Calibration language affects pruning of multilingual language models
Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models
Computation and LanguageMachine Learning
Summary
Pruning means cutting out parts of a language model to make it faster. The authors show that the way you measure which parts to cut depends a lot on the language used in the test data. For Indian languages like Hindi and Tamil, using English data to decide which parts to remove misses important pieces, causing worse performance. They find mixing calibration data from multiple languages can help, but results vary by model and how much pruning is done. This means language-specific testing is important for keeping model quality during pruning.
What this means in practice
- •For natural language processing engineers: Use multilingual calibration data to better select model parts for pruning in non-English languages to avoid hurting performance.
- •For machine learning platform teams: Validate pruning strategies separately per model architecture and pruning depth to maintain quality when supporting diverse languages.
Authors
Vedant Pandhare, Piyush Dhoka
Abstract
Block Influence (BI) ranks Transformer blocks for depth pruning from their input-output cosine change, but the ranking depends on the calibration distribution. We study whether English calibration transfers to Indian languages in Qwen2.5-1.5B-Instruct, Llama-3.2-1B, and Gemma-2-2B. English, Hindi, Marathi, and Tamil use disjoint Wikipedia articles for calibration and evaluation; every selector receives the same 150-chunk budget, and Tamil is excluded from multilingual aggregation to test cross-script transfer. Across all three models, late-block BI is 20.7-55.5% higher for the three Indian languages than for English, and every predeclared late-minus-middle contrast has a 95% bootstrap interval above zero. This mismatch changes the low-BI blocks selected for removal despite high global rank correlations. Averaging BI over equal English, Hindi, and Marathi budgets mitigates the resulting perplexity damage for Qwen at six removed blocks and for Gemma at all tested depths, including held-out Tamil. The mitigation is not universal: on Llama, balancing improves Hindi and Marathi at two removed blocks but severely harms Tamil, while larger pruning budgets select the same blocks and collapse perplexity regardless of calibration. Rank-mean and minimax-rank aggregators do not dominate the raw mean. These results support a diagnostic claim rather than a universal pruning rule: English-only BI systematically misses late-layer activity for Indian-language text, but multilingual calibration must still be validated per architecture and pruning budget.