The paper presents the first music‑specific, layer‑wise empirical study of hallucination in audio‑language models, framing it as a hierarchical perceptual grounding failure across five layers: sound events, temporal properties, tonal attributes, style, and emotion. It introduces MuseDiag, a diagnostic framework that evaluates nine models and finds universal vocal misperception, significant tonal perception differences, and identifies Audio‑Flamingo‑3 as the most stable model. The study also proposes two training‑free mitigation methods, ADD‑M and TPA, which reduce hallucination in probing but show variable effectiveness in free‑form generation, highlighting the need for multi‑paradigm evaluation.
arXiv:2608. 19211v1 Announce Type: cross Abstract: Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content.
arXiv:2606. 02642v1 Announce Type: cross Abstract: Despite the success of audio-visual large-language models (LLMs), they can produce plausible but ungrounded outputs, termed hallucination.
arXiv:2606. 07537v1 Announce Type: cross Abstract: Large language models hallucinate--producing fluent, confident, factually wrong outputs--with a consistency that persists across generations and scales.
The paper introduces AIB, the first benchmark of auditory perceptual illusion tasks designed for Large Audio Language Models (LALMs). It covers ten representative music, sound, and speech‑based illusory phenomena, each annotated for knowledge‑based priors, and evaluates models alongside controlled human listening studies. Results reveal that while LALMs are generally signal‑faithful on low‑level acoustic cues, some models show more human‑like responses when linguistic or musical priors are involved, yet none fully match human perceptual patterns.
The paper evaluates four Contrastive Decoding (CD) strategies for Large Audio Language Models (LALMs) and finds that Audio-Aware Decoding and Audio Contrastive Decoding are the most effective. Their performance varies across models, largely depending on the baseline error profile: CD reliably fixes errors where models incorrectly claim no audio or rely on uncertainty-driven guessing, but struggles with flawed reasoning or confident misassertions. A token-level analysis shows that CD’s suppression targets hesitation markers, explaining its limited impact on confident errors.
Audio-visual large language models (AVLLMs) have made remarkable progress in multimodal understanding and reasoning through interactions among visual, auditory, and linguistic information. However, re...
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.
arXiv:2509. 24039v2 Announce Type: replace-cross Abstract: If topography is a fundamental feature of the brain, it should influence both how neurons are arranged in space (i.
arXiv:2608.22236v2 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) have shown promising progress in understanding speech, music, and general sound events, yet their ability...
arXiv:2510.12851v2 Announce Type: replace-cross Abstract: Large Audio-Language Models (LALMs) excel in Audio QA but often suffer from hallucinations ungrounded in the audio. To our knowledge, we are...
arXiv:2608. 00722v1 Announce Type: cross Abstract: Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text.
arXiv:2606. 17417v1 Announce Type: cross Abstract: Large Audio Language Models (LALMs) achieve strong performance on a variety of audio understanding tasks but continue to struggle with temporal reasoning, a fundamental capability central to human auditory perception.