Calibrating Beyond English: Language Diversity for Better Quantized Multilingual LLMs (2026.eacl-long)
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| Challenge: | Existing quantization methods typically use small, English-only calibration sets . however, their impact on multilingual models remains underexplored . |
| Approach: | They evaluate eight calibration settings across two quantizers on data from 10 different languages. |
| Outcome: | The results show that tailoring calibration sets to the evaluation language yields the largest improvements for individual languages, underscoring the importance of linguistic alignment. |
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