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Keywords = multilingual bias benchmark

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19 pages, 3811 KB  
Article
Understanding and Mitigating Multilingual Bias in LLM-Driven Verilog Code Generation via Hard-Example In-Context Learning
by Guang Yang
Electronics 2026, 15(11), 2275; https://doi.org/10.3390/electronics15112275 - 25 May 2026
Viewed by 260
Abstract
Large language models (LLMs) are increasingly adopted for Verilog code generation, yet existing benchmarks assume English-only prompts, overlooking the linguistic diversity of the global FPGA engineering community. We introduce Multi-VerilogEval, the first multilingual Verilog benchmark, built from 156 unique underlying tasks instantiated in [...] Read more.
Large language models (LLMs) are increasingly adopted for Verilog code generation, yet existing benchmarks assume English-only prompts, overlooking the linguistic diversity of the global FPGA engineering community. We introduce Multi-VerilogEval, the first multilingual Verilog benchmark, built from 156 unique underlying tasks instantiated in four languages (English, Japanese, Hindi, and Mongolian), yielding 624 language-specific test cases. Our evaluation of four representative LLMs reveals a silent failure pattern: syntactic correctness remains high (∼90%) across languages, but functional correctness degrades by up to 23.9% for non-English prompts in open-source and domain-specific models, while commercial models remain near-parity. Hidden-state analysis suggests that multilingual bias is associated with persistent cross-lingual representation divergence throughout the network, which becomes most pronounced in the final layers that directly drive token generation. As fine-tuning and common prompt-based mitigations remain impractical or unreliable for multilingual RTL, we propose HE-ICL (Hard-Example In-Context Learning), a train-free method that constructs few-shot hard-example demonstrations from cross-lingually difficult cases. HE-ICL closes 80–100% of the multilingual gap without any parameter updates, achieving near-parity with or exceeding the English reference level across all evaluated HE-ICL settings. Full article
(This article belongs to the Section Artificial Intelligence)
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13 pages, 698 KB  
Article
What Is Your Favorite Gender, MLM? Gender Bias Evaluation in Multilingual Masked Language Models
by Jeongrok Yu, Seong Ug Kim, Jacob Choi and Jinho D. Choi
Information 2024, 15(9), 549; https://doi.org/10.3390/info15090549 - 7 Sep 2024
Cited by 3 | Viewed by 2765
Abstract
Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based masked language models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is now needed more than ever. [...] Read more.
Bias is a disproportionate prejudice in favor of one side against another. Due to the success of transformer-based masked language models (MLMs) and their impact on many NLP tasks, a systematic evaluation of bias in these models is now needed more than ever. While many studies have evaluated gender bias in English MLMs, only a few have explored gender bias in other languages. This paper proposes a multilingual approach to estimating gender bias in MLMs from five languages: Chinese, English, German, Portuguese, and Spanish. Unlike previous work, our approach does not depend on parallel corpora coupled with English to detect gender bias in other languages using multilingual lexicons. Moreover, a novel model-based method is presented to generate sentence pairs for a more robust analysis of gender bias. For each language, lexicon-based and model-based methods are applied to create two datasets, which are used to evaluate gender bias in an MLM specifically trained for that language using one existing and three new scoring metrics. Our results show that the previous approach is data-sensitive and unstable, suggesting that gender bias should be assessed on a large dataset using multiple evaluation metrics for best practice. Full article
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence 2024)
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