Papers by Yi Luo
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)
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Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang
| Challenge: | Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies. |
| Approach: | They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states. |
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
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| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)
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| Challenge: | Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning. |
| Approach: | They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion . |
| Outcome: | The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action. |
LJPCheck: Functional Tests for Legal Judgment Prediction (2024.findings-acl)
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| Challenge: | Existing LJP models fail to evaluate specific aspects of their performance, such as legal fairness and judicial fairness. |
| Approach: | They propose a suite of functional tests for LJP models to comprehend LJp models’ behaviors and offer diagnostic insights. |
| Outcome: | Extensive tests reveal weaknesses in LJP models and provide diagnostic insights. |
Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)
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| Challenge: | Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans. |
| Approach: | They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles. |
| Outcome: | The proposed framework combines adaptive temporal sampling with dynamic and static role profiles. |
MASTER: Multi-Agent Security Through Exploration of Roles and Topological Structures - A Comprehensive Framework (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs)-based Multi-Agent Systems (MAS) exhibit remarkable problem-solving and task planning capabilities across diverse domains . |
| Approach: | They propose a security research framework for LLM-based multi-agent systems . they propose corresponding defense strategies to address MAS security risks . |
| Outcome: | The proposed framework amplifies the severity of security risks under MAS attacks . it offers an automated construction process for different MAS setups and an interaction paradigm . |
Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)
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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu
| Challenge: | Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization. |
| Approach: | They propose a memory guideline optimization framework that learns how memory should be organized and what information to update. |
| Outcome: | The proposed framework learns how memory should be organized and what information to update. |
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods for fake news detection focus on fact-checked reports, resulting in limited coverage and debunking delays. |
| Approach: | They propose a Coarse-to-fine Cascaded Evidence-Distillation neural network for explainable fake news detection based on raw reports . they use hierarchical encoders and cascaded selectors to select most explainable sentences for verdicts on top of selected top-K reports based upon raw reports. |
| Outcome: | The proposed model outperforms baseline detection methods and generates high-quality explanations from diverse evaluation perspectives. |
Don’t Miss the Potential Customers! Retrieving Similar Ads to Improve User Targeting (2021.findings-emnlp)
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| Challenge: | a method for user targeting is developed to identify online users to whom an ad should be targeted. |
| Approach: | They propose a method for automatic augmentation of positive and negative clickthrough data for user targeting models. |
| Outcome: | The proposed method can increase positive and negative instances of positive training instances on two datasets. |
Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding (N18-2)
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| Challenge: | Entity recognition is a widely benchmarked task in natural language processing . a neural architecture called BiLSTM-CRF is used to model the language sequences . |
| Approach: | They propose a neural architecture called BiLSTM-CRF to model the language sequences. |
| Outcome: | The proposed system achieves state-of-the-art on English entity recognition task and also in other languages. |
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)
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| Challenge: | Existing image captioning models require large trainable parameters to bridge visual and textual representations. |
| Approach: | They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model. |
| Outcome: | The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
| Challenge: | Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. |
| Approach: | They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability. |
| Outcome: | The proposed system can understand and remediate norm violations step by step. |
Together We Make Sense–Learning Meta-Sense Embeddings (2023.findings-acl)
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| Challenge: | Existing sense embeddings do not cover all senses of ambiguous words equally well due to discrepancies in their training resources. |
| Approach: | They propose a meta-sense embedding method that preserves sense neighbourhoods by combining multiple independently trained source sense embeddables. |
| Outcome: | The proposed method outperforms several baselines on Word Sense Disambiguation and Word-in-Context tasks. |
Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are sensitive to the contextual position of information in input. |
| Approach: | They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions. |
| Outcome: | Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures. |
FAIRGAMER: Evaluating Social Biases in LLM-Based Video Game NPCs (2026.acl-long)
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Bingkang Shi, Jen-tse Huang, Luo Long, Tianyu Zong, Hongzhu Yi, Yuanxiang Wang, Songlin Hu, Xiaodan Zhang, Zhongjiang Yao
| Challenge: | Large Language Models (LLMs) have enhanced or replaced traditional non-player characters in video games. |
| Approach: | They propose a benchmark to evaluate social biases across three interaction patterns: transaction, cooperation, and competition. |
| Outcome: | The proposed benchmark assesses four bias types across transaction, cooperation, and competition using a novel metric, FairMCV. |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization (2025.findings-emnlp)
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| Challenge: | Existing ESC data entangles psychological strategies and response content, making it difficult to construct high-quality preference pairs. |
| Approach: | They propose a Decoupled ESC framework that decomposes the ESC task into two sequential subtasks: strategy planning and empathic response generation. |
| Outcome: | The proposed framework outperforms baselines, reducing preference bias and improving response quality. |
Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models (2024.findings-naacl)
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| Challenge: | Chain-of-thought (CoT) prompting is a technique to enhance the reasoning abilities of Large language models (LLMs) however, the reasoning chains of demonstrations are observed to be prone to errors, which can lead to incorrect reasoning during inference. |
| Approach: | They propose an iterative bootstrapping technique to enhance the reasoning abilities of Large language models (LLMs) by generating a series of reasoning steps to obtain the answer, and using the reasoning chains as exemplars to demonstrate the task. |
| Outcome: | The proposed method improves the performance of Large language models (LLMs) on three reasoning tasks on ten datasets. |
Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)
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| Challenge: | Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio. |
| Approach: | They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
| Outcome: | The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search. |
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks (2021.findings-emnlp)
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| Challenge: | Chinese has no word delimiter or inflection that can indicate segment boundaries or word semantics, increasing the difficulty of segmenting and labeling tasks. |
| Approach: | They propose a paradigm based on attention augmentation to introduce crucial cross-domain knowledge via a translation system into Chinese model. |
| Outcome: | The proposed model significantly advances the state-of-the-art results of Chinese cross-domain segmenting and labeling tasks. |
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)
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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo
| Challenge: | a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Approach: | They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Outcome: | The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains. |
AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)
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Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng, Qi Liu
| Challenge: | Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias. |
| Approach: | They propose a diagnostic benchmark for instruction-grounded financial code generation under strict semantic and temporal constraints. |
| Outcome: | The proposed benchmarks show that the models fail under causal, structural, or functional constraints. |
OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)
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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou
| Challenge: | Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs. |
| Approach: | They propose a framework that integrates dialogue, reasoning, and personalized recommendation. |
| Outcome: | Experiments across public benchmarks show state-of-the-art performance. |
Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models (2024.naacl-long)
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Yi Luo, Zhenghao Lin, YuHao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong
| Challenge: | Guide-Align is a guideline-oriented approach to augment the safety and quality of Large Language Models. |
| Approach: | They propose a guideline-oriented method to augment the safety and quality of large language models. |
| Outcome: | The proposed method outperforms existing methods on three benchmarks and shows significant improvements in security and quality. |