Papers with Chinese context

8 papers
An Alignment-Agnostic Model for Chinese Text Error Correction (2021.findings-emnlp)

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Challenge: Existing models for Chinese text error correction can correct mistaken, missing and redundant characters, but they cannot handle missing or redundant characters.
Approach: They propose an alignment-agnostic framework to correct Chinese text errors . framework detects missing and redundant characters and can be used as a cold start model .
Outcome: The proposed framework can handle both text aligned and non-aligned situations and can serve as a cold start model when no annotation data are provided.
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)

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Challenge: Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events.
Approach: They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context.
Outcome: The proposed framework reduces decline rate while maintaining similar attack success rate.
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)

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Challenge: MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture.
Approach: They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content.
Outcome: The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context.
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)

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Challenge: RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images .
Approach: They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a .
Outcome: The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images.
Exploring and Detecting Self-disclosure in Multi-modal posts on Chinese Social Media (2025.findings-emnlp)

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Challenge: Self-disclosure can provide psychological comfort but can also pose privacy concerns . a lack of high-quality corpora, analysis, and methods for detection is limiting research .
Approach: They construct a high-quality text-image corpus on Chinese multimodal social media platforms . they analyze the distribution of self-disclosure types, modality preferences, user intent .
Outcome: The proposed corpus analyzes self-disclosure behaviors on Chinese social media platforms . it fine-tunes five multimodal large language models to enhance self-discovery detection .
Chinese Inertial GAN for Handwriting Signal Generation and Recognition (2025.acl-long)

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Challenge: Inertial sensors can measure the acceleration and angular velocity of moving objects and are widely used in electronic devices such as smartphones, smartwatches, and fitness bands.
Approach: They propose to use Chinese glyph encoding, forced optimal transport, and semantic relevance alignment to acquire unlimited training samples for Chinese inertial writing recognition.
Outcome: The proposed system improves the performance of six widely used classifiers from 6.7% to 98.4%.
CogEmp:A Cognitive Empathy-Oriented Dialogue System for Structured Psychological Counseling (2026.findings-acl)

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Challenge: Existing models lack accurate modeling of cognitive empathy, especially the ability to understand users’ emotions and their underlying psychological causes.
Approach: They propose a model tailored for the Chinese cultural context that integrates cognitive empathy into LLMs.
Outcome: The proposed model outperforms existing models in key evaluation metrics, particularly in empathy, comprehensibility, and professionalism.
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups (2026.findings-acl)

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Challenge: Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they reflect and amplify social biases.
Approach: They propose a Mandarin-specific evaluation framework to examine social identity biases in Chinese LLMs using Mandarin-based prompts.
Outcome: The proposed framework compares ingroup (“We”) and outgroup (“They”) framings across 240 social groups salient in the Chinese context, using a two-tiered measurement framework that assesses both sentiment and toxicity.

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