Papers by Yuxi Feng
Exploring Question-Specific Rewards for Generating Deep Questions (2020.coling-main)
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| Challenge: | Recent question generation approaches use the sequence-to-sequence framework to optimize the log likelihood of ground-truth questions using teacher forcing. |
| Approach: | They propose to optimize for QG-specific objectives via reinforcement learning to improve question quality. |
| Outcome: | The proposed model improves the fluency, relevance, and answerability of generated questions. |
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search (2026.findings-eacl)
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| Challenge: | Existing LLM-based agents struggle with low diversity and suboptimal code generation. |
| Approach: | They propose an approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. |
| Outcome: | The proposed approach shows a 4% improvement in performance compared to the strong open-source AutoML agents. |
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)
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Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
| Challenge: | Existing frameworks that increase context window do not guarantee robust performance across long input tasks. |
| Approach: | They propose a framework that enables language models to handle extended inputs within limited context windows efficiently. |
| Outcome: | The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks. |
Towards Human-aligned Evaluation for Linear Programming Word Problems (2024.lrec-main)
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Linzi Xing, Xinglu Wang, Yuxi Feng, Zhenan Fan, Jing Xiong, Zhijiang Guo, Xiaojin Fu, Rindra Ramamonjison, Mahdi Mostajabdaveh, Xiongwei Han, Zirui Zhou, Yong Zhang
| Challenge: | Existing evaluation methodologies for MWPs diverge from human judgment and face challenges in recognizing mathematically equivalent answers. |
| Approach: | They propose an evaluation metric rooted in graph edit distance that features benefits such as permutation invariance and more accurate program equivalence identification. |
| Outcome: | The proposed evaluation metric features benefits such as permutation invariance and more accurate program equivalence identification. |
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation (2023.acl-long)
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| Challenge: | Existing approaches to augment self-training (ST) in attribute-controllable language generation are limited and limited. |
| Approach: | They propose a new ST framework that integrates self-generated pseudo text into attribute-controllable language generation. |
| Outcome: | The proposed framework can be applied to semi-supervised controllable language generation. |
Co-VQA : Answering by Interactive Sub Question Sequence (2022.findings-acl)
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| Challenge: | Existing approaches to Visual Question Answering (VQA) answer questions directly, but people usually decompose a complex question into a sequence of simple sub questions. |
| Approach: | They propose a conversation-based VQA framework that decomposes questions into sub questions and answers them one-by-one. |
| Outcome: | The proposed framework achieves state-of-the-art on VQA 2.0 and VQA-CP v2 datasets. |
DuRE: Dual Contrastive Self Training for Semi-Supervised Relation Extraction (2024.naacl-long)
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| Challenge: | Existing document-level relation extraction methods require manual training and labeled data to obtain supervised learning. |
| Approach: | They propose a document-level relation extraction framework that integrates RE and text generation as a dual process. |
| Outcome: | The proposed framework significantly boosts recall and F1 score with comparable precision on two document-level RE tasks against several strong baselines. |
Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning (2023.acl-long)
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| Challenge: | Existing research on information extraction tasks focuses on one specific task, but in real-world scenarios, new data of different IE tasks and domains come in a stream over time. |
| Approach: | They propose a parameter- and deployment-efficient prompt tuning method to evaluate the UIE system under a “lifelong learning” setting. |
| Outcome: | The proposed method is able to learn new tasks without forgetting old ones and expand knowledge and functionalities without retraining the whole system. |
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning (2025.coling-main)
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Raymond Li, Yuxi Feng, Zhenan Fan, Giuseppe Carenini, Weiwei Zhang, Mohammadreza Pourreza, Yong Zhang
| Challenge: | In-context Learning (ICL) has proven to be effective in a variety of complex tasks, but the selection of the most beneficial demonstration examples remains an open research problem. |
| Approach: | They propose a demonstration retrieval framework that learns a weighted combination of LLM hidden states where rich semantic information is encoded. |
| Outcome: | Experiments on two popular NL2SQL benchmarks show that the proposed method outperforms state-of-the-art models. |
Semantic Graphs for Generating Deep Questions (2020.acl-main)
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| Challenge: | Existing research has focused on generating factoid questions relevant to one fact obtainable from a single sentence. |
| Approach: | They propose a framework that first constructs a semantic-level graph and then encodes it by introducing an attention-based GGNN. |
| Outcome: | The proposed framework captures the global structure of the document and facilitates reasoning over multiple facts. |