Papers by Jialiang Xu
VANE: Guiding High-Value Exploration in RLVR via Outcome-Process Novelty Shaping (2026.findings-acl)
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| Challenge: | Extensive experiments on large-scale mathematical reasoning and out-of-distribution tasks demonstrate the effectiveness and generalization of the proposed method. |
| Approach: | They propose a method that quantifies novelty across the outcome space and semantic process space by using reward or solution divergence. |
| Outcome: | Experiments on Qwen2.5-Math-7B demonstrate the proposed method is general and efficient. |
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems (2022.emnlp-main)
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| Challenge: | Numerical Question Answering is the task of answering questions that require numerical capabilities. |
| Approach: | They propose to conduct numerical capability diagnosis on a series of Numerical Question Answering systems and datasets. |
| Outcome: | The proposed approach relieves existing systems’ lack of robust numerical capabilities. |
SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing (2024.findings-acl)
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| Challenge: | SPAGHETTI: Semantic Parsing Augmented Generation for Hybrid English information from Text Tables and Infoboxes is a hybrid question-answering pipeline . |
| Approach: | They propose a hybrid question-answering pipeline that leverages knowledge from multiple knowledge sources. |
| Outcome: | The proposed approach achieves state-of-the-art on the Compmix dataset with 56.5% exact match rate. |
Turning Failures into Value: Negative Experience Replay for RLVR via Confidence Gating and Boundary Failure Sampling (2026.acl-long)
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| Challenge: | Existing experience replay methods for RLVR ignore sample inefficiency . expensive reasoning trajectories are discarded immediately after a single gradient update . |
| Approach: | They propose a method to replay failure trajectories to improve model refinement . they propose 'nexGRPO' which employs mid-confidence gating to filter invalid noise and saturated errors. |
| Outcome: | The proposed model outperforms strong baaselines and achieves improved out-of-distribution generalization. |
AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks (2023.findings-acl)
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Xinyi He, Mengyu Zhou, Mingjie Zhou, Jialiang Xu, Xiao Lv, Tianle Li, Yijia Shao, Shi Han, Zejian Yuan, Dongmei Zhang
| Challenge: | Tabular data analysis is performed everyday across various domains. |
| Approach: | They propose to use a dataset of 467k tables with supervision labels for four types of field metadata. |
| Outcome: | The proposed framework improves the understanding capability of tabular models by incorporating distribution and knowledge information. |
SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have led to significant improvements in the Knowledge Base Question Answering task. |
| Approach: | They introduce an expert-annotated KBQA dataset from Wikidata’s “Request a Query” forum with 320 decontextualized question-SPARQL pairs. |
| Outcome: | The SPINACH dataset outperforms baselines on the QALD-7, QADL-9 Plus and QAL-10 datasets by 31.0%, 27.0% and 10.0% in F1 respectively. |
SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models (2024.findings-naacl)
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| Challenge: | SUQL is a conversational language that supports the generality of hybrid data access for large knowledge corpora. |
| Approach: | They propose a conversational agent that supports the full generality of hybrid data access for large knowledge corpora using SUQL. |
| Outcome: | The proposed language can handle hybrid data sources. |
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction (2021.emnlp-main)
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Mahsa Yarmohammadi, Shijie Wu, Marc Marone, Haoran Xu, Seth Ebner, Guanghui Qin, Yunmo Chen, Jialiang Guo, Craig Harman, Kenton Murray, Aaron Steven White, Mark Dredze, Benjamin Van Durme
| Challenge: | Zero-shot cross-lingual information extraction (IE) is a technique for training data in a source language but not in . |
| Approach: | They explore techniques including data projection and self-training to improve zero-shot cross-lingual information extraction (IE) IE is a construction of an IE model for some target language given existing annotations exclusively in English. |
| Outcome: | The proposed techniques show that they perform better than any single strategy. |
Do LLMs Know to Respect Copyright Notice? (2024.emnlp-main)
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| Challenge: | Existing studies have focused on the occurrence of copyright violations in LLM output, but a negative answer would suggest that LLMs will become the primary facilitator and accelerator of copy right infringement behavior. |
| Approach: | They propose to examine whether LLMs respect copyright information in user input . they use a set of language models, user prompts, and copyrighted materials . |
| Outcome: | The proposed model will be the primary facilitator and accelerator of copyright infringement behavior, the study finds . the study also provides a benchmark dataset serving as a test bed for evaluating infringement behaviors by LLMs . |
Profiling LLM’s Copyright Infringement Risks under Adversarial Persuasive Prompting (2025.findings-emnlp)
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| Challenge: | Large Language Models have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement. |
| Approach: | They propose a structured persuasion workflow to analyze the influence of persuasive prompts on LLM outputs. |
| Outcome: | The proposed method analyzes the influence of persuasive prompts on LLM outputs. |