arXiv:2606. 07533v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues.
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arXiv:2606. 07531v1 Announce Type: cross Abstract: We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to Multimodal LLMs (MLLMs) processing joint text and audio inputs.
EXAM$^2$ is a new benchmark for audio understanding that covers six languages and multiple modalities—speech, sound, music, mixed-audio, and visual images—providing 5,667 multiple-choice questions, 22,614 image instances, and 135,684 multilingual translations. It evaluates large audio language models (LALMs) and multimodal large language models (LLMs), revealing significant gaps in multilingual and cross‑modal performance. The authors also introduce Gemma3n-EXAM$^2$, a lightweight fusion model that improves multilingual results by up to 12.4% and multimodal results by 21.7% over a strong baseline.
arXiv:2607. 06611v1 Announce Type: cross Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words.
The paper introduces Omni Demand Understanding (ODU), a benchmark designed to test whether multimodal models can infer a user's underlying demand from complex audio‑visual interactions. ODU requires models to detect the presence of a demand and infer intent using multimodal and conversational context, evaluated across single‑turn and multi‑turn scenarios. The authors built ODU‑Bench through a taxonomy‑guided approach, agentic video generation, and human‑recorded interactions, and found that even top models like Gemini 3.1 Pro recover only 44.7% of key information, with many models exhibiting high false‑trigger rates.
SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.
arXiv:2512.07571v3 Announce Type: replace Abstract: This paper presents a simple method that allows to easily enhance textual pre-trained large language models with speech information, when fine-tune...