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PAL: Probing Audio Encoders via LLMs - Audio Information Transfer into LLMs

Tony Alex, Wish Suharitdamrong, Sara Atito, Armin Mustafa, Philip J. B. Jackson, Imran Razzak, Muhammad Awais

TL;DR

This work addresses the challenge of transferring rich audio semantics into LLMs by evaluating three integration strategies: the conventional PLITS baseline, a lightweight attention-only approach (LAL), and a hybrid encoder-aware method (PAL). LAL injects audio as attention keys/values while bypassing FFNs, yielding substantial compute and memory savings with maintained or improved task performance; PAL further combines PLITS and LAL by encoder to achieve efficient, general-purpose audio, music, and speech LLMs. Core contributions include a formal PLITS baseline, the LAL mechanism with a quantified efficiency advantage, and the encoder-aware PAL that selectively applies LAL or PLITS per encoder (e.g., using Whisper with PLITS and SSLAM/CLAP with LAL). Experimental results across classification, captioning, and reasoning demonstrate strong efficiency gains (up to 64.1% memory reduction and up to 247.5% throughput increase) with competitive accuracy, enabling scalable audio-LLMs for multi-domain tasks.

Abstract

Integration of audio perception into large language models (LLMs) is an emerging research area for enabling machine listening applications, yet efficient transfer of rich audio semantics from audio encoders to LLMs remains underexplored. The most widely used integration paradigm projects the audio encoder output tokens into the LLM input space (e.g., via an MLP or a Q-Former), then prepends or inserts them to the text tokens. We refer to this generic scheme as Prepend to the LLM's input token space (PLITS) integration. We propose an efficient alternative, Lightweight Audio LLM Integration (LAL). LAL introduces audio representations solely via the attention mechanism within different layers of the LLM, bypassing its feedforward module. LAL encodes rich audio semantics at an appropriate level of abstraction for integration into different blocks of LLMs. Our design significantly reduces computational overhead compared to existing integration approaches. Observing with Whisper that the speech encoder benefits from PLITS integration, we propose an audio encoder aware approach for efficiently Probing Audio encoders via LLM (PAL), which employs PLITS integration for Whisper and LAL for general audio encoders. Under an identical training curriculum, LAL consistently maintains performance or outperforms existing integration approaches across multiple base LLMs and tasks. For general audio tasks, LAL improvement is up to 30% over a strong PLITS baseline while reducing memory usage by up to 64.1% and increasing throughput by up to 247.5%. Furthermore, for general audio-music-speech LLM, PAL performs on par with a fully PLITS integration-based system but with substantially improved computational and memory efficiency. Project page: https://ta012.github.io/PAL/

PAL: Probing Audio Encoders via LLMs - Audio Information Transfer into LLMs

TL;DR

This work addresses the challenge of transferring rich audio semantics into LLMs by evaluating three integration strategies: the conventional PLITS baseline, a lightweight attention-only approach (LAL), and a hybrid encoder-aware method (PAL). LAL injects audio as attention keys/values while bypassing FFNs, yielding substantial compute and memory savings with maintained or improved task performance; PAL further combines PLITS and LAL by encoder to achieve efficient, general-purpose audio, music, and speech LLMs. Core contributions include a formal PLITS baseline, the LAL mechanism with a quantified efficiency advantage, and the encoder-aware PAL that selectively applies LAL or PLITS per encoder (e.g., using Whisper with PLITS and SSLAM/CLAP with LAL). Experimental results across classification, captioning, and reasoning demonstrate strong efficiency gains (up to 64.1% memory reduction and up to 247.5% throughput increase) with competitive accuracy, enabling scalable audio-LLMs for multi-domain tasks.

Abstract

Integration of audio perception into large language models (LLMs) is an emerging research area for enabling machine listening applications, yet efficient transfer of rich audio semantics from audio encoders to LLMs remains underexplored. The most widely used integration paradigm projects the audio encoder output tokens into the LLM input space (e.g., via an MLP or a Q-Former), then prepends or inserts them to the text tokens. We refer to this generic scheme as Prepend to the LLM's input token space (PLITS) integration. We propose an efficient alternative, Lightweight Audio LLM Integration (LAL). LAL introduces audio representations solely via the attention mechanism within different layers of the LLM, bypassing its feedforward module. LAL encodes rich audio semantics at an appropriate level of abstraction for integration into different blocks of LLMs. Our design significantly reduces computational overhead compared to existing integration approaches. Observing with Whisper that the speech encoder benefits from PLITS integration, we propose an audio encoder aware approach for efficiently Probing Audio encoders via LLM (PAL), which employs PLITS integration for Whisper and LAL for general audio encoders. Under an identical training curriculum, LAL consistently maintains performance or outperforms existing integration approaches across multiple base LLMs and tasks. For general audio tasks, LAL improvement is up to 30% over a strong PLITS baseline while reducing memory usage by up to 64.1% and increasing throughput by up to 247.5%. Furthermore, for general audio-music-speech LLM, PAL performs on par with a fully PLITS integration-based system but with substantially improved computational and memory efficiency. Project page: https://ta012.github.io/PAL/

Paper Structure

This paper contains 24 sections, 5 equations, 3 figures, 10 tables.

Table of Contents

  1. Introduction
  2. Literature review
  3. Methodology
  4. Terminologies
  5. Baseline Audio LLM: Prepend to the LLM’s input token space (PLITS)
  6. LAL: Lightweight Audio-LLM Integration
  7. Compute and memory efficiency.
  8. Not PEFT or LoRA. hu2022lora
  9. PAL: An Encoder Aware Architecture Extending LAL with Speech Understanding
  10. Experimental Setup
  11. LAL: Experimental Setup
  12. PAL: Experimental Setup
  13. Conclusion
  14. Appendix
  15. LLM Usage
  16. ...and 9 more sections

Figures (3)

  • Figure 1: Comparison of compute efficiency between LAL (ours) and PLITS, state of the art audio-LLM integration(our baseline). Training was performed with batch size 8 on an NVIDIA A100 using bfloat16, and inference with batch size 12 on an NVIDIA A100 using float16. All benchmarks were executed sequentially on the same node to eliminate load-related discrepancies.
  • Figure 2: Illustration of integration techniques: (A) SOTA integration PLITS (prepend to the LLM’s input token space), which prepends audio tokens to text tokens and propagates the full sequence through all LLM layers (our baseline); (B) our proposed lightweight integration LAL, which introduces audio representations only through the attention mechanism (see Equations \ref{['eq_audio_insert']}, \ref{['eq_qkv_proj']}, and \ref{['eq_attn']}) while bypassing the feedforward modules; (C) the hybrid PAL, an encoder aware integration that combines LAL and PLITS by selecting the method for each encoder.
  • Figure 3: Overview of LFST using the Cambrian connector tong2024cambrian. A single latent token is broadcast to every time–frequency location and then updated inside the connector by cross attention with local SSLAM and CLAP features, fusing fine grained spatiotemporal detail with language aligned semantics. The red tokens illustrate the latent query and the local encoder keys and values it attends to. A newline token is inserted at each new time step so the flattened sequence preserves the original spatiotemporal layout while keeping the output length fixed.