arXiv:2605.27649v1 Announce Type: cross Abstract: Multilingual LLMs are increasingly used when instruction, source content, and required response languages do not coincide. Existing benchmarks have expanded multilingual instruction-following evaluation, but they rarely isolate these three roles within a fully crossed design. We introduce MTM-Bench, a controlled benchmark for language-conditioned task execution in which each instance is defined by a triplet \((L_{\text{instr}}, L_{\text{content}}, L_{\text{resp}})\). Across English, Spanish, and Chinese, MTM-Bench enumerates all 27 triplets and
The proliferation of multilingual LLMs necessitates a deeper understanding of language interaction, especially as these models move beyond simple instruction following to complex, real-world tasks.
Understanding how language roles interact within multilingual LLMs is crucial for developing robust and adaptable AI systems, particularly as AI deployment expands across diverse linguistic environments.
The introduction of MTM-Bench allows for a more granular and controlled evaluation of multilingual LLMs, moving beyond aggregated performance metrics to isolate specific linguistic dependencies and failure points.
- · AI researchers
- · Multilingual LLM developers
- · Global tech companies
- · Organizations requiring cross-lingual AI applications
- · Developers of un-robust multilingual LLMs
- · Benchmarks lacking granular linguistic control
Improved performance and reliability of multilingual LLMs across various applications due to targeted benchmark-driven development.
Increased adoption of LLMs in contexts requiring translation, cross-lingual communication, and content generation in diverse languages.
Potential for new AI services and products specifically designed to bridge language barriers, fostering greater global digital integration and reducing friction in international data exchange.
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Read at arXiv cs.LG