Papers by Bo Luo

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)

Challenge:	Existing word embeddings are data intensive and require large-scale training corpus, sufficient training iterations, and high computational capacity.
Approach:	They propose a framework that infers context distributions from a downstream dataset and then uses them to fine-tune the embedding.
Outcome:	The proposed framework materializes privacy concern by inferring context distribution in the downstream dataset, which can lead to key information breach.

Copied to clipboard

Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

Syntactic Text Processing

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Copied to clipboard

Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

Beyond Linguistic Cues: Fine-grained Conversational Emotion Recognition via Belief-Desire Modelling (2024.lrec-main)

Copied to clipboard

Bo Xu, Longjiao Li, Wei Luo, Mehdi Naseriparsa, Zhehuan Zhao, Hongfei Lin, Feng Xia

Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)

Challenge:	Emotion recognition in conversation (ERC) is essential for dialogue systems to identify the emotions expressed by speakers.
Approach:	They propose a method that incorporates both belief and desire to accurately identify emotions by extracting emotion-eliciting events from utterances and construct graphs that represent beliefs and desires in conversations.
Outcome:	The proposed model outperforms existing models on four popular ERC datasets and validates its performance with multiple state-of-the-art models.

Copied to clipboard

Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, Bo Wang

Text Generation

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.

DisCal: Distribution-Aware Calibration for Mathematical Reasoning Under Character-Level Noisy Inputs (2026.acl-long)

Copied to clipboard

Bo Zhang, Jiawei Zhang, Cong Gao, Bingxu Han, Minghao Hu, Jun Zhang, Yunbo Cao, Zhunchen Luo, Wen Yao, Guotong Geng, Zhong Wang

Reasoning

Challenge:	Existing methods for calibration of large reasoning models (LRMs) focus on clean inputs, leaving noise unexplored.
Approach:	They propose a confidence calibration framework for character-level noisy inputs that extracts uncertainty signals from both the empirical answer distribution and the model’s predictive distribution and integrates them via a learned calibrator.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that DisCal outperforms existing calibration methods under noisy inputs, reducing expected calibration error (ECE) by up to 39.21% and improving Area Under the Receiver Operating Characteristic Curve (AUROC) by 31.44%.

Beyond Black-Box Interventions: Latent Probing for Faithful Retrieval-Augmented Generation (2026.findings-acl)

Copied to clipboard

Linfeng Gao, Qinggang Zhang, Baolong Bi, Bo Zeng, Zheng Yuan, Zerui Chen, Zhimin Wei, Shenghua Liu, Linlong Xu, Longyue Wang, Weihua Luo, Jinsong Su

Information Retrieval

Challenge:	Existing approaches to improve contextual faithfulness treat the LLM as a black box, generating responses that are inconsistent with the provided context.
Approach:	They propose a framework for faithful RAG that operates in three stages: (i) fine-grained knowledge pruning to filter irrelevant context, (ii) latent conflict probing to identify hard conflicts in the model’s latent space, and (iv) conflict-aware attention to modulate attention heads toward faithful context integration.
Outcome:	Experiments show that ProbeRAG significantly improves both accuracy and contextual faithfulness.

MAGRET: Machine-generated Text Detection with Rewritten Texts (2025.coling-main)

Copied to clipboard

Yifei Huang, Jiuxin Cao, Hanyu Luo, Xin Guan, Bo Liu

M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)

Challenge:	Existing studies focus on detecting machine-generated text in open-source models, but their performance on closed-source large models is limited.
Approach:	They propose a method to detect rewritten text from large language models using a BERT encoder and propose to refine it to achieve semantic alignment.
Outcome:	The proposed method outperforms baseline methods on three text-generated datasets.

Copied to clipboard

Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo

Machine Translation

Challenge:	prevailing methods for machine translation are often hindered by misleading reward signals.
Approach:	They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors .
Outcome:	The proposed framework outperforms open-source models and achieves parity with proprietary models.

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching (2026.acl-long)

Copied to clipboard

Bo Lv, Jingbo Sun, Jianwei Lv, Chen Tang, Shaojie Zhang, Nayu Liu, Guoxin Yu, Zihao Li, Qichao Zhang, Dongbin Zhao, Ping Luo, Yue Yu

Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)

Challenge:	Existing routing methods rely on direct mapping from queries to models based on surface-level features, leading to poor generalizability on out-of-distribution data.
Approach:	They propose a new routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs.
Outcome:	The proposed framework improves matching accuracy while lowering inference costs . it decouples linguistic surface forms from task-intrinsic requirements .

Copied to clipboard

Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang

Multilinguality

Challenge:	Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences .
Approach:	They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification .
Outcome:	The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization.

Prototype Tuning: A Meta-Learning Approach for Few-Shot Document-Level Relation Extraction with Large Language Models (2025.findings-naacl)

Copied to clipboard

Dinghao Pan, Yuanyuan Sun, Bo Xu, Jiru Li, Zhihao Yang, Ling Luo, Hongfei Lin, Jian Wang

Language Constrained Multimodal Hyper Adapter For Many-to-Many Multimodal Summarization (2025.acl-long)

Challenge:	Few-Shot Document-Level Relation Extraction (FSDLRE) aims to develop models capable of generalizing to new categories with minimal support examples.
Approach:	They propose a meta-training approach to train Large Language Models to improve their ICL capabilities . they construct simulated episodes using relation types that do not overlap with test corpus .
Outcome:	Experimental results show that the proposed approach outperforms baseline models on few-shot tasks.

Copied to clipboard

Nayu Liu, Fanglong Yao, Haoran Luo, Yong Yang, Chen Tang, Bo Lv

AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)

Challenge:	Existing models that share parameters neglect the language-specific knowledge learning.
Approach:	They propose a language-constrained multimodal hyper adapter for multimodal summarization that integrates language-specific adapters into multilingual pre-trained backbones.
Outcome:	The proposed model can generate summaries based on multimodal documents such as text and visuals, allowing people to quickly locate key information from the vast multimedia con.

Copied to clipboard

Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng

Breaking the Boundaries: A Unified Framework for Chinese Named Entity Recognition Across Text and Speech (2024.findings-emnlp)

Challenge:	Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content.
Approach:	They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories .
Outcome:	The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources.

Copied to clipboard

Jinzhong Ning, Yuanyuan Sun, Bo Xu, Zhihao Yang, Ling Luo, Hongfei Lin

URG: A Unified Ranking and Generation Method for Ensembling Language Models (2024.findings-acl)

Challenge:	Existing approaches to Named Entity Recognition (NER) tasks are limited by the complexity of the data and the potential connections between tasks.
Approach:	They propose a task to break the boundaries between different modal NER tasks by using a unified data format for inputs from different modalités.
Outcome:	The proposed task breaks the boundaries between different modal NER tasks and is a unified implementation of them.

Copied to clipboard

Bo Lv, Chen Tang, Yanan Zhang, Xin Liu, Ping Luo, Yue Yu

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

Challenge:	Existing approaches to rank and generate large language models have limited performance due to time-intensive nature of ranking process and lack of error propagation.
Approach:	They propose a framework that jointly ranks the outputs of Large Language Models and generates fine-grained fusion results.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on ranking and generation tasks.

Copied to clipboard

Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

TAeKD: Teacher Assistant Enhanced Knowledge Distillation for Closed-Source Multilingual Neural Machine Translation (2024.lrec-main)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

Copied to clipboard

Bo Lv, Xin Liu, Kaiwen Wei, Ping Luo, Yue Yu

R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have produced impressive results in the field of Multilingual Neural Machine Translation (MNMT).
Approach:	They propose a Teacher Assistant enhanced Knowledge Distillation method to augment knowledge transfer capacity from closed-source MNMT models.
Outcome:	The proposed method outperforms the state-of-the-art KD methods on both WMT22 and FLORES-101 test sets.

Copied to clipboard

Yuan Li, Qi Luo, Xiaonan Li, Bufan Li, Qinyuan Cheng, Bo Wang, Yining Zheng, Yuxin Wang, Zhangyue Yin, Xipeng Qiu

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning (2026.findings-acl)

Challenge:	RAG systems that integrate external knowledge with Large Language Models often become bottlenecks due to their limited parameters compared to LLMs and their inability to perform step-by-step reasoning.
Approach:	They propose a model that integrates external knowledge with Large Language Models to enhance factual correctness and mitigate hallucination.
Outcome:	The proposed model outperforms baselines and can transfer well to different retrievers.

Copied to clipboard

Zhiyin Yu, Bo Zhang, Qibin Hou, Zhonghai Wu, Xiao Luo, Lei Bai

Supervised Treebank Conversion: Data and Approaches (P18-1)

Challenge:	Experimental results show that EasyRL consistently outperforms state-of-the-art baselines due to the substantial annotation cost and issues such as model collapse or reward hacking.
Approach:	They propose a supervised RL approach with a divide-and-conquer strategy that simulates the human cognitive acquisition curve using easy labeled data.
Outcome:	The proposed approach outperforms state-of-the-art models on mathematical and scientific benchmarks using only 10% of easy labeled data.

Copied to clipboard

Xinzhou Jiang, Zhenghua Li, Bo Zhang, Min Zhang, Sheng Li, Luo Si

Syntactic Text Processing

Challenge:	Existing work on treebank conversion focuses on unsupervised treebanks . lack of manually labeled data means that sentences have two syntactic trees at the same time.
Approach:	They propose supervised treebank conversion using bi-tree aligned sentences . they propose two conversion approaches based on state-of-the-art deep biaffine parser .
Outcome:	The proposed method outperforms the state-of-the-art deep biaffine parser on the English WSJ dataset by 0.97 (93.76% -92.79%)

DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)

Copied to clipboard

Xinyi Wang, Yiping Song, Chang Liu, Tingjin Luo, Bo Liu, Zheng Xie, Minlie Huang

Structured Data in NLP

Challenge:	Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps .
Approach:	They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step .
Outcome:	The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines.

A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)

Copied to clipboard

Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo

Naive Bayes-based Context Extension for Large Language Models (2024.naacl-long)

Challenge:	Existing research on reinforcement learning for LLMs under data scarcity has not been unified.
Approach:	They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric.
Outcome:	The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area.

Copied to clipboard

Jianlin Su, Murtadha Ahmed, Bo Wen, Luo Ao, Mingren Zhu, Yunfeng Liu

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have shown promising in-context learning abilities, but are often impeded by length limitations of transformer architecture.
Approach:	They propose a framework to enable existing Large Language Models to perform ICL with an increased number of demonstration examples by significantly expanding their context size.
Outcome:	The proposed framework outperforms existing methods in in-context learning with an increased number of demonstration examples while preserving linear efficiency.

Copied to clipboard

Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo, Guotong Geng, Xiaoying Bai, Jun Zhang, Wen Yao, Zhong Wang

NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)

Challenge:	Large language models (LLMs) have many advantages but they also pose significant safety risks.
Approach:	They propose a method to enhance the safety self-evaluation capability of LLMs . they perform semantic mutations on the original safety evaluation questions .
Outcome:	The proposed method improves safety self-evaluation accuracy by 5.86% and 7.79% over baseline methods on Chinese and English datasets.

Copied to clipboard

Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo

Unleashing the Native Recommendation Potential: LLM-Based Generative Recommendation via Structured Term Identifiers (2026.findings-acl)

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.

Copied to clipboard

Zhiyang Zhang, Junda She, Kuo Cai, Bo Chen, Shiyao Wang, Xinchen Luo, Qiang Luo, Ruiming Tang, Han Li, Kun Gai, Guorui Zhou

Robustness in NLP

Challenge:	Existing methods for constructing item identifiers face bottlenecks due to their large output space and expensive vocabulary expansion and alignment training.
Approach:	They propose to use Large Language Models to develop general-purpose, semantically-aware recommender systems that can be generalized and reusable.
Outcome:	Experiments on real-world datasets show that GRAM outperforms baselines and significantly outperformed baselines.

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Copied to clipboard

Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025.acl-long)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

Copied to clipboard

Huifeng Yin, Yu Zhao, Minghao Wu, Xuanfan Ni, Bo Zeng, Huaiyu.wh Huaiyu.wh, Tianqi Shi, Liangying Shao, Chenyang Lyu, Longyue Wang, Weihua Luo, Kaifu Zhang

Dynamic Evil Score-Guided Decoding: An Efficient Decoding Framework For Red-Team Model (2025.findings-acl)

Challenge:	Recent efforts to distill large reasoning models into smaller lightweight models have shown competitive performances.
Approach:	They propose to distill long Chain-of-Thought data to improve SFT and RL methods by constructing data from scratch using Monte Carlo Tree Search.
Outcome:	The proposed method significantly improves reasoning performance on various benchmarks such as math (GSM8K, MATH, AIME).

Copied to clipboard

Cong Gao, Bo Zhang, Linkang Yang, Minghao Hu, Zhunchen Luo, Xiaoying Bai, Guotong Geng, Jun Zhang, Yunhua Xue

Whether LLMs Know If They Know: Identifying Knowledge Boundaries via Debiased Historical In-Context Learning (2025.findings-acl)

Challenge:	Existing red-teaming methods require expensive fine-tuning, especially for large LLMs.
Approach:	They propose a red-teaming method that uses an ‘evil score’ to evaluate the potential of tokens to contribute to harmful outputs during decoding.
Outcome:	The proposed method achieves an ASR of 92.83% on the Llama-3.2-3B-Instruct model, compared to 83.48% with adversarial fine-tuning while using less computational resources.

Copied to clipboard

Bo Lv, Nayu Liu, Yang Shen, Xin Liu, Ping Luo, Yue Yu