Papers by Xuanliang Zhang
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format (2026.findings-acl)
Copied to clipboard
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, Longxu Dou, Xianzhen Luo, Yingwei MA, Qingfu Zhu, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Prior work showed that multiple reasoning formats outperform a single format when generating multiple answers. |
| Approach: | They propose a method to measure reasoning error when generating multiple answers . they propose 'formatadapter' which generates and selects suitable reasoning formats . |
| Outcome: | The proposed method achieves a 4.3% performance improvement over previous works on math and commonsense reasoning tasks. |
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL (2025.coling-main)
Copied to clipboard
| Challenge: | Existing multi-hop retrieval of open-domain text-to-SQL tasks is not applicable due to the tendency to retrieve tables similar to those already retrieved but irrelevant to the question. |
| Approach: | They propose a multi-hop table retrieval with removal task to retrieve unretrieved tables from open-domain text-to-SQL databases. |
| Outcome: | The proposed method improves performance 5.7% over the previous state-of-the-art methods on open-domain text-to-SQL datasets. |
DAC: Decomposed Automation Correction for Text-to-SQL (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to improve text-to-SQL performance are hard to detect errors in SQL directly. |
| Approach: | They propose to use decomposed correction to improve text-to-SQL performance . they first detect errors based on decompose subtasks, then use it to correct them . |
| Outcome: | The proposed method improves text-to-SQL performance by 1.4% compared with previous methods . |
Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)
Copied to clipboard
| Challenge: | Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases. |
| Approach: | They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods. |
| Outcome: | The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness. |
Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes (2024.acl-long)
Copied to clipboard
| Challenge: | Numerical reasoning is an essential ability for NLP systems to handle numeric information. |
| Approach: | They propose a numerical reasoning method that generates reliable reasoning processes by decomposing the answer formula and aim to train models to generate the process with synthesized data. |
| Outcome: | The proposed method improves on all five datasets with an average improvement of 1.8% compared with baselines and gpt-3.5-turbo. |
RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in reasoning large language models (RLLMs) have significantly enhanced reasoning capabilities, leading to brilliant performance on table reasoning. |
| Approach: | They propose a method which performs iterative row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal. |
| Outcome: | Experiments show that the proposed method outperforms RLLMs on WikiTableQuestions and TableBench by 4.3% and achieves state-of-the-art results with comparable models. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
Copied to clipboard
Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs. |
| Approach: | They propose a method that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs. |
| Outcome: | The proposed method achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on mainstream datasets. |
MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing TATQA datasets are limited to English, leading to drawbacks . existing datasets overlook challenges of multilingual TAT-QA and do not reflect real-world multilingual scenarios . |
| Approach: | They propose a multilingual TATQA dataset that can be translated into 10 languages . they use data from 3 mainstream TATQ datasets and analyze the results . |
| Outcome: | The proposed dataset outperforms other baselines by an average of 3.3 . |