Papers by Mu Zhang

SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC (2024.findings-acl)

Challenge:	Existing methods to optimize instruction tuning datasets face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision.
Approach:	They propose an automated iterative framework for instruction data optimization that prunes low-quality data and refines low quality data using feedback-driven iteration.
Outcome:	The proposed framework outperforms state-of-the-art methods on seven public benchmark datasets with high data efficiency.

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Jinglong Luo, Yehong Zhang, Zhuo Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu

Responsible & Trustworthy NLP

Challenge:	a growing number of cloud-based inference services are relying on SMPC to protect data privacy.
Approach:	They propose a framework for Privacy-Preserving Inference for Transformer models that eliminates exponential and maximum operations in PPI without sacrificing model performance.
Outcome:	The proposed framework outperforms MPCFormer in terms of performance and efficiency . it is 3.57 and 3.58 times faster than PUMA for BERTBASE and BERTLARGE .

Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)

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Donghang Duan, Xu Zheng, Yuefeng He, Chong Mu, Leyi Cai, Lizong Zhang

Responsible & Trustworthy NLP

Challenge:	Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse.
Approach:	They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks.
Outcome:	The proposed framework achieves superior privacy-utility trade-off compared to strong baselines.

MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)

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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu

Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning (2026.findings-acl)

Challenge:	Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets.
Approach:	They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity.
Outcome:	Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets.

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Mu Zhang, Yuxiang Chu, Guangya Yu, Yongqi Fan, Weiyan Zhang, Hang Hu, Tong Ruan, Jingping Liu

DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation (2026.acl-long)

Challenge:	a lack of knowledge breadth and task depth can hinder curriculum learning in domains such as medicine and finance.
Approach:	They propose a two-dimensional curriculum learning framework that coordinates model training along two orthogonal axes: the knowledge dimension and the task dimension.
Outcome:	The proposed framework improves accuracy on medical evaluations by 2.49% and on financial evaluations 1.2% compared with the second-best method.

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Li Huang, Zhongxin Liu, Yifan Wu, Tao Yin, Dong li, Jichao Bi, Nankun Mu, Hongyu Zhang, Meng Yan

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) can replicate insecure patterns from training data.
Approach:	They propose a framework that leverages distributed security-relevant cues by aggregating representations from multiple upper layers via an attention-based module.
Outcome:	Experiments show that the framework improves the secure-and-correct generation rate by 11.9% over baselines.

MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease Progression (2025.findings-acl)

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Linjie Mu, Zhongzhen Huang, Shengqian Qin, Yakun Zhu, Shaoting Zhang, Xiaofan Zhang

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)

Challenge:	Existing datasets and models fail to consider critical aspects of medical diagnostics, authors argue . MMXU enables multi-image questions incorporating both current and historical patient data.
Approach:	They propose a dataset for MedVQA that focuses on identifying changes in specific regions between two patient visits.
Outcome:	The proposed dataset improves diagnostic accuracy by 20% by integrating historical data.

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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu

Generative Bridging Network for Neural Sequence Prediction (N18-1)

Challenge:	Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT .
Approach:	They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models.
Outcome:	The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache.

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Wenhu Chen, Guanlin Li, Shuo Ren, Shujie Liu, Zhirui Zhang, Mu Li, Ming Zhou

Text Generation

Challenge:	Existing approaches to improve the likelihood of sequence prediction models are based on MLE and teacher forcing.
Approach:	They propose a Generative Bridging Network (GBN) that extends the point-wise ground truth to a bridge distribution conditioned on it and optimizes their KL-divergence.
Outcome:	The proposed bridge module can improve on two recognized sequence prediction tasks and minimize learning burden.

HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification (2024.eacl-long)

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Vidit Jain, Mukund Rungta, Yuchen Zhuang, Yue Yu, Zeyu Wang, Mu Gao, Jeffrey Skolnick, Chao Zhang

Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)

Challenge:	Hierarchical text classification is a complex subtask under multi-label text classification . the relevance of document sections can vary based on the hierarchy level, necessitating a dynamic document representation.
Approach:	They propose a text-generation-based framework that uses language models to encode dynamic text representations.
Outcome:	The proposed framework surpasses existing methods while handling data and mitigating class imbalance.

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Wenhao Zhang, Lin Mu, Li Ni, Peiquan Jin, Yiwen Zhang

Revealing the Parallel Multilingual Learning within Large Language Models (2024.emnlp-main)

Challenge:	Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity.
Approach:	They propose a method that introduces structured polynomial expansion directly into the low-rank factor space.
Outcome:	The proposed method outperforms state-of-the-art methods across diverse benchmarks.

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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu

Evaluating the Validity of Word-level Adversarial Attacks with Large Language Models (2024.findings-acl)

Challenge:	Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous studies focusing on using English as the pivot language to enhance language understanding and reasoning focus on using multiple languages.
Approach:	They propose to use parallel multilingual input to enhance the model's comprehension of the input and to examine how multilingual processing affects prediction.
Outcome:	The proposed model can handle multilingual and cross-lingual text within a single input, but previous studies focused on using English as the pivot language to enhance language understanding and reasoning.

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Huichi Zhou, Zhaoyang Wang, Hongtao Wang, Dongping Chen, Wenhan Mu, Fangyuan Zhang

Robustness in NLP

Challenge:	Existing adversarial examples can generate invalid adversarials due to significant changes in semantic meanings compared to their originals.
Approach:	They propose to use a large language model to evaluate adversarial examples by semantic constraints.
Outcome:	The proposed method can generate valid adversarial examples even when they are not equipped with semantic constraints.

DDPrompt: Differential Diversity Prompting in Large Language Models (2024.acl-short)

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Lin Mu, Wenhao Zhang, Yiwen Zhang, Peiquan Jin

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown that their reasoning ability can be enhanced through approaches like Chain-of-Thought (CoT) prompting.
Approach:	They propose a method that generates differentially diverse reasoning paths for different types of questions by voting on the optimal prompts.
Outcome:	The proposed method improves LLMs' reasoning ability on complex reasoning tasks by learning from demonstrations while keeping their parameters frozen.

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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang

Training Language Model to Critique for Better Refinement (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question .
Approach:	They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance .
Outcome:	The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered .

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Tianshu Yu, Chao Xiang, Mingchuan Yang, Pei Ke, Bosi Wen, Cunxiang Wang, Jiale Cheng, Li Zhang, Xinyu Mu, Chuxiong Sun, Minlie Huang

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Large language models (LLMs) have remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks.
Approach:	They propose a framework to train critic models using refinement signals to generate feedback loops where critiques guide the model in refining its responses.
Outcome:	The proposed framework outperforms traditional methods and open-source models in terms of critique quality and refinement outcomes.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

JOLT-SQL: Joint Loss Tuning of Text-to-SQL with Confusion-aware Noisy Schema Sampling (2025.emnlp-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Jinwang Song, Hongying Zan, Kunli Zhang, Lingling Mu, Yingjie Han, Haobo Hua, Min Peng

Programming Languages in NLP

Challenge:	Recent advances in Large Language Models have improved Text-to-SQL methods . however, they still face challenges such as complex multi-stage pipelines and poor robustness to noisy schema information.
Approach:	They propose a single-stage SFT framework that optimizes schema linking and SQL generation via a unified loss.
Outcome:	Experiments on the Spider and BIRD benchmarks show that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models.

EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios (2026.acl-industry)

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Yuanqi Mu, null Bingfeng.Pi, Defei Xia, null Lei.Zuo, Yongqi Zhang

Green & Sustainable NLP

Challenge:	Large language models (LLMs) are increasingly adopted in downstream industries, yet aligning proprietary agents remains challenging.
Approach:	They propose a cost-effective, tool-conditioned alignment framework that forms a closed loop over data collection, iterative training, and deployment-oriented evaluation.
Outcome:	The proposed framework effectively aligns model in SME scenarios while preserving general tool-calling capability.

Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)

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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise (2023.emnlp-main)

Challenge:	Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths.
Approach:	They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models.
Outcome:	The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio.

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Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang

Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)

Challenge:	Existing diffusion models have limitations in modeling discrete data, e.g., languages . we present a novel diffusion model for language modeling inspired by linguistic features in languages based on iterative denoising .
Approach:	They propose a method that iteratively denoises and adds corruptions to the textual data through soft-masking to better noise it.
Outcome:	The proposed model achieves better generation quality and lower training cost than current models with better performance.

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Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

Machine Translation

Challenge:	A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken.
Approach:	They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence.
Outcome:	The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios.

CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples (2024.findings-acl)

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Jianrui Zhang, Mu Cai, Tengyang Xie, Yong Jae Lee

DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models (2026.findings-acl)

Challenge:	Using high-performing text and image generation models, we can improve compositional reasoning capabilities for large multimodal models.
Approach:	They propose a framework to improve visio-linguistic compositional reasoning capability for multimodal models by leveraging physical grounding and using image-based models for semantic counterfactual fine-tuning.
Outcome:	The proposed framework outperforms GPT-4V and DALLE-3 on a new dataset and checkpoints.

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Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang

Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach (2025.findings-acl)

Challenge:	Existing medical benchmarks for diagnostic reasoning are limited in their ability to perform complex tasks.
Approach:	They propose to benchmark diagnostic capabilities of large language models to assess their accuracy and generalization bottlenecks.
Outcome:	The proposed model achieves 45.82%, 31.09%, and 17.79% accuracy, compared to current models, o3-mini, e1 and DeepSeek-R1 .

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Yurong Wu, Fangwen Mu, Qiuhong Zhang, Jinjing Zhao, Xinrun Xu, Lingrui Mei, Yang Wu, Lin Shi, Junjie Wang, Zhiming Ding, Yiwei Wang

Meta-Tool: Unleash Open-World Function Calling Capabilities of General-Purpose Large Language Models (2025.acl-long)

Challenge:	Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images.
Approach:	They propose a prompt-stealing benchmark consisting of 50 templates and 450 images organized into Easy and Hard difficulty levels.
Outcome:	The proposed method outperforms baseline methods with an average improvement of over 10%.

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Shengqian Qin, Yakun Zhu, Linjie Mu, Shaoting Zhang, Xiaofan Zhang

Text2World: Benchmarking Large Language Models for Symbolic World Model Generation (2025.findings-acl)

Challenge:	Large language models struggle with addressing diverse user inquiries in open-world tasks.
Approach:	They propose a plug-and-play tool retrieval system for LLMs to access external tool library and use retrieved tools to solve user's problem.
Outcome:	The proposed model improves on a finetuned version of LLaMA-3.1 and 2,800 dialogues and 7,361 tools spanning ten distinct test categories.

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Mengkang Hu, Tianxing Chen, Yude Zou, Yuheng Lei, Qiguang Chen, Ming Li, Yao Mu, Hongyuan Zhang, Wenqi Shao, Ping Luo

Augmenting Large Language Model Translators via Translation Memories (2023.findings-acl)

Challenge:	Recent studies have encountered limitations in leveraging large language models to generate symbolic world models.
Approach:	They propose a benchmarking framework based on planning domain definition language (PDDL) that employs multi-criteria, execution-based metrics for a more robust evaluation.
Outcome:	The proposed model outperforms models trained with large-scale reinforcement learning, but lacks the robustness needed to perform in world modeling.

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Yongyu Mu, Abudurexiti Reheman, Zhiquan Cao, Yuchun Fan, Bei Li, Yinqiao Li, Tong Xiao, Chunliang Zhang, Jingbo Zhu

TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)

Challenge:	Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models.
Approach:	They propose to use translation memories (TMs) as prompts to prompt large language models (LLMs) they find that the ability of LLMs to "understand" prompts is helpful .
Outcome:	The results are comparable to state-of-the-art NMT systems with bilingual data and are tuned on downstream tasks.

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Lin Mu, Haiyang Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China (2026.acl-long)

Challenge:	Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance.
Approach:	They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing.
Outcome:	The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance.

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Bolun Sun, Charles Chang, Yuen Yuen Ang, Ruotong Mu, Yuchen Xu, Zhengxin Zhang, Pingxu Hao

Challenge:	Adaptive policy communication is a theory of governance in large, decentralized organizations where leaders exercise influence rather than precise control by combining clear and ambiguous instructions to calibrate discipline and flexibility.
Approach:	They propose an expert-directed annotation method that integrates codebook design, structured training, a two-step workflow, and LLM-based scaling.
Outcome:	The proposed method achieves a Fleiss’ kappa of 0.86 on directive labels, indicating high reliability.

DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)

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Lin Mu, Jun Shen, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

DenseLoRA: Dense Low-Rank Adaptation of Large Language Models (2025.acl-long)

Challenge:	Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships.
Approach:	They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions.
Outcome:	The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions.

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Lin Mu, Xiaoyu Wang, Li Ni, Yang Li, Zhize Wu, Peiquan Jin, Yiwen Zhang

WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)

Challenge:	Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization.
Approach:	They propose a low-rank adaptation approach that fine-tunes two low-ranked matrices and adapts them through a dense low-Rank matrix, improving parameter utilization and adaptation efficiency.
Outcome:	The proposed approach achieves 83.8% accuracy with only 0.01% of trainable parameters compared to LoRA's 80.8% with 0.70% of trainability parameters on LLaMA3-8B.

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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie

VGBench: A Comprehensive Benchmark of Vector Graphics Understanding and Generation for Large Language Models (2024.emnlp-main)

Challenge:	despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models.
Approach:	They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese.
Outcome:	The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated .

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Bocheng Zou, Mu Cai, Jianrui Zhang, Yong Jae Lee

RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service (2025.findings-acl)

Challenge:	Current vision models use pixels to rasterize the visual world, but vector graphics are not the best or unique way to represent visual content.
Approach:	They propose a benchmark for vector graphics processing with capable Large Language Models . they use a set of questions to evaluate vector graphics formats and a wide range of prompting techniques .
Outcome:	The proposed benchmark compares LLMs on rasterized representations with vector graphics . it shows that LLM models show strong capability on both aspects .

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Yihang Cheng, Lan Zhang, Junyang Wang, Mu Yuan, Yunhao Yao

Responsible & Trustworthy NLP

Challenge:	Large language models (LLMs) have a tendency to generate factually incorrect or purely fictional responses, a phenomenon known as hallucination.
Approach:	They propose to use remote RAG to protect user query from privacy leakage . they introduce (n,)-DistanceDP to characterize privacy leakages of user query .
Outcome:	The proposed solution can resist embedding inversion attacks while achieving no loss in retrieval under various settings.

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)

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Caihong Mu, Keyang Zhang, Jialiang Zhou, Yi Liu

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)

Challenge:	Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance .
Approach:	They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs .
Outcome:	The proposed model outperforms benchmark models on multiple public datasets.

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Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario (2024.findings-emnlp)

Challenge:	Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies.
Approach:	They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway.
Outcome:	Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization.

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Feiteng Mu, Yong Jiang, Liwen Zhang, Liuchu Liuchu, Wenjie Li, Pengjun Xie, Fei Huang

Challenge:	Existing tool learning methods focus on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness.
Approach:	They propose to predict query performance and cost required to accomplish a given task . they then assign queries to the optimal tools in a cost-effective manner .
Outcome:	The proposed method achieves higher performance at lower cost compared to baseline approaches.

LMDX: Language Model-based Document Information Extraction and Localization (2024.findings-acl)

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Vincent Perot, Kai Kang, Florian Luisier, Guolong Su, Xiaoyu Sun, Ramya Sree Boppana, Zilong Wang, Zifeng Wang, Jiaqi Mu, Hao Zhang, Chen-Yu Lee, Nan Hua

Attention Calibration for Transformer in Neural Machine Translation (2021.acl-long)

Challenge:	Large Language Models have revolutionized Natural Language Processing but their application in extracting information from visually rich documents has not been successful.
Approach:	They propose a language model-based document information extraction and localization methodology to reframe the document information extract task for a LLM.
Outcome:	The proposed method enables extraction of singular, repeated, and hierarchical entities with and without training data.

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Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li