Papers with language-agnostic framework
ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models (2025.findings-naacl)
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| Challenge: | Performance prediction is a method to estimate the performance of Language Models (LMs) on various Natural Language Processing (NLP) tasks. |
| Approach: | They propose a task- and language-agnostic framework to predict the performance of Language Models (LMs) using proxy models. |
| Outcome: | The proposed framework outperforms the state-of-the-art in root-mean-square error (RMSE) and other robustness tests on multilingual NLP tasks. |
SWE-Mutation: Can LLMs Generate Reliable Test Suites in Software Engineering? (2026.findings-acl)
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Yuxuan Sun, Yuze Zhao, Yufeng Wang, Yao Du, Zhiyuan Ma, Jinbo Wang, Mengdi Zhang, Kai Zhang, Zhenya Huang
| Challenge: | Evaluating software engineering capabilities is a core component of large language models (LLMs). |
| Approach: | They propose a benchmark to evaluate LLM-generated test suites that introduces mutated solutions that attempt to "fool" them. |
| Outcome: | The proposed test suites are based on 2,636 mutated variants derived from 800 original instances and include a multilingual subset spanning nine programming languages. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
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| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |