IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages
arXiv:2606.19157v1 Announce Type: cross Abstract: AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear whether these models genuinely utilise such context or rely on parametric knowledge learned during pretraining. Existing benchmarks cannot answer this question because they evaluate transcription under fixed prompting conditions and rarely include explicit contextual inputs. We introduce IndicContextEval, a 56-hour multilingual benchmark of natural speech from 555 speakers across 8 Indian languages and 23 p
The proliferation of AudioLLMs creates an immediate need to understand their genuine contextual understanding versus reliance on pre-trained knowledge, especially as these models are deployed globally in diverse linguistic contexts.
This benchmark directly addresses a critical limitation in AudioLLM evaluation, paving the way for more robust, context-aware speech recognition systems crucial for multilingual applications and potentially sovereign AI initiatives.
The explicit evaluation of context utilization in AudioLLMs, particularly for Indic languages, provides a new standard for development and identifies models that can genuinely understand and leverage textual prompts.
- · Indic language users
- · Developers of context-aware AudioLLMs
- · AI researchers focusing on multilingual models
- · Entities building language-specific AI infrastructure
- · Developers of AudioLLMs lacking genuine context utilization
- · Generic, non-contextual speech recognition systems
Improved performance and reliability of AudioLLMs in real-world, multilingual scenarios needing contextual understanding.
Increased investment and research into techniques to enhance true context assimilation in AI models, particularly for underrepresented languages.
Accelerated development of localized and culturally relevant AI solutions across diverse linguistic regions, fostering greater digital inclusion and potentially fueling sovereign AI efforts.
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Read at arXiv cs.CL