mengyang Xiong, Yinhuan Chen 외 hf

Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence and a hard requirement for deploying multimodal large language models (MLLMs) as…

Question Answering

전체 12,370편 보기 →