Papers by Yan Xiang

Noise-robust Cross-modal Interactive Learning with Text2Image Mask for Multi-modal Neural Machine Translation (2022.coling-1)

Challenge:	Existing studies focus on introducing salient word information to general text summarization framework to guide selection of key content in radiology findings.
Approach:	They propose a method for automatic impression generation using word graphs and a Word Graph guided Summarization model to capture critical words and their relations.
Outcome:	The proposed method is validated on two datasets, OPENI and MIMIC-CXR.

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Junjie Ye, Junjun Guo, Yan Xiang, Kaiwen Tan, Zhengtao Yu

RockNER: A Simple Method to Create Adversarial Examples for Evaluating the Robustness of Named Entity Recognition Models (2021.emnlp-main)

Challenge:	Existing studies on multi-modal neural machine translation focus on visual information, but text and image may not match exactly, and visual noise is often ignored.
Approach:	They propose a noise-robust multi-modal interactive fusion approach with cross-modal relation-aware mask mechanism for MNMT.
Outcome:	The proposed model achieves state-of-the-art scores in all En-De, En-Fr and En-Cs translation tasks.

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Bill Yuchen Lin, Wenyang Gao, Jun Yan, Ryan Moreno, Xiang Ren

Robustness in NLP

Challenge:	Recent named entity recognition models have great performance on many conventional benchmarks, but it is not reliable in realistic applications.
Approach:	They propose a method to create natural adversarial examples using Wikidata and pre-trained language models.
Outcome:	The proposed method produces natural adversarial examples with a shifted distribution from training data.

Instruction-following Evaluation through Verbalizer Manipulation (2024.findings-naacl)

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Shiyang Li, Jun Yan, Hai Wang, Zheng Tang, Xiang Ren, Vijay Srinivasan, Hongxia Jin

Sentiment Analysis

Challenge:	Existing benchmarks focus on common instructions that align well with what the model learned during training, but proficiency in responding to these instructions does not necessarily imply strong ability in instruction following.
Approach:	They propose a new instruction-following evaluation protocol called verbalizer manipulation that instructs the model to verbalize the task label with words aligning with model priors to different extents.
Outcome:	The proposed protocol can be integrated with any classification benchmark to examine the model’s reliance on priors and its ability to override them to accurately follow the instructions.

On the Robustness of Reading Comprehension Models to Entity Renaming (2022.naacl-main)

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Jun Yan, Yang Xiao, Sagnik Mukherjee, Bill Yuchen Lin, Robin Jia, Xiang Ren

SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models (2026.findings-acl)

Challenge:	SpanBERT model is more robust than RoBERTa, despite having similar accuracy on unperturbed test data.
Approach:	They propose a pipeline to replace entity names with names from a variety of sources.
Outcome:	The proposed model performs worse when entities are renamed, the authors show . SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa .

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Xinyi Zeng, Xue Yang, Jingyuan Zhang, Huanqian Yan, Xiang Chen, Kaiwen Wei, Hankun Kang, Yu Tian

Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA (2025.findings-acl)

Challenge:	Existing defense methods rely on fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks.
Approach:	They propose a decoding-level defense mechanism that employs a lightweight discriminator to iteratively steer the decoding process toward safety.
Outcome:	The proposed method improves safety performance by up to 33.40% without fine-tuning on multiple MLLMs.

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Qianqi Yan, Xuehai He, Xiang Yue, Xin Eric Wang

The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis (2024.emnlp-main)

Challenge:	Large Multimodal Models (LMMs) have demonstrated impressive performance on existing medical visual question answering benchmarks.
Approach:	They evaluate large multimodal models that perform worse than random guessing on medical questions . authors suggest more robust evaluation methods to ensure reliability of LMMs .
Outcome:	a new study shows that large multimodal models perform worse than random guessing on medical visual question answering benchmarks.

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Yuxiang Zhou, Jiazheng Li, Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

PrinciplismQA: A Philosophy-Grounded Approach to Assessing LLM-Human Clinical Medical Ethics Alignment (2026.findings-acl)

Challenge:	In-context learning (ICL) is a capability that enables large language models to excel in proficiency through demonstration examples.
Approach:	They present a survey on the interpretation and analysis of in-context learning . they focus on theoretical and empirical perspectives on the concept .
Outcome:	The proposed model can perform tasks with minimal examples without re-training and has demonstrated proficiency across various tasks with a minimal set of task-oriented examples.

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Chang Hong, Minghao Wu, Qingying Xiao, Yuchi Wang, Xiang Wan, Guangjun Yu, Benyou Wang, Yan Hu

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)

Challenge:	Existing benchmarks lack systematic approaches to integrate philosophical frameworks and expert validation for ethical reasoning assessment.
Approach:	They propose a philosophy-grounded approach to assess medical ethics alignment . PrinciplismQA comprises 3,648 expert-validated questions spanning knowledge assessment and clinical reasoning .
Outcome:	PrinciplismQA provides a philosophy-grounded approach to assessing medical ethics alignment.

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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li

Cross-modal Memory Networks for Radiology Report Generation (2021.acl-long)

Challenge:	Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process.
Approach:	They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor.
Outcome:	Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework.

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Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan

Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection (2024.naacl-long)

Challenge:	Medical imaging reports are essential in clinical practice, and generating the reports is beneficial to reduce the burden of radiologists.
Approach:	They propose to use a shared memory to enhance the encoder-decoder framework for radiology report generation.
Outcome:	The proposed model can generate more accurate reports on two widely used datasets.

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Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)

Challenge:	Instruction-tuned Large Language Models (LLMs) can modulate responses based on human instructions, but they can be maliciously steered to impact society in subtle but persistent ways.
Approach:	They propose a backdoor attack setting that allows an attacker to inject a virtual prompt into an LLM to steer it without any explicit injection at its input.
Outcome:	The proposed method is able to poison the model's instruction tuning data and show that it is highly effective in steering the model.

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Yanlin Feng, Xinyue Chen, Bill Yuchen Lin, Peifeng Wang, Jun Yan, Xiang Ren

Question Answering

Challenge:	Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale.
Approach:	They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs.
Outcome:	The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.

PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization (2025.naacl-long)

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Jiayi Wu, Hengyi Cai, Lingyong Yan, Hao Sun, Xiang Li, Shuaiqiang Wang, Dawei Yin, Ming Gao

Named Entity Recognition for Social Media Texts with Semantic Augmentation (2020.emnlp-main)

Challenge:	Existing approaches to optimize RAG generators fail to align with RAG requirements thoroughly.
Approach:	They propose a method for optimizing the RAG generator from multiple preference perspectives to align with RAG requirements comprehensively.
Outcome:	The proposed method improves the performance of RAG generators by incorporating retrieved documents into the prompt.

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Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai

A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization (2020.emnlp-main)

Challenge:	Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts.
Approach:	They propose a neural-based approach to named entity recognition for social media texts . they obtain augmented semantic information from a large-scale corpus and encode it .
Outcome:	The proposed approach outperforms existing approaches on three social media datasets.

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Jinghui Yan, Yining Wang, Lu Xiang, Yu Zhou, Chengqing Zong

Challenge:	Medical entity normalization (NEN) is a task that links medical mentions to entities in knowledge bases.
Approach:	They propose a sequence generative framework to generate Chinese medical procedure entity normalization by constraint decoding and category-based model refining.
Outcome:	The proposed model improves on baselines especially in the case of multi-implication Chinese medical procedures.

EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)

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Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu

Semantic Text Processing

Challenge:	Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts.
Approach:	They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.
Outcome:	The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering.

Multilingual Generative Retrieval via Cross-lingual Semantic Compression (2025.findings-emnlp)

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Yuxin Huang, Simeng Wu, Ran Song, Yan Xiang, Yantuan Xian, Shengxiang Gao, Zhengtao Yu

Multilinguality

Challenge:	Existing methods for multilingual retrieval still face cross-lingual identifier misalignment and identifiere inflation.
Approach:	They propose a framework that unifies semantically equivalent multilingual keywords into shared atoms to align semantics and compresses the identifier space.
Outcome:	The proposed framework improves cross-lingual alignment and reduces redundancy.

Meet Changes with Constancy: Learning Invariance in Multi-Source Translation (2020.coling-main)

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Jianfeng Liu, Ling Luo, Xiang Ao, Yan Song, Haoran Xu, Jian Ye

Machine Translation

Challenge:	Existing approaches to multi-source neural machine translation neglect inconsistencies between sources of information.
Approach:	They propose a source invariance network to learn invariant information of parallel sources . they propose to integrate such network with multi-encoder based multi-source NMT methods .
Outcome:	The proposed approach achieves clear gains in translation quality and captures implicit invariance between different sources.

Rethinking Backdoor Detection Evaluation for Language Models (2025.emnlp-main)

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Jun Yan, Wenjie Jacky Mo, Xiang Ren, Robin Jia

Robustness in NLP

Challenge:	Existing backdoor detection methods have high accuracy in detecting backdoored models, but they are not robust enough to detect backdoors in the wild.
Approach:	They examine the robustness of backdoor detectors by manipulating different factors during backdoor planting.
Outcome:	The proposed methods are able to detect backdoors in the wild, but they lack robustness against backdoor attacks.

MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)

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Wentao Ge, Shunian Chen, Hardy Chen, Nuo Chen, Junying Chen, Zhihong Chen, Wenya Xie, Shuo Yan, ChenghaoZhu ChenghaoZhu, Ziyue Lin, Dingjie Song, Xidong Wang, Anningzhe Gao, Zhang Zhiyi, Jianquan Li, Xiang Wan, Benyou Wang

BITE: Textual Backdoor Attacks with Iterative Trigger Injection (2023.acl-long)

Challenge:	Existing evaluation methodologies for multimodal large language models are limited in evaluating objective queries without considering real-world user experiences.
Approach:	They propose to evaluate multimodal large language models with per-sample criteria using potent MLLM as the judge.
Outcome:	The proposed evaluation paradigm shows that it can be used to evaluate multimodal large language models with per-sample criteria.

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Jun Yan, Vansh Gupta, Xiang Ren

Robustness in NLP

Challenge:	Existing methods to defend against backdoor attacks are based on model stealing, model thieving and training data extraction attacks.
Approach:	They propose a backdoor attack that poisons training data to establish strong correlations between the target label and a set of “trigger words” These trigger words are iteratively identified and injected into the target-label instances through natural word-level perturbations.
Outcome:	The proposed attack is significantly more effective than baseline methods while maintaining decent stealthiness, raising alarm on the usage of untrusted training data.

Learning Contextualized Knowledge Structures for Commonsense Reasoning (2021.findings-acl)

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Jun Yan, Mrigank Raman, Aaron Chan, Tianyu Zhang, Ryan Rossi, Handong Zhao, Sungchul Kim, Nedim Lipka, Xiang Ren

Reasoning

Challenge:	Recent knowledge graph (KG) augmented models have achieved notable success on commonsense reasoning tasks.
Approach:	They propose a KG-augmented model that contextualizes extracted and generated knowledge by reasoning over both within a single graph structure.
Outcome:	The proposed model outperforms existing models on four commonsense reasoning benchmarks and a user study on edge validness and helpfulness.

Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation Perspective (2024.emnlp-main)

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Hanqi Yan, Yanzheng Xiang, Guangyi Chen, Yifei Wang, Lin Gui, Yulan He

Challenge:	Recent studies focus on monosemanticity on its basic units.
Approach:	They propose to revisit monosemanticity from the feature decorrelation perspective and advocate for its encouragement.
Outcome:	The proposed method improves representation diversity and activation sparsity and improves preference alignment performance.

ASD-iLLM:An Intervention Large Language Model for Autistic Children based on Real Clinical Dialogue Intervention Dataset (2025.findings-emnlp)

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Shuzhong Lai, Chenxi Li, Junhong Lai, Yucun Zhong, Chenyu Yan, Xiang Li, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Currently, leveraging large language models (LLMs) for autism intervention is a significant yet challenging task, especially when directly employing LLMs as an intervention doctor.
Approach:	They propose a framework for training LLMs to conduct dialogue interventions in accordance with the principles of Applied Behavior Analysis (ABA) they also propose 'role-play' strategy in which LLM act as autistic children to comprehensively evaluate the doctor model's capabilities at the dialogue level.
Outcome:	The proposed framework outperforms existing models in both automatic and human evaluation, with intervention strategies and dialogue style more closely resembling those of clinical intervention doctors.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Ling Luo, Xiang Ao, Yan Song, Feiyang Pan, Min Yang, Qing He

From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement (2025.emnlp-main)

Challenge:	Existing methods for extracting text summarization are abstractive and extractive.
Approach:	They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading .
Outcome:	The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets.

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Zike Yuan, Yang Xiang, Buzhou Tang

Challenge:	Chain-of-Thought reasoning introduces significant inference latency due to its verbosity.
Approach:	They propose a framework that leverages token elasticity phenomenon to progressively compress CoTs via multiround refinement.
Outcome:	The proposed method achieves an average accuracy improvement of 5.6% over state-of-the-art baselines while reducing CoT length by an average of 47 tokens and significantly lowering latency.

Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales (2025.findings-emnlp)

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JianZhi Yan, Le Liu, Youcheng Pan, Shiwei Chen, Yang Xiang, Buzhou Tang

Generating Radiology Reports via Memory-driven Transformer (2020.emnlp-main)

Challenge:	Existing work on rationale quality underestimates the importance of CoT distillation, focusing primarily on data quantity, which may result in transferring noisy or incorrect information to the student model.
Approach:	They propose a method which can discern and select high quality rationales for distillation and a Rationale Difficulty metric to measure the ability of the student model to generate the correct answer under a given rationale.
Outcome:	The proposed method achieves 4.6% accuracy improvement over baseline data on seven datasets over three tasks, controlling accuracy, diversity, and difficulty.

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Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan

GRPO-Guided Modality Selection Enhanced LoRA-Tuned LLMs for Multimodal Emotion Recognition (2025.findings-emnlp)

Challenge:	Medical imaging reports are time-consuming and can be error-prone for inexperienced radiologists.
Approach:	They propose to generate radiology reports with memory-driven Transformer using relational memory and memory-based conditional layer normalization.
Outcome:	The proposed method outperforms existing models on IU X-Ray and MIMIC-CXR . it generates long reports with medical terms and meaningful image-text attention mappings .

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Yang Chen, Shuwan Yang, Yan Xiang, Ran Song, Yuxin Huang, Zhengtao Yu

Sentiment Analysis

Challenge:	Multimodal emotion recognition in conversation (MERC) aims to identify speakers’ emotional states by utilizing text, audio, and visual modalities.
Approach:	They propose an adaptive modality selection framework for multimodal emotion recognition in conversation that integrates all available modalities into one .
Outcome:	The proposed framework outperforms existing methods on multimodal dialogue datasets and is available at https://github.com/youflyaway/Modality-Selection-Enhanced-LoRA-Tuned-LLMs.

Addressing Order Sensitivity of In-Context Demonstration Examples in Causal Language Models (2024.findings-acl)

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Yanzheng Xiang, Hanqi Yan, Lin Gui, Yulan He

Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space (2026.findings-acl)

Challenge:	In-context learning is a popular paradigm in natural language processing, but its performance can be significantly influenced by the order of in-concept demonstration examples.
Approach:	They propose an unsupervised fine-tuning method to reduce the sensitivity of causal language models to the order of in-context demonstration examples.
Outcome:	The proposed method reduces the sensitivity of CausalLMs to the order of in-context examples and exhibits robust generalizability.

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Xiang Zhang, Kun Wei, Xu Yang, Jiahua Li, Su Yan, Cheng Deng

Challenge:	Existing methods to remove undesirable data from Large Language Models suffer from cumulative catastrophic utility loss under continuous unlearning requests.
Approach:	They propose a method that leverages the rotational salience weight of RCU to quantify and control the unlearning degree in the continuous unlearning process.
Outcome:	The proposed method achieves SOTA performance without a retained dataset.

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information (2020.findings-emnlp)

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Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, Xiang Wan

Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)

Challenge:	Existing studies have shown that named entity recognition (NER) is effective in encoding and aggregating syntactic information, but they lack the appropriate knowledge to model such properties.
Approach:	They propose to leverage syntactic information by leveraging attentive ensembles to model NER . they propose key-value memory networks, syntax attention and gate mechanism for encoding, weighting and aggregating syntaktic information.
Outcome:	The proposed model outperforms previous studies on six English and Chinese benchmark datasets.

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Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang

Semantic Text Processing

Challenge:	a rapid advancement of perovskite solar cells has led to an exponential growth in research publications.
Approach:	They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components.
Outcome:	The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks.

ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems (2020.acl-demos)

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Qi Zhu, Zheng Zhang, Yan Fang, Xiang Li, Ryuichi Takanobu, Jinchao Li, Baolin Peng, Jianfeng Gao, Xiaoyan Zhu, Minlie Huang

Natural Language Interfaces

Challenge:	ConvLab-2 inherits Convlab's framework but integrates more powerful dialogue models and supports more datasets.
Approach:	They present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models and perform an end-to-end evaluation.
Outcome:	The new tool inherits ConvLab's framework and extends it by integrating many recently proposed state-of-the-art dialogue models.

AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding (2021.acl-long)

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Jun Yan, Nasser Zalmout, Yan Liang, Christan Grant, Xiang Ren, Xin Luna Dong

Measure Children’s Mindreading Ability with Machine Reading (2023.findings-emnlp)

Challenge:	Existing approaches to extract product attribute values are limited by knowledge sharing across different attributes.
Approach:	They propose to use adaptive decoding to handle extraction of product attribute values by parameterizing the decoder with pretrained attribute embeddings, through a hypernetwork and a Mixture-of-Experts module.
Outcome:	The proposed model is able to handle multiple attributes without sharing the entire network parameters across all attributes.

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Yuliang Yan, Xiaohua Wang, Xiang Zhou, Xiaoqing Zheng, Xuanjing Huang

Visual Data in NLP

Challenge:	Existing scoring models do not take the features of the stories and video clips into account when scoring, which will reduce the accuracy of the models.
Approach:	They propose to leverage the features extracted from stories and videos related to the questions being asked during the children’s mindreading evaluation.
Outcome:	The proposed framework agrees well with human experts on scores produced by the models.

Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025.emnlp-main)

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Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, Polydoros Giannouris, Ruoyu Xiang, Yan Wang, Lingfei Qian, Jimin Huang, Qianqian Xie, Sophia Ananiadou

Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (2021.acl-long)

Challenge:	Greek is the dominant language of the world's merchant navy and is a key language for international trade.
Approach:	They propose to develop a Greek financial evaluation benchmark and a financial LLM fine-tuned on Greek-specific financial data to bridge this gap.
Outcome:	The proposed benchmarks surpass GPT-4 by 8.33%, GPT- 4o by 26.83%, and Deepseek-V3 by 67.74%.

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Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan