Papers by Xiangci Li
Contextualizing Generated Citation Texts (2024.lrec-main)
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| Challenge: | Abstractive citation text generation is usually framed as an infilling task . however, examining a recent LED-based citation generation system, we find that many of the generated citations are generic summaries of the reference paper’s main contribution, ignoring the citation context’s focus on a different topic. |
| Approach: | They propose a modification to the citation text generation task by training the generation model to generate a citation given a reference paper and the context window around the target. |
| Outcome: | The proposed model can generate citations based on the entire context window, including the target citation. |
CodeScout: Contextual Problem Statement Enhancement for Software Agents (2026.findings-acl)
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Manan Suri, Xiangci Li, Mehdi Shojaie, Songyang Han, Chao-Chun Hsu, Shweta Garg, Aniket Anand Deshmukh, Varun Kumar
| Challenge: | Current AI-powered code assistance tools struggle with ambiguous problem statements . failures on such ambiguously requests are highly correlated with longer trajectories . |
| Approach: | They propose a contextual query refinement approach that transforms ambiguous user requests into comprehensive, actionable problem statements through lightweight pre-exploration of the target codebase. |
| Outcome: | Empirical results show that CodeScout improves resolution rates with 27 additional issues resolved compared to baseline method. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
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| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
Context-aware Stand-alone Neural Spelling Correction (2020.findings-emnlp)
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| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |
Scientific Discourse Tagging for Evidence Extraction (2021.eacl-main)
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| Challenge: | Primary experimental articles provide the crucial raw material for all subsequent scientific research, but the growing number of scientific literature makes it difficult for domain experts to efficiently utilize them. |
| Approach: | They propose to automatically extract text fragments from primary research papers that describe the evidence presented in that paper's figures and to use them to build models of scientific argument. |
| Outcome: | The proposed method is able to extract text fragments from primary research papers that describe the evidence presented in that paper's figures, and it is transferable to new datasets. |
Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching (2025.acl-long)
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Xiangci Li, Zhiyu Chen, Jason Ingyu Choi, Nikhita Vedula, Besnik Fetahu, Oleg Rokhlenko, Shervin Malmasi
| Challenge: | Prior human-annotated CPS datasets are small in size and lack integration with real-world product search systems. |
| Approach: | They propose a method to generate target-oriented shopping conversations without human annotations by using large language models. |
| Outcome: | The proposed method achieves highly natural and coherent conversations from three shopping domains and significantly improves on human evaluations and downstream tasks. |
How Does Knowledge Selection Help Retrieval Augmented Generation? (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a powerful method for enhancing natural language generation by integrating external knowledge into a model’s output. |
| Approach: | They empirically analyze how knowledge selection influences downstream generation performance in RAG systems by simulating different retrieval and selection conditions through a controlled mixture of gold and distractor knowledge. |
| Outcome: | The proposed model is based on a controlled mixture of gold and distractor knowledge and simulated with a gold and distractors. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
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| Challenge: | Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field . |
| Approach: | They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works . |
| Outcome: | a new study summarises key approaches and defines the tasks and discusses the challenges of RWG. |
CORWA: A Citation-Oriented Related Work Annotation Dataset (2022.naacl-main)
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| Challenge: | Academic research is an exploratory activity to discover new solutions to problems . prior work focused on the sentence as the basic unit of generation, neglecting that related work sections consist of variable length text fragments derived from different information sources. |
| Approach: | They propose a Citation Oriented Related Work Annotation dataset that labels citation text fragments . they propose linguistically-motivated framework for human-in-the-loop, abstractive related work generation . |
| Outcome: | The proposed framework is based on a Citation Oriented Related Work Annotation dataset . it automatically tags unlabeled related work sections on the dataset based upon the proposed model . |
Explaining Relationships Among Research Papers (2025.coling-main)
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| Challenge: | Existing literature reviews focus on summarizing individual papers without addressing the need for expository and transition sentences to explain the relationships among multiple papers. |
| Approach: | They propose a feature-based, LLM-prompting approach to generate richer citation texts . they propose to use related work sections of scientific articles as proxy for the kind of short, customized, daily feed summaries . |
| Outcome: | The proposed approach captures complex relationships among multiple papers while generating richer citation texts. |