arXiv AI

The Sound of Absence: Audio-Language Embedding Models Struggle with Negation

arXiv:2607. 12290v1 Announce Type: cross Abstract: Audio-language embedding models such as CLAP are widely evaluated on matching present sound events, but rarely on negation.

arXiv AI

Jul 1

ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models

arXiv:2606. 30682v1 Announce Type: cross Abstract: Recent advances in language--audio retrieval have been largely driven by contrastive dual-encoder architectures that align audio and text in a shared embedding space.

By Fengjie Lu, Chenang Jiang, Jiarui Hai, Helin Wang, Aaron Yee

llms rag nlp multimodal benchmarks

arXiv AI

Jul 1

Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

arXiv:2511. 16757v2 Announce Type: replace-cross Abstract: Audio-language pretraining (ALP) holds promise for learning general-purpose audio representation, yet remains underexplored.

By Wei-Cheng Tseng, Xuanru Zhou, Mingyue Huo, Yiwen Shao, Hao Zhang, Dong Yu

llms

arXiv AI

Aug 5

From Generator to Embedder: Harnessing Innate Abilities of Multimodal LLMs via Building Zero-Shot Discriminative Embedding Model

arXiv:2508. 00955v3 Announce Type: replace-cross Abstract: Adapting generative Multimodal Large Language Models (MLLMs) into universal embedding models typically demands resource-intensive contrastive pre-training, while traditional hard negative mining methods suffer from severe false negative contamination.

By Yeong-Joon Ju, Seong-Whan Lee

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 15

AudioDER: A Deduplication-Enhanced Reasoning Dataset for Post-Training Large Audio-Language Models

arXiv:2606. 14591v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) have shown strong performance on a wide range of audio understanding tasks, yet they still struggle with complex audio reasoning.

By Hui Geng, Yi Su, Han Yin, Tianjiao Wan, Qisheng Xu, Jiaxin Chen, Zijian Gao, Hengzhu Liu, Xie Chen, Kele Xu

llms benchmarks

arXiv AI

Jun 16

NVMOS: Non-Verbal Vocalization Quality Assessment in Speech

arXiv:2606. 15888v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVs), such as laughter, sighs, and coughs, are important acoustic cues for emotion and intent.

By Jialong Mai, Jinxin Ji, Xiaofen Xing, Wencui Liu, Xiangmin Xu

llms multimodal

arXiv AI

Jul 31

MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning

arXiv:2607. 27109v2 Announce Type: cross Abstract: With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions.

By Weijie Wu, Junbo Li, Lin Li, Jun Fang, Qingyang Hong

llms benchmarks

arXiv Machine Learning

Jun 16

MVEB: Massive Video Embedding Benchmark

arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.

By Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, M\'arton Kardos, Niklas Muennighoff, Kenneth Enevoldsen

rag nlp multimodal benchmarks

arXiv Machine Learning

Aug 11

Assessing Factual Music Comprehension in Large Audio Language Models

arXiv:2511. 05550v3 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio.

By Daniel Chenyu Lin, Michael Freeman, John Thickstun

llms multimodal benchmarks

arXiv Machine Learning

Jun 24

Aligning Audio Captions with Human Preferences

arXiv:2509. 14659v3 Announce Type: replace-cross Abstract: Current audio captioning relies on supervised learning with paired audio-caption data, which is costly to curate and may not reflect human preferences in real-world scenarios.

By Kartik Hegde, Rehana Mahfuz, Yinyi Guo, Erik Visser

reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 18

Investigating Faithfulness in Large Audio Language Models

arXiv:2509. 22363v4 Announce Type: replace Abstract: Large Audio Language Models (LALMs) integrate audio encoders with pretrained Large Language Models to perform complex multimodal reasoning tasks.

By Pooneh Mousavi, Lovenya Jain, Mirco Ravanelli, Cem Subakan

llms multimodal benchmarks safety

arXiv AI

Jul 7

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

arXiv:2607. 04383v1 Announce Type: cross Abstract: Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound Event Detection attains frame-level precision only over a closed label set.

By Zihan Zhang, Xize Cheng, Wenhao Yan, Tong Zhang, Dongjie Fu, Boyun Zhang, Yongbo He, Tao Jin

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

MOSS-Audio Technical Report

arXiv:2606. 01802v1 Announce Type: cross Abstract: MOSS-Audio is a unified audio-language model for speech, environmental sound, and music understanding, supporting audio captioning, time-aware question answering, timestamped transcription, and audio-grounded reasoning.

By Chen Yang, Chufan Yu, Hanfu Chen, Jie Zhu, Jingqi Chen, Ke Chen, Wenxuan Wang, Yang Wang, Yaozhou Jiang, Yi Jiang, Zhengyuan Lin, Ziqi Chen, Zhaoye Fei, Chenghao Liu, Jun Zhan, Kang Yu, Kexin Huang, Mingshu Chen, Qinyuan Cheng, Ruixiao Li, Shimin Li, Songlin Wang, Yang Gao, Yiyang Zhang, Xipeng Qiu

llms agents nlp fine-tuning multimodal

Related stories