Audio-Based Large Language Models

• Audio-Based Large Language Models (ALLMs) are unified multimodal systems integrating audio perception and text processing for tasks such as transcription, translation, and interactive reasoning.
• They employ innovative architectures like unified decoder-only models, concatenation pipelines, and adapter modules to seamlessly blend audio tokens with text in a single autoregressive framework.
• ALLMs leverage advanced training paradigms, including multitask instruction-following and chain-of-thought reasoning, to achieve zero-shot and few-shot performance across diverse audio and language tasks.

Audio-Based LLMs (ALLMs) are multimodal neural architectures that combine audio perception—typically via speech or audio encoders—with LLMs to process, understand, reason over, and generate both spoken and written language within a single, unified system. These models enable seamless transformation between speech and text, robust audio understanding across a diverse range of tasks, and open the door to instruction-following, zero-shot learning, and more sophisticated interactive audio applications.

1. Architectural Innovations and Model Design

ALLMs are characterized by designs that expand upon classical LLMs, integrating dedicated mechanisms for audio processing. Three major architectures are prevalent:

• Unified Decoder-Only Models: AudioPaLM exemplifies this class by extending a pretrained text-only decoder such as PaLM or PaLM-2 to accommodate new audio tokens by increasing the embedding matrix from size $(t \times m)$ to $((t + a) \times m)$, where $t$ is the text vocabulary size and $a$ is the number of discrete audio tokens derived from a speech representation model. Both modalities are then handled equivalently in a single, autoregressive Transformer decoder, enabling arbitrary interleavings of text and audio tokens (2306.12925).
• Prepend or Concatenation Pipelines: Some models introduce a small, pretrained audio encoder (such as a Conformer) whose embeddings are linearly projected and prepended to the sequence of token embeddings for the LLM. The form

$z = [a_1, a_2, ..., a_N, t_1, t_2, ..., t_M]$

allows the LLM to treat the auditory context as conditioning information for subsequent generations (2307.11795, 2409.12319).

• Adapter and Projector Systems: To efficiently incorporate audio into large frozen LLMs, lightweight adapters (between the audio encoder and LLM) or modality-specific projectors compress and align features to the LLM’s input space. In addition, LoRA (Low-Rank Adaptation) modules are frequently used for parameter-efficient fine-tuning (2409.12319, 2311.07919).

Tokenization Strategies: Audio is commonly tokenized using speech representation models (e.g., w2v-BERT, USM-v1/v2, Whisper’s encoder), quantized (often to 1024 tokens at 25 Hz), allowing direct processing within the LLM’s token-level sequence modeling framework (2306.12925, 2311.07919).

Multi-task Conditioning: Hierarchical tagging (i.e., inserting <|transcribe|>, <|caption|>, etc.) and explicit prompt engineering permit ALLMs to multiplex diverse audio and text tasks within the same model (2311.07919).

2. Training Paradigms and Objectives

ALLMs leverage a variety of training regimes to maximize cross-modal understanding and robust performance:

• Multitask and Instruction-Following Training: These approaches simultaneously expose the model to a broad distribution of audio tasks—automatic speech recognition (ASR), speech-to-text translation (AST), speech-to-speech translation (S2ST), audio captioning, and acoustic scene analysis—by structuring training samples with explicit tags for tasks, input/output languages, and timestamping needs (2311.07919).
• Contrastive and Generative Objectives: Models are often trained with contrastive losses to align audio and language representations (e.g., via InfoNCE losses in two-tower models), generative losses (predicting masked audio features or next tokens), and discriminative losses for classification or QA tasks (2501.15177).
• Chain-of-Thought Reasoning: Advanced models enforce multi-step, explicit reasoning. Audio-Reasoner, for instance, produces both a chain-of-thought output and a final answer, requiring the model to articulate its reasoning before producing a result (2503.02318).
• Preventing Catastrophic Forgetting: Synthetic alignment data—generated using backbone LLMs and contrastive-like prompts (e.g., training with both “Which sounds are present?” and “Which are absent?”)—mitigates the risk that tuning on audio-related data erases prior textual competency (2505.20166, 2505.14518).
• Multi-audio Discriminative Training: Recent developments such as MALLM and BALSa-MA introduce discriminative training with synthetically paired audio clips to learn fine-grained differences and similarities between multiple streams (2409.18680, 2505.20166).

3. Core Capabilities and Task Coverage

Modern ALLMs are distinguished by the breadth and integration of their audio-centric abilities:

• End-to-End Speech Processing: AudioPaLM and similar models natively handle ASR, AST, and S2ST—including speaker identity transfer—without cascading multiple separate models (2306.12925).
• Universal Audio Understanding: Qwen-Audio, for example, is trained to cover over 30 audio language tasks (spanning human speech, environmental sounds, music, timestamped events, and QA) under one unified framework using hierarchical tags (2311.07919).
• Interactive and Conversational Analysis: Extended instruction-tuning enables multi-turn dialog—ALLMs can process mixed audio, issue clarifying questions, or carry out audio-centric ChatML-style exchanges (2311.07919, 2503.02318).
• General-Purpose Audio Reasoning: By leveraging large, reasoning-rich datasets (such as CoTA for Audio-Reasoner), ALLMs can follow planning, captioning, stepwise reasoning, and summarization processes on complex multi-domain audio (2503.02318).
• Zero-Shot and Few-Shot Transfer: Initialization from text LLMs endows ALLMs with strong zero-/few-shot learning for unseen language pairs or rare scenarios, provided adequate grounding in multi-modal data (2306.12925, 2311.07919).
• Multi-Audio Reasoning: The ability to compare, contrast, and jointly describe multiple simultaneous audio inputs—integral for real-world scene understanding, multi-user environments, and context-sensitive assistants—is evaluated and benchmarked in frameworks such as MAE and BALSa-MA (2409.18680, 2505.20166).
• Special-Purpose Applications: ALLMs are being adapted for tasks including audio deepfake detection via question-answering reformulations, where fine-tuning on “Is this audio fake or real?” queries leads to high performance even in data-scarce settings (2505.11079).

4. Evaluation, Performance, and Benchmarks

Assessment of ALLMs is conducted against a wide array of benchmarks and using diverse metrics:

• Speech Recognition and Translation: BLEU for translation and WER for ASR (e.g., BLEU gains on CoVoST2 and WER <2.0% on Librispeech with Qwen-Audio) (2306.12925, 2311.07919).
• Audio Captioning and Scene Analysis: Tasks are measured using SPICE or similar semantic metrics on datasets such as AudioCaps, Clotho, and multi-domain benchmarks (2411.15685).
• Multi-Audio Comprehension: Multi-audio benchmarks (MAE) aggregate 20 datasets and 11 tasks to assess performance on comparison, retrieval, joint captioning, and discriminative understanding across both speech and sound (2409.18680).
• Audio Question Answering: Systematic pipelines such as AQUALLM automate QA dataset construction, producing large and diverse benchmarks for AQA accuracy assessment (2312.17343).
• Reasoning and Hallucination Testing: Reasoning-centric leaderboards (AIR-Bench, SAKURA, etc.) quantify multi-hop and chain-of-thought performance, while audio hallucination detection tasks evaluate a model’s susceptibility to describing non-existent sound events (2401.09774, 2505.14518).
• Quality and Style Judging: ALLMs are increasingly used as automatic judges—for example, evaluating generated speech for emotional content, clarity, and paralinguistic expressiveness, with agreement rates similar to human raters (2506.05984).

A table summarizing key capabilities of selected ALLMs:

Model	ASR/AST	Multi-Audio	Reasoning (CoT)	Interactive Dialog	Hallucination Mitigation
AudioPaLM	Yes	Basic	No	No	No
Qwen-Audio	Yes	No	No	Yes	No
Audio-Reasoner	Yes	No	Yes	Yes	Partial
MALLM	Yes	Yes	No	No	No
BALSa-MA	Yes	Yes	No	No	Yes (LISTEN)

5. Trustworthiness, Hallucination, and Security

ALLMs inherit existing LLM challenges (e.g., hallucination, bias) and introduce new concerns unique to the audio domain:

• Audio Hallucinations: Models may erroneously invent sound events not present in the input, or misattribute actions/objects when combining modalities (especially in audio-video scenarios) (2401.09774). Structured contrastive training (e.g., LISTEN, BALSa) with synthesized negative samples for “absent sound” queries demonstrably reduces this error type (2505.14518, 2505.20166).
• Trustworthiness Evaluation: The AudioTrust benchmark offers a multidimensional assessment across fairness, hallucination, safety, privacy, robustness, and authentication, each with tailored evaluation setups and metrics. Experiments show significant remaining challenges in fairness (systematic accent/gender/SES-based biases), hallucination (especially in cross-modal content), and resistance to adversarial audio (2505.16211).
• Adversarial Vulnerabilities: ALLMs are susceptible to real-world attacks via gradient-based adversarial noise, capable of stealthily triggering targeted behaviors (e.g., voice assistant “wake words”) or degrading response quality. These attacks are effective “over the air” and can evade naive defenses unless robust audio compression or denoising is employed (2507.06256).
• Privacy Risks: ALLMs can be prompted to leak sensitive data through direct or inference-based attacks; prompt engineering and explicit privacy-protective mechanisms are necessary but not wholly sufficient (2505.16211).

6. Benchmarks, Datasets, and Evaluation Methodology

Comprehensive datasets underpin ALLM training and evaluation:

• Audio-Text Paired Sets: Large corpora such as AudioCaps, Clotho, and LAION-Audio-630K provide caption-level supervision (2501.15177).
• QA and Reasoning Corpora: AQUALLM leverages LLMs to generate 1M+ QA pairs, while CoTA supports chain-of-thought reasoning for complex multi-modal tasks (2312.17343, 2503.02318).
• Multi-Audio and Trustworthiness Benchmarks: The MAE benchmark (multi-audio), AudioTrust (trustworthiness/safety), and various reasoning leaderboards enable rigorous comparative evaluation (2409.18680, 2505.16211).
• Scalable and Automated Metrics: Automated pipelines (often GPT-4o-based) allow for reproducible and scalable scoring of outputs, including group unfairness metrics, cross-modal WER, and defense success rates tailored to audiocentric vulnerabilities (2505.16211).

7. Future Directions and Open Problems

Key open directions in ALLMs are:

• Unified Multi-Modal Architectures: Continued exploration of “One Head” or cooperative agent systems with multimodal adapters to enable even tighter cross-modal reasoning and reduced resource usage (2501.15177).
• Instruction Tuning and Continual Learning: Preventing catastrophic forgetting and enabling robust instruction-following under continual multi-task training via synthetic data and adapter-based approaches (2505.20166).
• Scalability and Efficiency: Incorporation of state-space models (SSMs) is a promising line of work to address the quadratic complexity of Transformers for long audio sequences while retaining strong generative performance (2411.15685).
• Security and Robustness: Proactive incorporation of adversarial training, advanced denoising, and privacy/robustness constraints will be essential for safely deploying ALLMs in the wild (2507.06256).
• Human-in-the-Loop Judging: As ALLMs begin to serve as automatic evaluators for speech style, dialogue quality, or emotional content, further research on bias calibration and cross-lingual/jurisdictional adaptation of these “AI judges” will be needed (2506.05984).

ALLMs now represent a convergence point in the development of general artificial intelligence systems: combining advances in language, audio, reasoning, and interactive capabilities. Continued innovation in architecture, training, evaluation, and trustworthiness will determine the trajectory and societal impact of ALLMs as they move from research to real-world deployment.