Papers by Xiangjue Dong
Quantifying the Impact of Disfluency on Spoken Content Summarization (2024.lrec-main)
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| Challenge: | a recent study has found that disfluencies negatively impact spoken content summarization . |
| Approach: | They aim to quantify the impact of disfluency on spoken content summarization . they also investigate two methods towards improving summarizing in the presence of disflouencies . |
| Outcome: | The proposed methods improve summarization quality in the presence of disfluencies. |
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning (2025.findings-naacl)
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| Challenge: | Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while simultaneously providing human-intelligible explanations. |
| Approach: | They propose a reasoning-based recommendation framework that leverages Large Language Models to model users and items, focusing on preferences, aversions, and explanatory reasoning. |
| Outcome: | The proposed framework surpasses state-of-the-art methods by up to 12.5% in recommendation prediction while providing human-intelligible explanations. |
DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management (2025.findings-emnlp)
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Kai Yin, Xiangjue Dong, Chengkai Liu, Lipai Huang, Yiming Xiao, Zhewei Liu, Ali Mostafavi, James Caverlee
| Challenge: | Existing information retrieval benchmarks focus on general or specialized domains, such as medicine or finance, neglecting the unique linguistic complexity and diverse information needs encountered in disaster management scenarios. |
| Approach: | DisastIR is the first comprehensive IR evaluation benchmark specifically tailored for disaster management. |
| Outcome: | DisastIR covers 48 retrieval tasks derived from six search intents and eight general disaster categories . evaluations show no single model excelling universally . |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
DisastQA: A Comprehensive Benchmark for Evaluating Question Answering in Disaster Management (2026.findings-acl)
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Zhitong Chen, Kai Yin, Xiangjue Dong, Chengkai Liu, Xiangpeng Li, Bo Li, Junwei Ma, Yiming Xiao, Ali Mostafavi, James Caverlee
| Challenge: | Existing benchmarks for question answering (QA) are lacking in a high-stakes environment. |
| Approach: | They propose a rigorously verified benchmark of 3,000 expert-annotated questions . they propose 'keypoint-based evaluation protocol' emphasizing factual completeness over verbosity . |
| Outcome: | Experiments with 20 models reveal substantial divergences from general-purpose models such as MMLU-Pro. |
DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management (2026.acl-long)
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| Challenge: | Existing models fail to handle the varied search intents inherent to disaster management scenarios, resulting in inconsistent and unreliable performance. |
| Approach: | They propose a new series of dense retrieval models tailored for disaster management that train on a three-stage framework with unsupervised contrastive pre-training and difficulty-aware progressive instruction fine-tuning. |
| Outcome: | The proposed model outperforms baseline models by 13.3 times and 33 times over baselines with only 7.6% of their parameters. |
PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts (2023.findings-acl)
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| Challenge: | Toward building more robust and reliable conversational systems, we introduce a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models. |
| Approach: | They propose a prompt-based learning approach to automatically generate effective adversarial examples to probe DST models. |
| Outcome: | The proposed framework leads to the greatest reduction in accuracy and the best attack success rate while maintaining good fluency and a low perturbation ratio. |
DACL: Disfluency Augmented Curriculum Learning for Fluent Text Generation (2024.lrec-main)
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| Challenge: | Disfluency-aware language models are traditionally trained on fluent, written text corpora. |
| Approach: | They propose a Disfluency Augmented Curriculum Learning approach to tackle disfluency . they use CL coupled with synthetically augmented disfluent texts of various levels . |
| Outcome: | The proposed model surpasses existing techniques in word-based precision (by up to 1%) and has shown favorable recall and F1 scores. |
DA3: A Distribution-Aware Adversarial Attack against Language Models (2024.emnlp-main)
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| Challenge: | Recent attacks have shown that adversarial examples have a different data distribution than the original examples, reducing their effectiveness under detection methods. |
| Approach: | They propose a distribution-aware adversarial attack method that considers the distribution shifts of adversarials to improve attacks’ effectiveness under detection methods. |
| Outcome: | The proposed method improves the effectiveness of adversarial examples under detection methods and integrates both ASR and detectability. |
Co2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Pre-trained language models can encode unfair social biases from large pre-training corpora and even amplify biase in downstream applications. |
| Approach: | They propose a *debias-while-prompt tuning* method for mitigating biases via counterfactual contrastive prompt tuning on downstream tasks. |
| Outcome: | The proposed method can mitigate biases on three extrinsic bias benchmarks and adapt to existing debiased language models. |
Closed-book Question Generation via Contrastive Learning (2023.eacl-main)
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| Challenge: | Recent studies on open-book QG have achieved promising progress, but generating natural questions under a more practical closed-book setting remains a challenge. |
| Approach: | They propose a QG model that stores more information in its parameters through contrastive learning and an answer reconstruction module. |
| Outcome: | The proposed model outperforms baselines in automatic evaluation and human evaluation on a public dataset and a new WikiCQA dataset. |
CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning (2026.acl-long)
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| Challenge: | Persona-assigned Large Language Models can be useful for personalized, context-aware reasoning. |
| Approach: | They propose a framework that harmonizes demographic perturbations into a unified prediction by balancing agreement and divergence among counterfactual personas. |
| Outcome: | The proposed framework improves reasoning performance even when base personas are suboptimal. |