Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
Blsp: Bootstrapping language-speech pre-training via behavior alignment of continuation writing
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MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.
CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
Empirical sweep finds 4.17 Hz frame rate plus intermediate-layer alignment optimal for speech QA under frozen text LLM backbone.
AuRA uses LoRA and layer-wise distillation from an ASR teacher to internalize audio encoding into LLMs for improved speech-language performance.
Reinforced Behavior Alignment (RBA) uses self-synthesized data from a teacher LLM and reinforcement learning to close the instruction-following gap in SpeechLMs, outperforming distillation and reaching SOTA on spoken QA and speech-to-text translation benchmarks.
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.
citing papers explorer
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Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models
Semantic-level and verification-based uncertainty methods outperform token-level baselines for audio reasoning in ALLMs, but their relative performance on hallucination and unanswerable-question benchmarks is model- and task-dependent.
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MECAT: A Multi-Experts Constructed Benchmark for Fine-Grained Audio Understanding Tasks
MECAT is a multi-expert benchmark for audio AI offering fine-grained captions and QA pairs generated via expert models and LLM reasoning, paired with the DATE metric that combines semantic similarity and cross-sample discriminability to favor detailed outputs.
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Uncertainty-based Debiasing and Unlearning for Decontamination
UBD leverages ensemble uncertainty to estimate per-sample memorization and construct debiased targets for post-hoc correction or unlearning, yielding output distributions closer to uncontaminated models on MMLU-Pro and MATH-MCQA than baselines.
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CORTIS: Text-Only Adaptation of Spoken Language Models for Task-Oriented Voice Agents
CORTIS is a text-only adaptation method for spoken language models that enables direct speech-to-structured-output generation for task-oriented agents and matches or exceeds ASR-LLM cascades under acoustic degradation.
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Which Speech Representation Better Matches Text-Native Reasoning? A Study of Speech-Text Alignment on Frame Rate and Representation
Empirical sweep finds 4.17 Hz frame rate plus intermediate-layer alignment optimal for speech QA under frozen text LLM backbone.
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AuRA: Internalizing Audio Understanding into LLMs as LoRA
AuRA uses LoRA and layer-wise distillation from an ASR teacher to internalize audio encoding into LLMs for improved speech-language performance.
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Enhancing Speech Large Language Models through Reinforced Behavior Alignment
Reinforced Behavior Alignment (RBA) uses self-synthesized data from a teacher LLM and reinforcement learning to close the instruction-following gap in SpeechLMs, outperforming distillation and reaching SOTA on spoken QA and speech-to-text translation benchmarks.
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A Survey of Audio Reasoning in Multimodal Foundation Models
A survey that provides a unified formulation of audio reasoning and reviews advances across Audio-to-Text, Audio-to-Speech, Audio-Visual, and Agentic paradigms while discussing challenges and future directions.