Papers by Weicheng Wang

Self-Para-Consistency: Improving Reasoning Tasks at Low Cost for Large Language Models (2024.findings-acl)

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Wenqing Chen, Weicheng Wang, Zhixuan Chu, Kui Ren, Zibin Zheng, Zhichao Lu

Challenge:	Recent studies have shown that self-consistency decoding can improve performance for complex reasoning tasks with large language models.
Approach:	They propose a self-consistency decoding strategy that generates multiple paraphrases for each test question and then generates reasoning paths for the original and all the paraphrased questions based on greedy decoding.
Outcome:	The proposed strategy reduces the sampling number and improves performance on complex reasoning tasks.

Intersectional Stereotypes in Large Language Models: Dataset and Analysis (2023.findings-emnlp)

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Weicheng Ma, Brian Chiang, Tong Wu, Lili Wang, Soroush Vosoughi

Semantic Text Processing

Challenge:	Existing studies on intersectional stereotypes focus on broader, individual categories . current studies focus on single-group stereotypes, such as racial bias against African Americans .
Approach:	They propose to use a dataset of intersectional stereotypes curated with the ChatGPT model to analyze propagation in three contemporary LLMs.
Outcome:	The proposed dataset enables analysis of stereotype propagation in three contemporary LLMs.

Deciphering Stereotypes in Pre-Trained Language Models (2023.emnlp-main)

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Weicheng Ma, Henry Scheible, Brian Wang, Goutham Veeramachaneni, Pratim Chowdhary, Alan Sun, Andrew Koulogeorge, Lili Wang, Diyi Yang, Soroush Vosoughi

Language Models

Challenge:	Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples.
Approach:	They propose a framework for examining stereotype-encoding behavior of PLMs using model probing and textual analyses.
Outcome:	The proposed approach can debiase PLMs without compromising their language modeling capabilities or performance.

Enhancing LLM-Based Persuasion Simulations with Cultural and Speaker-Specific Information (2025.findings-emnlp)

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Weicheng Ma, Hefan Zhang, Shiyu Ji, Farnoosh Hashemi, Qichao Wang, Ivory Yang, Joice Chen, Juanwen Pan, Michael Macy, Saeed Hassanpour, Soroush Vosoughi

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to persuasive dialogue generation suffer from stance oscillation and low informativeness.
Approach:	They propose reinforced instructional prompting, a method that ensures speaker characteristics consistently guide all stages of dialogue generation.
Outcome:	The proposed method ensures speaker characteristics guide all stages of dialogue generation and aligns language use with speakers’ native languages to better capture cultural nuances.

Simulated Misinformation Susceptibility (SMISTS): Enhancing Misinformation Research with Large Language Model Simulations (2024.findings-acl)

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Weicheng Ma, Chunyuan Deng, Aram Moossavi, Lili Wang, Soroush Vosoughi, Diyi Yang

Responsible & Trustworthy NLP

Challenge:	Psychological inoculations have shown efficacy in curbing its spread and mitigating its adverse effects at early stages, but their design and optimization typically requires substantial human and financial resources due to the need for repeated experimental trials.
Approach:	They propose to use large language models to simulate participant responses in misinformation studies to mitigate caricatures and stereotypes in the simulations.
Outcome:	The proposed method mitigates caricatures and stereotypes in LLM simulations and enhances response diversity.

FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)

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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

Multimodality

Challenge:	Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers.
Approach:	They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model achieves competitive performance with frontier models while maintaining generation efficiency.

AscendKernelGen: LLM-Driven Kernel Generation for NPUs (2026.findings-acl)

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Xinzi Cao, Jianyang Zhai, Pengfei Li, Zhiheng Hu, Cen Yan, null Mubingxu, Guanghuan Fang, Bin She, Jiayu Li, Yihan Su, Dongyang Tao, Feidiao Yang, Chang-Dong Wang, Yutong Lu, Weicheng Xue, Bin Zhou, Yonghong Tian

Challenge:	Neural Processing Units (NPUs) are critical for AI infrastructure, but their development remains a bottleneck due to vendor-specific Domain-Specific Languages (DSLs).
Approach:	They propose a framework for NPU kernel development that bridges the gap in hardware-specific coding . compiler success on complex Level-2 kernels improves from 0% to 95.5%, they say .
Outcome:	The proposed framework bridges the gap in hardware-specific coding, showing a near-zero success rate on complex kernels.

Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks (2021.acl-long)

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Weicheng Ma, Kai Zhang, Renze Lou, Lili Wang, Soroush Vosoughi

Language Models

Challenge:	Prior research has found that only a few attention heads are important in each mono-lingual NLP task and pruning the remaining heads leads to comparable or improved performance of the model.
Approach:	They examine the relative importance of attention heads in Transformer-based models to aid their interpretability in cross-lingual and multi-lingual tasks.
Outcome:	The proposed model performs better with the remaining heads pruned than with the other models, the authors show .

EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background Prediction in English (2022.findings-acl)

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Weicheng Ma, Samiha Datta, Lili Wang, Soroush Vosoughi

Sentiment Analysis

Challenge:	Existing research on cultural background modeling is coarse-grained and does not examine cultural differences among speakers of the same language.
Approach:	They use a news-based cultural background prediction dataset to annotate, validate and benchmark NLP models with cultural background features.
Outcome:	The proposed model improves on nine syntactic, semantic, and psycholinguistic tasks while introducing cultural background information does not improve the Go-Emotions task due to text domain conflicts.

Multi-resolution Annotations for Emoji Prediction (2020.emnlp-main)

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Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi

Visual Data in NLP

Challenge:	Emojis are able to express various linguistic components, such as emotions, sentiments, events, etc. emojis have the merit of preserving information more densely, compared to words, argues a new study.
Approach:	They propose to use passage-level and aspect-level emoji annotations to predict the proper emmojis associated with text.
Outcome:	The proposed method is heuristically generated and validated with a pre-trained BERT model.

Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation (2020.emnlp-main)

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Ruibo Liu, Guangxuan Xu, Chenyan Jia, Weicheng Ma, Lili Wang, Soroush Vosoughi

Information Extraction & Text Mining

Challenge:	Existing methods for data augmentation produce low readability or semantic consistency.
Approach:	They propose a framework which augments data through reinforcement learning guided conditional generation.
Outcome:	The proposed framework improves F1 performance on three different classification tasks by 8.7% on average when given only 10% of the whole data for training.

GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks (2021.emnlp-main)

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Weicheng Ma, Renze Lou, Kai Zhang, Lili Wang, Soroush Vosoughi

Language Models

Challenge:	A key problem in multi-task learning (MTL) research is how to select high-quality auxiliary tasks automatically.
Approach:	They propose an automatic auxiliary task selection method based on gradient calculation in Transformer-based models that improves MT-DNN performance.
Outcome:	The proposed method improves MT-DNN performance on 8 natural language understanding (GLUE) tasks, while costing less than AUTOSEM and comparable GPU consumption.

Improving Syntactic Probing Correctness and Robustness with Control Tasks (2023.acl-short)

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Weicheng Ma, Brian Wang, Hefan Zhang, Lili Wang, Rolando Coto-Solano, Saeed Hassanpour, Soroush Vosoughi

Responsible & Trustworthy NLP

Challenge:	Syntactic probing methods are biased by the PLMs’ memorization of common word co-occurrences, even if they do not form syntactical relations.
Approach:	They propose to use random word substitution and random label matching to reduce these biases and improve the robustness of syntactic probing methods.
Outcome:	The proposed tasks improve probing results and consistency between probing methods and make them more generalizable to unseen text domains.

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction (2025.emnlp-industry)

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Victor Junqiu Wei, Weicheng Wang, Di Jiang, Yuanfeng Song, Lu Wang

Multimodality

Challenge:	Automatic Speech Recognition (ASR) systems have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc.
Approach:	They use a benchmark dataset to analyze ASR errors in the Chinese language . they then apply large language models to correct ASR error correction .
Outcome:	The proposed method is based on a dataset of ASR errors in the Chinese language . it shows prompting is not effective for ASR error correction .