The hidden auditory knowledge in language models
Large language models have become the backbone of nearly every audio understanding system built in the last two years. The recipe is straightforward: take a pretrained LLM, attach an audio encoder that converts sound waves into meaningful embeddings, and fine-tune the whole system on audio tasks. It works remarkably well, which is why audio language models (LALMs) have proliferated across speech recognition, music understanding, and general sound comprehension.
But this architectural choice creates an odd situation. Those language models were trained entirely on text, never exposed to actual audio. Yet they have absorbed decades of human writing about sound. They know descriptions of what a violin sounds like, understand the physics of reverb, can reason about pitch and frequency. The question nobody had systematically asked: how much does this textual knowledge about audio actually matter when you add an audio encoder?
This gap in understanding motivated a comprehensive investigation into what auditory knowledge LLMs actually encode through text-only pretraining, and whether that knowledge predicts downstream audio performance. The findings are striking. Text-only performance on auditory questions correlates strongly with real audio performance. You can predict whether a language model backbone will be good at audio understanding before you ever attach an audio encoder to it, just by testing what it knows about sound from text alone.
Building a framework to measure auditory knowledge
Understanding whether LLMs possess auditory knowledge requires measuring it in multiple ways. A single test captures only one angle. The researchers designed three progressively richer evaluation approaches, each revealing different aspects of what language models know about sound.
The foundation is AKB-2000, a curated benchmark of 2,000 questions testing what LLMs actually know about audio. These aren't general knowledge questions or reasoning tasks, but specifically auditory facts and concepts. What frequencies can humans hear? How does reverb differ from echo? Which instrument has the highest pitch range? The questions span both breadth (do you know many facts about audio?) and depth (do you understand nuances and relationships between concepts?).
Building this benchmark required careful human involvement. The researchers used LLMs to generate candidate questions guided by a taxonomy of sound categories, then had humans verify every single question. It's labor-intensive, but necessary. Without ground truth about correct answers, you can't actually measure what language models know.
Overview of the three evaluations: AKB-2000 construction with a two-level taxonomy guiding question generation and human verification, cascade evaluation using text captions of audio, and audio-grounded fine-tuning where models learn from actual sound
The cascade evaluation creates a middle ground between pure text and full audio access. A pretrained audio captioner processes actual audio and generates text descriptions. The LLM never touches the audio itself, only reads these captions. How well does it perform on audio understanding tasks when operating purely on text descriptions of sound? This approach is weaker than true audio access, since the LLM cannot perceive frequency content or timbre directly. But it's richer than general knowledge, since someone who actually heard the audio summarized what they heard.
Finally, the audio-grounded evaluation attaches an audio encoder, fine-tunes the entire system on actual audio tasks, and measures real performance. Now the LLM has direct access to audio through the encoder's embeddings. The central question: is performance here correlated with text-only and cascade results?
This three-layer structure matters because it shows the full spectrum from "what does the LLM know in isolation" to "how well does it actually perform in a working audio system." Each layer builds on the previous one.
Text knowledge predicts audio performance
The results are striking. Text-only performance on the AKB-2000 benchmark correlates strongly with downstream audio performance. Different LLM families encode substantially different amounts of auditory knowledge. When those models are fine-tuned with audio encoders, their advantage from text knowledge persists. The models that knew more about audio from text also learn better from audio.
Pearson correlation heatmap across all evaluation metrics, with the white line separating text-only metrics (top-left) from audio-grounded metrics (bottom-right)
This is not obvious. Text knowledge and audio capabilities operate through different pathways. They should be nearly independent. Yet they move together. The practical implication is significant: you can predict an LLM backbone's audio capabilities without building the full audio system. Run a quick audit on auditory knowledge, and you gain a strong signal about which backbones will perform better once you attach audio encoders.
The correlation is strong but not perfect. The audio encoder still matters significantly. You cannot simply pick an LLM with good text-based auditory knowledge and expect everything else to follow automatically. The quality of the encoder, the design of the audio pathway, the fine-tuning strategy, all remain consequential. But text knowledge is a powerful first signal.
This finding connects to broader questions about how different modalities interact within language models. Work on multi-source evidence fusion in audio question answering has explored how combining different types of information improves audio understanding, and this research reveals that text alone encodes more about audio than previously assumed.
Where text knowledge helps most
The correlation between text and audio knowledge is not uniform across all domains. Different audio categories show different patterns. Speech understanding benefits heavily from background knowledge about language, phonetics, and communication. An LLM's understanding of linguistics transfers directly to speech tasks. Music understanding might depend more on acoustic and cultural knowledge that text captures less completely. General sound effects occupy the middle ground.
Category-level scatter plots comparing cascade and audio-grounded accuracy for eight fine-tuned LALMs, broken down by Sound, Music, and Speech domains
When comparing cascade performance (text descriptions of audio) against true audio-grounded performance (the encoder processes sound), the scatter of results varies by domain. Speech points cluster more tightly, suggesting that text knowledge is a better predictor of speech performance. Music might show more spread, indicating that acoustic processing through the encoder becomes more dominant relative to text knowledge.
This domain-specific variation is practically useful. It tells practitioners which audio tasks benefit most from choosing an LLM backbone with strong auditory knowledge in text, and where acoustic processing through the encoder becomes the decisive factor. For speech-heavy applications, backbone selection matters more. For music understanding, you cannot rely on text knowledge alone.
Implications for building audio AI systems
The research opens a practical decision-making framework for anyone building large audio language models. Before committing to months of training and fine-tuning, audit an LLM's auditory knowledge using a benchmark like AKB-2000. Models that score high on understanding audio concepts through text will likely perform better once you attach audio encoders.
This is a cheap signal compared to full fine-tuning. You can test multiple backbones quickly and narrow down candidates before investing compute. But it is not perfectly predictive. The audio encoder remains essential. The research suggests treating text knowledge as a strong signal about which backbone to prioritize, not as the determining factor.
The findings also hint at how we might improve audio understanding systems. If text knowledge predicts audio performance, then improving how language models absorb and represent auditory knowledge during pretraining could benefit downstream audio systems. This could mean designing text datasets that emphasize audio descriptions, or pretraining objectives that specifically target acoustic knowledge. Work on alignment between audio and language models has begun exploring these directions.
More broadly, the research demonstrates that knowledge about distinct modalities interact within a text-only system in informative ways. Language models trained on text alone absorb implicit knowledge about sound, and that knowledge remains accessible and useful when the system gains actual audio access. Understanding these cross-modal interactions could inform how we design better multimodal systems.
For researchers studying how language models encode world knowledge, this work is evidence that different domains of knowledge are not isolated. Audio knowledge and linguistic knowledge are entangled in the representations learned from text. That entanglement has real downstream consequences for performance on audio tasks.
The practical upshot: the choice of LLM backbone is not neutral. It shapes whether an audio system will excel or merely suffice. By measuring what language models already know about sound, teams can make faster, better-informed decisions about architecture before committing to full development cycles.
This is a Plain English Papers summary of a research paper called How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.