Papers by Yujie He

HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)

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Shuyu Zhang, Yifan Wei, Xinru Wang, Yanmin Zhu, Yangfan He, Yixuan Weng, Yujie Liu, Bin Li

Low-Resource NLP

Challenge:	Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training.
Approach:	They propose a framework that enhances zero-shot slot inference through robust prompt alignment.
Outcome:	Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST.

Let’s Think Frame by Frame with VIP: A Video Infilling and Prediction Dataset for Evaluating Video Chain-of-Thought (2023.emnlp-main)

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Vaishnavi Himakunthala, Andy Ouyang, Daniel Rose, Ryan He, Alex Mei, Yujie Lu, Chinmay Sonar, Michael Saxon, William Wang

Multimodality

Challenge:	Existing studies show vision-language systems can reason about images using natural language, but their capacity for video reasoning remains underexplored.
Approach:	They propose to frame video reasoning as the sequential understanding of a small number of keyframes, thereby leveraging the power and robustness of vision-language systems' capacity to reason about images using natural language.
Outcome:	The proposed models can generate multiple intermediate keyframes and predict future keyframe, and they perform poorly on GPT-4, GPT-3, and VICUNA.

Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning (2022.acl-long)

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Vivek Gupta, Shuo Zhang, Alakananda Vempala, Yujie He, Temma Choji, Vivek Srikumar

Information Extraction & Text Mining

Challenge:	Recent studies show that tabular reasoning models use spurious correlations and focus on false evidence or ignore it altogether.
Approach:	They propose a task where models need to extract evidence and then inference labels . they crowdsource evidence row labels and develop unsupervised evidence extraction strategies .
Outcome:	The proposed approach outperforms baseline models on the inference task using only the automatically extracted evidence as the premise.

Stability Implies Redundancy: Delta Attention Selective Halting for Efficient Long-Context Prefilling (2026.acl-long)

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Yujie Chen, Tailai Chen, Yifeng Gao, Zoe Wanying He, Yijue Xu, Shaobo Wang, Linfeng Zhang

Green & Sustainable NLP

Challenge:	Existing methods to reduce sequence length rely on heuristics that break compatibility with hardware-efficient kernels like FlashAttention.
Approach:	They propose a method that selectively halts stabilized tokens by monitoring layer-wise update dynamics of the self-attention mechanism.
Outcome:	The proposed method can reduce prefill complexity while preserving model accuracy and hardware efficiency.

ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)

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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin

Multimodality

Challenge:	Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0.
Approach:	They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes.
Outcome:	The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation.

RAP-ID: Mechanistic Prompt Injection Detection via Impostor Behavior Analysis (2026.findings-acl)

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Yuchen Yang, Lei Peng, Yujie He, Yang yu, Zhongxin Wu, Yanlei Shi

Responsible & Trustworthy NLP

Challenge:	Existing defenses rely on externally deployed guardrail models or response inspection . current defenses depend on external guardrails or response inspecting .
Approach:	They propose a mechanistic, train-free detection framework that operates exclusively on internal state dynamics during the initial forward pass.
Outcome:	The proposed framework achieves competitive performance with significant overall improvements compared to heuristic methods.

Automatic Construction of Enterprise Knowledge Base (2021.emnlp-demo)

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Junyi Chai, Yujie He, Homa Hashemi, Bing Li, Daraksha Parveen, Ranganath Kondapally, Wenjin Xu

Knowledge Representation

Challenge:	Existing knowledge bases are often based on bootstrapping entities from human-curated sources such as Wikipedia.
Approach:	They propose to build a knowledge base from enterprise documents with minimal human intervention by using deep learning models and classical machine learning techniques.
Outcome:	The proposed system is currently serving as part of a Microsoft 365 service.

OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)

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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun

Language Models

Challenge:	Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks.
Approach:	They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions.
Outcome:	The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning.

TempTabQA: Temporal Question Answering for Semi-Structured Tables (2023.emnlp-main)

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Vivek Gupta, Pranshu Kandoi, Mahek Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar

Multimodality

Challenge:	Semi-structured data often include temporal information about entities, either implicitly or explicitly.
Approach:	They present a dataset that includes 11,454 question-answer pairs from Wikipedia Infobox tables spanning more than 90 distinct domains.
Outcome:	The proposed dataset can be used as a benchmark to improve models for temporal reasoning on semi-structured tables.

DenseSSM: State Space Models with Dense Hidden Connection for Efficient Large Language Models (2025.naacl-long)

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Wei He, Kai Han, Yehui Tang, Chengcheng Wang, Yujie Yang, Tianyu Guo, Yunhe Wang

Semantic Text Processing

Challenge:	Large language models (LLMs) face excessive computational and memory requirements due to the commonly used Transformer architecture.
Approach:	They propose a method to enhance the flow of hidden information between layers in large language models by selectively integrating shallow-layer hidden states into deeper layers.
Outcome:	The proposed method maintains parallelizability and inference efficiency of SSMs while significantly boosting performance on public benchmarks.