Papers by Xiaoyu Zhu
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)
Copied to clipboard
Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following. |
| Approach: | They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply. |
| Outcome: | The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply. |
Exploring Decomposition for Table-based Fact Verification (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research focuses on fact verification based on unstructured text, but structured data is becoming more prevalent. |
| Approach: | They propose to decompose complex statements into simpler subproblems to improve table-based verification by a weakly supervised parser. |
| Outcome: | The proposed method achieves state-of-the-art accuracy on the TabFact benchmark. |
Weaker Than You Think: A Critical Look at Weakly Supervised Learning (2023.acl-long)
Copied to clipboard
| Challenge: | Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. |
| Approach: | They propose to use weakly supervised learning to train models with noisy labels from weak sources instead of collecting expensive human annotations. |
| Outcome: | The proposed methods outperform weakly supervised methods on various NLP datasets and tasks on the test sets. |
A Preference-driven Paradigm for Enhanced Translation with Large Language Models (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent research shows that large language models (LLMs) can achieve remarkable translation performance through supervised fine-tuning (SFT) however, SFT simply instructs the model to imitate reference translations token by token, making it vulnerable to the noise present in the data. |
| Approach: | They propose a preference-based approach to supervised fine-tuning that trains the model to imitate reference translations token by token, making it vulnerable to noise. |
| Outcome: | The proposed approach overcomes the plateau associated with imitation-based SFT and is more resilient in the absence of gold translations. |
Meta Self-Refinement for Robust Learning with Weak Supervision (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent methods leverage self-training to build noise-resistant models . however, the teacher trained under weak supervision may have fitted a substantial amount of noise and therefore produce incorrect pseudo-labels. |
| Approach: | They propose a framework that encourages teacher to refine its pseudo-labels to effectively combat label noise from weak supervision. |
| Outcome: | The proposed framework outperforms state-of-the-art methods by 11.4% in accuracy and 9.26% in F1 score on eight NLP benchmarks. |
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)
Copied to clipboard
Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). |
| Approach: | a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance. |
| Outcome: | a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models . |
CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a global audience, so alignment must extend to cultural resonance. |
| Approach: | They propose a framework that frames alignment as a conditional capacity separation problem. |
| Outcome: | The proposed framework outperforms both dense baselines and semantic-only MoEs on three large language models. |
Neural Data-to-Text Generation with LM-based Text Augmentation (2021.eacl-main)
Copied to clipboard
| Challenge: | Neural data-to-text generation is a difficult task for many new applications because of a lack of training data. |
| Approach: | They propose a few-shot approach that augments the data available for training by generating new text samples based on replacing specific values by alternative ones from the same category and pairing the new text with data samples. |
| Outcome: | The proposed approach outperforms fully supervised sequence-to-sequence models with less than 10% of the training set on both datasets. |
Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for fact verification based on structured data are challenging and require further study. |
| Approach: | They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models. |
| Outcome: | The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models . |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
Copied to clipboard
Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |
Fine-Tuning Large Language Models to Translate: Will a Touch of Noisy Data in Misaligned Languages Suffice? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
| Approach: | They revisit the importance of large language models for translation by fine-tuning on 32 parallel sentences. |
| Outcome: | The proposed model can be fine-tuned on as few as 32 parallel sentences . however, the choice of direction is critical to avoid misinterpretation, the authors say . |
The Accuracy Paradox in RLHF: When Better Reward Models Don’t Yield Better Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) significantly enhances Natural Language Processing by aligning language models with human expectations. |
| Approach: | They propose to integrate feedback from humans into RLHF to improve language models by capturing human-like preferences. |
| Outcome: | The proposed model outperforms models trained with moderately accurate reward models on relevance, factuality, and completeness tasks. |
InternLM-Law: An Open-Sourced Chinese Legal Large Language Model (2025.coling-main)
Copied to clipboard
| Challenge: | InternLM-Law is a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Approach: | They introduce a large language model (LLM) tailored for addressing diverse legal tasks related to Chinese laws. |
| Outcome: | The proposed model performs better than existing models in a variety of legal tasks related to Chinese laws. |
Point, Disambiguate and Copy: Incorporating Bilingual Dictionaries for Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to incorporate bilingual dictionaries into Neural Machine Translation (NMT) models have been criticized for lack of integration of bilingual lexical information into the neural architecture. |
| Approach: | They propose a neural architecture to incorporate bilingual dictionaries into Neural Machine Translation models by introducing three new components: Pointer, Disambiguator, and Copier. |
| Outcome: | The proposed method achieves the following merits inherently compared with previous efforts: (1) Pointer leverages the semantic information from bilingual dictionaries, for the first time, to better locate source words whose translation in dictionary can potentially be used; (2) Disambiguator synthesizes contextual information from source view and target view, both of which contribute to distinguishing translation of a specific source word from multiple candidates in dicaries; (3) Copier systematically connects Pointer and Disambiguators based on a hierarchical |
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability . |
| Approach: | They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks. |
| Outcome: | The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers . |
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive. |
| Approach: | They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models. |
| Outcome: | The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models. |
To Preserve or To Compress: An In-Depth Study of Connector Selection in Multimodal Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Recent multimodal large language models (MLLMs) have attracted widespread attention from both industry and academia due to their potential to handle multiple modalities in a unified framework. |
| Approach: | They propose to classify connectors into feature-preserving and feature-compressing types and categorize tasks into three task types: coarse-grained perception, fine-grain perception, and reasoning. |
| Outcome: | The proposed architectures perform better on tasks with varying granularities than on external fusion architectures. |
Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference (2022.tacl-1)
Copied to clipboard
| Challenge: | a neural network model for natural language inference (NLI) is proposed. |
| Approach: | They propose a neuro-symbolic natural logic framework based on reinforcement learning with introspective revision that rewards specific reasoning paths through policy gradients. |
| Outcome: | The proposed model shows superior capability in monotonicity inference, generalization, and interpretability compared with previous models on the existing datasets. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
Copied to clipboard
Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Fine-Grained and Multi-Dimensional Metrics for Document-Level Machine Translation (2025.naacl-srw)
Copied to clipboard
| Challenge: | Large language models excel in machine translation, but most studies focus on sentence-level translation. |
| Approach: | They propose to use LLMs as a judge paradigm to evaluate document-level translations by directly prompting them to translate entire documents in a single pass. |
| Outcome: | The proposed method improves translation quality even without document-level fine-tuning compared to translating sentences separately . |
Assessing “Implicit” Retrieval Robustness of Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) is a framework to enhance large language models with external knowledge, but its effectiveness is constrained by the retrieval robustness of the model. |
| Approach: | They propose to use gold and distracting context to fine-tune models to handle relevant or irrelevant retrieved context in an end-to-end manner. |
| Outcome: | The proposed model performs better when gold and distracting context are used, while still extracting correct answers when retrieval is accurate. |