Papers by Jianfei Ma
Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement (2025.emnlp-industry)
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Haotan Guo, Jianfei He, Jiayuan Ma, Hongbin Na, Zimu Wang, Haiyang Zhang, Qi Chen, Wei Wang, Zijing Shi, Tao Shen, Ling Chen
| Challenge: | Phonetic Cloaking Replacement (PCR) is a problem in content moderation in China. |
| Approach: | They organize PCR into a four-way surface-form taxonomy and compile PCR-ToxiCN, a dataset of 500 phonetically cloaked offensive posts gathered from the RedNote platform. |
| Outcome: | The proposed model achieves only an F1-score and zero-shot chain-of-thought prompting pushes performance even lower. |
Good Arguments Against the People Pleasers: How Reasoning Mitigates (Yet Masks) LLM Sycophancy (2026.acl-long)
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| Challenge: | Recent studies have identified a critical drawback of aligning models with human judgments and outputs that are flawed or incorrect. |
| Approach: | They evaluate a range of LLMs to examine whether CoT reasoning mitigates sycophancy . they find that reasoning masks a tendency to scophage in some cases . |
| Outcome: | The proposed model models show that CoT reasoning reduces sycophancy but masks it in some cases. |
A Joint Coreference-Aware Approach to Document-Level Target Sentiment Analysis (2024.acl-long)
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| Challenge: | Existing work on aspect-based sentiment analysis (ABSA) focuses on sentence level, document level ABSA is more practical and requires holistic document-level understanding capabilities. |
| Approach: | They propose a learning framework to jointly model the DTSA task and the coreference resolution task using ChatGPT. |
| Outcome: | The proposed framework reduces the reliance on annotated coreference information and alleviates evaluation bias caused by missing coreference targets. |
PhonoThink: Improving Large Language Models’ Reasoning on Chinese Phonological Ambiguities (2025.emnlp-main)
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| Challenge: | Effectively resolving phonological ambiguities is crucial for robust natural language processing, as these ambiguity are pervasive in tasks ranging from speech-to-text, spelling correction, to offensive language detection. |
| Approach: | They propose a framework to enhance LLMs’ phonological capability through a multiple-stage training approach. |
| Outcome: | The proposed framework enables the base model to achieve comparable performance to a much larger model. |
Bridging the Temporal Gap in Multimodal LLMs: Deeply Stacking Temporal Tokens for Audio-Visual Speech Recognition (2026.findings-acl)
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| Challenge: | Existing audio-visual speech recognition systems suffer from a temporal gap . visual speech patterns captured from lip movements provide complementary information that remains inherently robust to acoustic noise. |
| Approach: | They propose a framework that deeply stacks temporal tokens across both encoding and decoding stages to bridge this temporal gap. |
| Outcome: | The proposed framework outperforms existing supervised, self-supervised, and LLM-based methods by 6.1% on LRS2 and 7.8% on LLS3. |
Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention (2025.acl-long)
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| Challenge: | Autoregressive Large Language Models (LLMs) demonstrate exceptional performance in language understanding and generation tasks, but their application in text embedding tasks has been relatively slow due to the constraints of the unidirectional attention mechanism. |
| Approach: | They propose to enable bidirectional attention in LLMs to improve their ability to represent subsequent context but weaken their utilization of preceding context. |
| Outcome: | The proposed model improves the LLMs’ ability to represent subsequent context but weakens their utilization of preceding context, while contrastive learning training can help to maintain both abilities. |