Papers by Qianru Sun
Interventional Training for Out-Of-Distribution Natural Language Understanding (2022.emnlp-main)
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| Challenge: | Existing methods for NLU training use only known and single confounders, but in many NLU tasks the confounder can be unknown and multifactorial. |
| Approach: | They propose a method that performs multi-granular intervention with identified multifactorial confounders by using a bottom-up automatic intervention method. |
| Outcome: | The proposed method performs multi-granular intervention with identified multifactorial confounders on three NLU tasks, namely, natural language inference, fact verification and paraphrase identification. |
Reverse Modeling in Large Language Models (2025.naacl-short)
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| Challenge: | Using pre-trained LLMs with reversed text inputs can improve their performance across multiple languages. |
| Approach: | They propose a way to determine whether LLMs can understand reversed text inputs by reversing entire paragraphs or documents at the token level. |
| Outcome: | The proposed model can be used to improve understanding across multiple languages. |
Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)
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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen
| Challenge: | Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models. |
| Approach: | They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model. |
| Outcome: | The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD. |
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)
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| Challenge: | Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors. |
| Approach: | They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models. |
| Outcome: | The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks. |
COSY: COunterfactual SYntax for Cross-Lingual Understanding (2021.acl-long)
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| Challenge: | Pre-trained multilingual language models suffer from a large performance gap between source and target languages . e.g., multilingual-BERT models are widely used in cross-lingual tasks . |
| Approach: | They propose a language-agnostic approach to integrate universal syntax into language models . they use SYntax-aware networks and a COunterfactual training method . |
| Outcome: | The proposed model achieves state-of-the-art performance on natural language inference and question answering without auxiliary training data. |
Translate-Train Embracing Translationese Artifacts (2022.acl-short)
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| Challenge: | Existing approaches to train multilingual tasks are based on translationese and translatetrain. |
| Approach: | They propose to use translationese to mitigate the gap between the source and target languages to train the translator. |
| Outcome: | The proposed method outperforms baselines on the multilingual QA dataset TyDiQA. |