Papers by Xingjian Wei

2 papers
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)

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Challenge: composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies.
Approach: They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings.
Outcome: The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%.
OpenHuEval: Evaluating Large Language Model on Hungarian Specifics (2025.findings-acl)

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Challenge: Recent advances in Large Language Models (LLMs) represent significant strides toward artificial general intelligence (AGI).
Approach: They introduce OpenHuEval, the first benchmark for LLMs focusing on the Hungarian language and specifics.
Outcome: The framework reveals intrinsic patterns and mechanisms of LLMs in non-English languages, with Hungarian serving as an example.

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