Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese | Paper Citation Record

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Name Statistics

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This paper cites Qwen Technical Report.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Qwen Technical Report

Reference 6

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This paper cites 2023.Fairness and Machine Learning: Limitations and Opportunities.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese 2023.Fairness and Machine Learning: Limitations and Opportunities

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This paper cites Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results

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This paper cites Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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This paper cites Dialect prejudice predicts AI decisions about people's character, employability, and criminality.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Dialect prejudice predicts AI decisions about people's character, employability, and criminality

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This paper cites Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Advancing the Evaluation of Traditional Chinese Language Models: Towards a Comprehensive Benchmark Suite

Reference 19

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Breeze-7B Technical Report

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This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese CMMLU: Measuring massive multitask language understanding in Chinese

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This paper cites 2014.Cross-Strait Common Vocabulary.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese 2014.Cross-Strait Common Vocabulary

Reference 26

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese AlignBench: Benchmarking Chinese Alignment of Large Language Models

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese You Gotta be a Doctor, Lin

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese GPT-4 Technical Report

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Representation Bias in Political Sample Simulations with Large Language Models

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese DRCD: a Chinese Machine Reading Comprehension Dataset

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese An Improved Traditional Chinese Evaluation Suite for Foundation Model

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese InInternational Conference on Future Data and Security Engineering

Reference 53

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Baichuan 2: Open Large-scale Language Models

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This paper cites Measuring Massive Multitask Chinese Understanding.

Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Measuring Massive Multitask Chinese Understanding

Reference 60

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 61

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 62

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 63

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 64

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models

Reference 65

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese A Survey of Large Language Models

Reference 68

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 2018

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Emergent Abilities of Large Language Models

Reference 2022

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Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese Unresolved cited work

Reference 2023

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