arXiv:2512.09066v2 Announce Type: replace-cross Abstract: Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art. As benchmarks rapidly evolve to incorporate complex reasoning and subjective tasks, they increasingly necessitate open-ended responses from LALMs. We present Open-ended Response Correctness Assessment (ORCA) -- a reliable and lightweight model-based approach for answer correctness and disagreement modeling. We employ a three-stage annotation pipeline combining human judgment, structured feedback, and human-AI corre
The rapid advancement and deployment of large audio language models (LALMs) necessitate robust and scalable assessment methods, making ORCA's release timely for current development cycles.
Reliable and scalable evaluation frameworks are critical for advancing AI capabilities, especially for open-ended and complex tasks, directly impacting the trust and utility of advanced AI systems.
The introduction of ORCA provides a standardized, lightweight, and model-based approach for assessing the correctness of open-ended responses from LALMs, which can accelerate AI development and benchmarking.
- · AI researchers and developers
- · Large Audio Language Models
- · AI ethics and safety organizations
- · Manual AI evaluation processes
- · Subjective and inconsistent AI benchmarks
Improved and faster iteration cycles for LALM development due to more efficient evaluation.
Increased adoption of open-ended AI applications in sensitive or complex domains due to higher evaluation reliability.
Potentially democratizes advanced AI development by providing accessible and robust evaluation tools, shifting competitive landscapes.
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Read at arXiv cs.AI