Audio Large Language Models (ALLMs)

• Audio Large Language Models (ALLMs) are multimodal AI systems that fuse discrete audio tokens with text processing to enable integrated audio and language understanding.
• They utilize token-level fusion, prompted embedding, or dual-encoder architectures to achieve high performance in tasks like speech recognition, translation, and audio captioning.
• Recent advances in training methods, safety assessments, and unified benchmarks highlight ALLMs’ potential to address challenges such as hallucination, data scarcity, and adversarial attacks.

Audio LLMs (ALLMs) are a class of multimodal artificial intelligence systems designed to jointly process, interpret, and generate information from both audio signals (including speech, sound, and music) and text. By integrating sophisticated audio representation learning with the reasoning, generation, and instruction-following capabilities of LLMs, ALLMs push the boundaries of machine audition, enabling tasks that span automatic speech recognition (ASR), audio captioning, zero-shot event detection, cross-modal reasoning, and interactive conversational AI. Recent advances underscore both their technical promise and their unique safety, robustness, and evaluation challenges, especially as these systems increasingly approach human-like versatility and are deployed in high-stakes real-world scenarios.

1. Architectural Foundations and Modal Integration

ALLMs extend text-only LLMs through explicit inclusion of audio processing front-ends coupled to (typically pretrained) LLM backbones. Prominent integration paradigms include:

• Token-level Fusion: Audio signals are discretized into audio tokens (via codecs or neural quantizers such as w2v-BERT, USM-v2) and concatenated with text tokens. The unified token sequence is then modeled autoregressively by a Transformer decoder, as in AudioPaLM, where text and audio tokens are considered indistinguishable at the embedding layer, with the token embedding matrix expanded from $\mathbf{E} \in \mathbb{R}^{t \times m}$ to $\mathbf{E}' \in \mathbb{R}^{(t+a) \times m}$ for $t$ text and $a$ audio tokens (Rubenstein et al., 2023).
• Prompted Embedding Fusion: Feature vectors from a continuous audio encoder (e.g., Conformer, Whisper, AF-Whisper) are prepended or interleaved with text embeddings at the LLM input layer. Temporal stacking and projection align the audio representation dimension with the LLM (e.g., 4096-d embeddings for LLaMA-7B) (Fathullah et al., 2023).
• Dual-encoder and Adapter Architectures: Some models employ a dual-tower (two-encoder) structure for independent audio and text embeddings, followed by contrastive or generative cross-modal alignment (such as CLAP), while others adopt lightweight adapters that mediate audio input without modifying the LLM weights (Kuan et al., 20 May 2025, Chu et al., 2023).
• Unified Encoders for Multiple Modalities: Recent models (e.g., Audio Flamingo 3's AF-Whisper) move toward a shared encoder across speech, sound, and music, enabling denser cross-modal representations and superior task generalization (Goel et al., 10 Jul 2025).

Technical design choices, such as the use of LoRA adapters for parameter-efficient fine-tuning (Cappellazzo et al., 18 Sep 2024), modality-specific projectors and compression rates, hierarchical tagging for multi-task conditioning (Chu et al., 2023), and chain-of-thought (CoT) reasoning modules (Xie et al., 4 Mar 2025, Goel et al., 10 Jul 2025), underpin state-of-the-art flexibility and performance.

2. Core Capabilities and Applications

ALLMs have demonstrated the capacity to address a wide spectrum of audio-centric and cross-modal tasks:

• Automatic Speech Recognition (ASR): By conditioning LLMs on audio tokens/embeddings, models achieve competitive or state-of-the-art word error rates even with the LLM frozen, as in multilingual LibriSpeech and LRS3 benchmarks (Fathullah et al., 2023, Cappellazzo et al., 18 Sep 2024).
• Speech Translation and Multilingual Transfer: Leveraging pre-trained translation capabilities from the language backbone, ALLMs such as AudioPaLM accomplish both in-domain and zero-shot speech-to-text and speech-to-speech translation across hundreds of language pairs, transferring performance to underrepresented languages (Rubenstein et al., 2023).
• Audio Captioning and Audio-Text Retrieval: Instruction-tuned models with hierarchical tagging (e.g., Qwen-Audio) or large-scale datasets with prompt chaining (e.g., AudioSetCaps) achieve superior results in captioning and paired retrieval tasks. For instance, AudioSetCaps yields R@1 scores of 46.3% (text-to-audio) and 59.7% (audio-to-text) in retrieval and a CIDEr score of 84.8 in captioning (Bai et al., 28 Nov 2024).
• Audio Question Answering and Reasoning: By casting tasks as conditional generation or QA over audio input, models such as Audio-Reasoner and Balanced Alignment via Synthetic Data Generation (BALSa) demonstrate high accuracy in benchmarks that probe multi-hop and reasoning skills, especially when structured CoT training is used (Xie et al., 4 Mar 2025, Kuan et al., 26 May 2025).
• Multi-audio Processing and Co-reasoning: Models and benchmarks have recently begun to address the need for context-dependent reasoning across multiple simultaneous audio streams, using discriminative and comparative learning to capture subtle inter-stream differences as in MALLM and the MAE benchmark (Chen et al., 27 Sep 2024). JASCO tasks require true joint reasoning over speech and sound, not simple information concatenation (Wang et al., 22 Sep 2024).
• Dialogue and Voice Interaction: Instruction-fine-tuned, multi-turn chat frameworks (e.g., Qwen-Audio-Chat, AF3's AF-Chat) allow for dynamic conversations conditioned on both text and audio, including long-context tracking and interactive voice-to-voice replies (Chu et al., 2023, Goel et al., 10 Jul 2025).
• Specialized Evaluation and Judging: ALLMs are now also used to assess paralinguistic style and dialogue quality automatically in generated speech, matching or surpassing human–human agreement in certain speaking style assessment scenarios (Chiang et al., 6 Jun 2025).

3. Advancements in Model Training, Data, and Alignment

ALLM development has been driven by methodological advances in task formulation, training regimes, and data curation:

• Multi-Stage and Curriculum-based Training: Five-stage curriculum strategies, as in Audio Flamingo 3, progressively extend model alignment through pre-training, encoder tuning, skill and long-context fine-tuning, and interactive dialogue/voice modules (Goel et al., 10 Jul 2025).
• Multi-task Learning with Hierarchical Tags: Qwen-Audio demonstrates the necessity of conditioning on structured sequences of tags (for transcription, language, task, output type) to maximize knowledge sharing and minimize task interference during large-scale co-training (Chu et al., 2023).
• Contrastive and Generative Pre-training: InfoNCE objectives and masked reconstruction (as in CLAP, AudioMAE) are used for learning joint embeddings; cross-entropy objectives dominate encoder–decoder captioning (Su et al., 25 Jan 2025).
• Synthetic Data Generation and Negative Sampling: BALSa and LISTEN leverage backbone LLMs to synthesize positive and negative caption-style training pairs, enforcing better discrimination between present and absent events and mitigating hallucinations—without heavily modifying the LLM itself (Kuan et al., 26 May 2025, Kuan et al., 20 May 2025).
• Large-scale Curated Datasets: Pipelines that combine audio–LLMs, chaining prompts and CLAP-based refinement (e.g., AudioSetCaps 1.9M pairs, plus multi-million expansion with VGGSound/YouTube-8M) provide the scale and granularity for robust pre-training and zero-shot transfer (Bai et al., 28 Nov 2024).

These methodological improvements have yielded dramatic reductions in catastrophic forgetting and audio hallucinations, while allowing for efficient parameter adaptation and improved alignment across modalities.

4. Trustworthiness, Safety, and Robustness

ALLMs introduce unique safety and robustness challenges not present in unimodal LLMs:

• Hallucination Suppression: Models are prone to generating false positives—describing audio events not present in input—especially when visual or text cues mislead cross-modal reasoning. The LISTEN method and BALSa synthetic negative sampling significantly reduce hallucination rates, balancing F1 scores between present and absent sounds (Kuan et al., 20 May 2025, Kuan et al., 26 May 2025).
• Backdoor and Adversarial Robustness: Hidden in the Noise (HIN) framework demonstrates that subtle, acoustic-pattern-based backdoors (arising from modifications to speed, accent, temporal dynamics, or injected noise) can achieve attack success rates over 90% at low poisoning ratios, while remaining stealthy with undetectable loss curve changes. Temporal and emotion-based triggers are far more effective than amplitude triggers (volume) (Lin et al., 4 Aug 2025).
• Adversarial Perturbations and Over-the-Air Attacks: White-box gradient attacks can be constructed to trigger specific model behaviors or to degrade transcription even via background environmental noise played in real-world settings. Transferability across instruction sets and robust success rates expose vulnerabilities in practical deployments (Sadasivan et al., 7 Jul 2025).
• Comprehensive Benchmarking of Trustworthiness: The AudioTrust platform provides a multi-dimensional trustworthiness evaluation (fairness, hallucination, safety, privacy, robustness, authentication) using over 4,420 real-world samples and 18 experimental setups. Current models demonstrate vulnerabilities including gender, accent, and socioeconomic status bias, inconsistent defenses against jailbreak and privacy attacks, and varying degrees of robustness to noise and impersonation (Li et al., 22 May 2025).
• Evaluation Taxonomies: Holistic evaluation frameworks for ALLMs now include separate axes for auditory awareness, knowledge/reasoning, dialogue competence, and ethical/safety dimensions (Yang et al., 21 May 2025).

5. Benchmarks, Evaluation Practices, and Community Standardization

Systematic evaluation of ALLMs is maturing with the creation of:

• Universal Audio-Centric Benchmarks: Large and diverse datasets pair audio with captions, QA, and complex multi-hop tasks (AudioCaps, Clotho, AudioSetCaps, ClothoAQA). Synthetic multi-audio and joint scene-reasoning benchmarks (MAE, JASCO, "What Are They Doing") challenge models with compositional, comparison, and co-reasoning requirements (Chen et al., 27 Sep 2024, Wang et al., 22 Sep 2024, Bai et al., 28 Nov 2024).
• Instruction-Following and Multi-task Challenges: Frameworks (AIR-Bench, AudioBench, MMAU) stress the need for standardized comparison across instruction degree, reasoning, and integration of diverse audio types (Su et al., 25 Jan 2025, Yang et al., 21 May 2025).
• Trustworthiness and Fairness Metrics: Quantitative metrics, including group unfairness scores, hallucination detection rates, defense/harmful response rates, and false acceptance in authentication, sharpen model comparison while reflecting real-world deployment risks (Li et al., 22 May 2025).

This coordinated development of benchmarks and metrics is vital to robust progress and cross-paper reproducibility in the field.

6. Limitations, Open Challenges, and Prospective Advances

Several longstanding and emergent challenges limit ALLM advancement:

• Data Scarcity and Distribution Shift: Diversity and scale of audio-text datasets lag those of text and vision. Hallucinations and brittle generalization can arise from noisy, synthetic, or unbalanced training data (Su et al., 25 Jan 2025).
• Catastrophic Forgetting and Modality Misalignment: Adapting LLMs to audio can lead to erosion of text/instruction-following skills and suboptimal cross-modal representations (Kuan et al., 26 May 2025).
• Security and Backdoor Risks: Acoustic backdoors and adversarial attacks targeting ALLMs exploit modality-specific vulnerabilities, often invisible to standard loss-based detection (Lin et al., 4 Aug 2025, Sadasivan et al., 7 Jul 2025).
• Ethical and Societal Considerations: Biases in gender, accent, age, and fluency, as well as susceptibility to privacy attacks and voice impersonation, necessitate regular debiasing and privacy-preserving strategies (Li et al., 22 May 2025).
• Evaluation Fragmentation: Unified protocols covering all modalities, reasoning types, and ethical dimensions are still in early stages (Yang et al., 21 May 2025).

Advancing the field requires research on richer data collection/annotation, improved negative sample and contrastive training schemes, robust unified architectures, continual learning (avoiding catastrophic forgetting), explainability for cross-modal outputs, and adaptive defenses against audio-specific security threats.

7. Future Outlook and Research Trajectories

ALLMs are on a trajectory to become foundational models for universal perception and reasoning across domains. Key pathways include:

• Unified Multimodal Architectures: Moving beyond "two-tower" or pipelined systems to architectures that natively handle audio, text, vision, and beyond.
• Scalable Instructional Data Synthesis: Automating high-quality multi-modal alignment via LLM-generated caption, negative, and multi-audio comparison samples.
• Zero/Few-shot Skill Transfer: Leveraging natural language as universal supervision, enabling robust reasoning and recognition in unseen domains and under-resourced languages.
• Holistic Safety and Trust Systems: Continually updated, community-maintained benchmarks (AudioSafe, AudioTrust) and metrics for real-world deployment under adversarial and ethically sensitive scenarios.
• Rich, Large-Context and Conversational Capabilities: Support for multi-turn, multi-audio dialogue, voice-to-voice interaction, and long-form contextual reasoning (minutes to hours).

A plausible implication is that progress in aligning audio and language modalities, coupled with robust safety evaluation, will be foundational for the next generation of trustworthy, contextually aware, and universally applicable LLMs.