Papers by Yilin Yang
EventRAG: Enhancing LLM Generation with Event Knowledge Graphs (2025.acl-long)
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Zairun Yang, Yilin Wang, Zhengyan Shi, Yuan Yao, Lei Liang, Keyan Ding, Emine Yilmaz, Huajun Chen, Qiang Zhang
| Challenge: | Existing approaches to text generation often neglect event structures that shape real-world narratives. |
| Approach: | They propose a framework that integrates structured event semantics with iterative retrieval and inference to enhance text generation. |
| Outcome: | Experiments on UltraDomain and MultiHopRAG show that the proposed framework outperforms baseline RAG systems in generation effectiveness, logical consistency, and multi-hop reasoning accuracy. |
Each graph is a new language: Graph Learning with LLMs (2025.findings-acl)
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| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)
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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang
| Challenge: | Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation. |
| Approach: | a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 . |
| Outcome: | a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 . |
Offline Reinforcement Learning for LLM Multi-step Reasoning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly applied to complex tasks requiring multi-step reasoning. |
| Approach: | They propose an offline method for enhancing multi-step reasoning by optimizing the soft Bellman Equation by combining a policy model and a value function. |
| Outcome: | The proposed method surpasses existing methods on multi-step reasoning benchmarks and can be extended to multi-iteration frameworks when additional resources are available. |
On the Sub-layer Functionalities of Transformer Decoder (2020.findings-emnlp)
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| Challenge: | Existing efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation have focused on assessing the encoded representations or interpreting the multi-head self-attentions. |
| Approach: | They propose to use Transformer-based encoder-decoder architectures to analyze how information is propagated through each module of each decoder layer. |
| Outcome: | The proposed model can be dropped with minimal loss of performance on three translation datasets and can be used to train and inference faster. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
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Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
Improving Multilingual Translation by Representation and Gradient Regularization (2021.emnlp-main)
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| Challenge: | Multilingual Neural Machine Translation models often produce low quality translations, often failing to produce outputs in the right target language. |
| Approach: | They propose a joint approach to regularize NMT models at both representation-level and gradient-level to reduce off-target translation occurrences and improve zero-shot translation performance. |
| Outcome: | The proposed approach reduces off-target translation occurrences and improves zero-shot translation performance by +5.59 and +10.38 BLEU on WMT and OPUS datasets. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
UCFE: A User-Centric Financial Expertise Benchmark for Large Language Models (2025.findings-naacl)
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Yuzhe Yang, Yifei Zhang, Yan Hu, Yilin Guo, Ruoli Gan, Yueru He, Mingcong Lei, Xiao Zhang, Haining Wang, Qianqian Xie, Jimin Huang, Honghai Yu, Benyou Wang
| Challenge: | Recent advances in large language models (LLMs) have expanded their potential applications in finance. |
| Approach: | They propose a framework to evaluate the ability of large language models to handle financial tasks using human expert evaluations and task-specific interactions. |
| Outcome: | The proposed framework evaluates the ability of large language models to handle complex financial tasks and combines human expert evaluations with dynamic, task-specific interactions to simulate the complexities of evolving financial scenarios. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020.acl-main)
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| Challenge: | Existing studies in multimodal sentiment analysis only use unified multimodal annotations, which do not reflect the independent sentiment of single modalities. |
| Approach: | They propose a Chinese single- and multi-modal sentiment analysis dataset with multimodal and independent unimodal annotations that can be used to study the interaction between modalities. |
| Outcome: | The proposed methods achieve state-of-the-art performance and learn more distinctive unimodal representations. |
CIRAG: Construction–Integration Retrieval and Adaptive Generation for Multi-hop Question Answering (2026.acl-long)
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| Challenge: | Existing methods for iterative retrieval-augmented generation (iRAG) suffer from greedy single-path expansion and granularity–demand mismatch . |
| Approach: | They propose a model that constructs candidate triples and history-conditionally integrates them to distill core triples to generate the next-hop query. |
| Outcome: | The proposed model mitigates the greedy single-path expansion and granularity–demand mismatch by preserving multiple plausible evidence chains. |
Human-Agent Collaborative Paper-to-Page Crafting (2026.findings-acl)
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Qianli Ma, Siyu Wang, Chen Yilin, Yinhao Tang, Yixiang Yang, Chang Guo, Bingjie Gao, Zhening Xing, Yanan Sun, Zhipeng Zhang
| Challenge: | Existing approaches to create project pages from academic papers have focused on static slides and posters, but the dynamic nature of webpages remains an unaddressed challenge. |
| Approach: | They propose a novel multi-agent system that deconstructs paper-to-page creation into a coarse-to fine pipeline from narrative planning to multimodal content generation and interactive rendering. |
| Outcome: | The proposed system generates high-quality, visually appealing pages in under 15 minutes for less than $0.1 . |
LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026.acl-long)
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Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang
| Challenge: | Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) however, traditional RAG attacks are difficult to pose an effective threat to GraphRAg systems. |
| Approach: | They propose a novel attack framework that targets logical reasoning rather than injecting false contents into GraphRAG systems by grounding their responses in structured knowledge graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art attacks on GraphRAG systems in both effectiveness and stealth. |
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion (2024.lrec-main)
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| Challenge: | Knowledge graph completion (KGC) is a critical task to predict missing facts among entities. |
| Approach: | They propose a knowledge-constrained generative re-ranking method based on generative large language models for KGC that can predict missing facts among entities. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets and 9.0% and 11.1% compared to the previous methods. |
cAST: Enhancing Code Retrieval-Augmented Generation with Structural Chunking via Abstract Syntax Tree (2025.findings-emnlp)
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| Challenge: | Existing line-based chunking heuristics often break semantic structures, splitting functions or merging unrelated code. |
| Approach: | They propose a structure-aware method that breaks large AST nodes into smaller chunks . this method generates self-contained, semantically coherent units across programming languages . |
| Outcome: | The proposed method boosts Recall@5 by 4.3 points on RepoEval retrieval and Pass@1 by 2.67 points on SWE-bench generation. |
Language-Informed Beam Search Decoding for Multilingual Machine Translation (2024.findings-acl)
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| Challenge: | Beam search decoding is the de-facto method for decoding auto-regressive Neural Machine Translation (NMT) models, but decoding multilingual NMT models produces off-target translations . |
| Approach: | They propose a general decoding algorithm incorporating an off-the-shelf Language Identification (LiD) model into beam search decoding to reduce off-target translations. |
| Outcome: | The proposed language-informed beam search improves +1.1 BLEU and +0.9 BLUE on WMT and OPUS datasets and reduces off-target rates from 22.9% to 7.7% and 65.8% to 25.3% respectively. |
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance (2026.acl-long)
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Yuchun Fan, Bei Li, Peiguang Li, Yilin Wang, Yongyu Mu, Jian Yang, Xin Chen, Rongxiang Weng, Jingang Wang, Xunliang Cai, JingBo Zhu, Tong Xiao
| Challenge: | Existing methods for enhancing multi-step reasoning have not fully translated to multilingual contexts. |
| Approach: | They propose a framework that leverages language-conditioned hints to guide exploration in non-English reasoning tasks. |
| Outcome: | Empirical results show that the proposed framework improves reasoning performance without compromising language consistency. |