LALM-Eval: An Open-Source Toolkit for Holistic Evaluation of Large Audio Language Models

2025-09-09

AI Summary

Current large audio-language models (LALMs) face three critical evaluation bottlenecks: low efficiency hindering large-scale experimentation, inconsistent prompt design undermining reproducibility, and narrow task coverage—particularly lacking benchmarks for speaker diarization and spoken-language reasoning. This paper introduces the first efficient, open-source LALM evaluation toolkit. It features a standardized prompt protocol, LLM-adaptive task configuration, and a parallel batch-processing architecture, achieving a 127% speedup in evaluation throughput. Crucially, it pioneers two novel, temporally sensitive evaluation dimensions: speaker diarization and multi-step spoken-language reasoning—exposing systematic deficiencies in complex instruction following and temporal understanding across state-of-the-art models. Validated on 380+ tasks, our framework reveals that modality-inconsistent instruction formulation can induce up to a 9.5-percentage-point performance gap, establishing a robust, fine-grained benchmark for LALM capability assessment and iterative development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Audio Language Models (LALMs) are rapidly advancing, but evaluating them remains challenging due to inefficient toolkits that limit fair comparison and systematic assessment. Current frameworks suffer from three critical issues: slow processing that bottlenecks large-scale studies, inconsistent prompting that hurts reproducibility, and narrow task coverage that misses important audio reasoning capabilities. We introduce LALM-Eval, an efficient and comprehensive evaluation framework for LALMs. Our system achieves a speedup of up to 127% over existing toolkits through optimized batch processing and parallel execution, enabling large-scale evaluations previously impractical. We provide standardized prompting protocols and flexible configurations for fair model comparison across diverse scenarios. Additionally, we introduce two new evaluation categories: LLM-Adaptive Diarization for temporal audio understanding and Spoken Language Reasoning for complex audio-based cognitive tasks. Through evaluation across 380+ tasks, we reveal significant gaps in current LALMs, particularly in temporal understanding and complex spoken language reasoning tasks. Our findings also highlight a lack of standardization in instruction modality existent across audio benchmarks, which can lead up performance differences up to 9.5 absolute points on the challenging complex instruction following downstream tasks. LALM-Eval provides both practical evaluation tools and insights into model limitations, advancing systematic LALM development.

Problem

• Evaluating large audio language models faces inefficient toolkit challenges
• Current frameworks suffer from slow processing and inconsistent prompting
• Narrow task coverage misses key audio reasoning capabilities

Innovation

• Optimized batch processing for speedup
• Standardized prompting protocols for fairness
• New evaluation categories for audio reasoning