Papers by Xilun Chen
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2022.findings-naacl)
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| Challenge: | Existing approaches to answer open domain questions rely on unlabeled text or synthetically generated question-answer pairs. |
| Approach: | They propose a large-scale open-domain question-answering dataset based on the Common Crawl project that can be used to in-domain pre-train popular language models. |
| Outcome: | The proposed dataset achieves promising results in zero-shot, low resource and fine-tuned settings across multiple tasks, models and benchmarks. |
Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)
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| Challenge: | Existing methods for text classification are domain-dependent, but there is no annotated data for some domains. |
| Approach: | They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task . |
| Outcome: | The proposed model outperforms the prior art on the multi-domain text classification task. |
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing (2020.emnlp-main)
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| Challenge: | Recent advances in deep learning have enabled several approaches to successfully parse more complex queries, but these models require a large amount of annotated training data to parser on new domains (e.g. reminder, music). |
| Approach: | They propose a method that adapts task-oriented semantic parsers to low-resource domains and outperforms a supervised neural model at a 10-fold data reduction. |
| Outcome: | The proposed method outperforms baseline methods on a newly collected multi-domain task-oriented semantic parsing dataset (TOPv2) . |
Muppet: Massive Multi-task Representations with Pre-Finetuning (2021.emnlp-main)
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| Challenge: | Recent work shows gains from pre-training and fine-tuning that are multi-task . but it can be difficult to know which intermediate tasks will best transfer . |
| Approach: | They propose a large-scale learning stage for pre-finetuning between pre-training and fine-tun. |
| Outcome: | The proposed model improves performance on pretrained discriminators and generation models on a wide range of tasks while improving sample efficiency during fine-tuning. |
Unsupervised Multilingual Word Embeddings (D18-1)
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| Challenge: | Prior art for learning UMWEs relies on a number of independently trained UBWEs to obtain multilingual embeddings. |
| Approach: | They propose a fully unsupervised framework that exploits the relations between all language pairs to learn multilingual embeddings without cross-lingual supervision. |
| Outcome: | The proposed framework outperforms supervised approaches on multilingual word translation and cross-lingual word similarity and beats a number of other approaches trained with cross-linguistic resources. |
Nonparametric Masked Language Modeling (2023.findings-acl)
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| Challenge: | Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. |
| Approach: | They introduce a nonparametric masked language model that replaces a softmax with a distribution over every phrase in a reference corpus and uses an in-batch approximation to train it. |
| Outcome: | The proposed model outperforms larger parametric models on 16 tasks including classification, fact probing and question answering. |
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval (2023.acl-long)
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Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing multi-vector retrieval methods are slower and require more space to store indices compared to their single-vektor counterparts. |
| Approach: | They propose a multi-vector retrieval method that uses dynamic lexical routing to route different token vectors to the predicted lexicals. |
| Outcome: | The proposed method achieves state-of-the-art performance on several benchmark datasets while being nearly 40 times faster than the current state-out-of the-art method. |
Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026.eacl-long)
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Kai Sun, Yin Huang, Srishti Mehra, Mohammad Kachuee, Xilun Chen, Renjie Tao, Zhaojiang Lin, Andrea Jessee, Nirav Shah, Alex L Betty, Yue Liu, Anuj Kumar, Wen-tau Yih, Xin Luna Dong
| Challenge: | Existing literature on knowledge extraction for question answering questions whether it is still relevant for question answerrs. |
| Approach: | They extend an existing benchmark with knowledge extraction annotations and evaluate commercial and open-source LLMs of varying sizes. |
| Outcome: | The proposed model can achieve high QA accuracy, but can still benefit from knowledge extraction through augmentation with extracted triples and multi-task learning. |
Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering (2024.eacl-long)
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| Challenge: | Recent approaches to multi-hop question answering rely on in-context learning . however, these models contain billions of parameters making them inefficient at inference time. |
| Approach: | They propose a framework that allows for improving smaller language models with less than 10 human-annotated QA pairs by synthesizing millions of multi-hop questions and claims to fine tune language models. |
| Outcome: | The proposed framework improves model performance on multi-hop question answering and fact verification benchmarks while being almost one-third the size in parameter count. |
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One? (2022.findings-emnlp)
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Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih
| Challenge: | Existing sparse retrievers lack the ability to match salient phrases and rare entities in the query. |
| Approach: | They introduce a dense Lexical Model that can be trained to imitate a sparse one. |
| Outcome: | The proposed model outperforms sparse retrievers on a range of tasks including five question answering datasets and the MS MARCO passage retrieval. |
Extracting and Understanding the Superficial Knowledge in Alignment (2025.naacl-long)
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Runjin Chen, Gabriel Jacob Perin, Xuxi Chen, Xilun Chen, Yan Han, Nina S. T. Hirata, Junyuan Hong, Bhavya Kailkhura
| Challenge: | Recent studies have shown that alignment of large language models with human values and preferences requires substantial data and computation resources. |
| Approach: | They propose a method to extract and isolate superficial knowledge from aligned models by focusing on the shallow modifications to the final token selection process. |
| Outcome: | The proposed method extracts and isolates superficial knowledge from aligned models, focusing on the shallow modifications to the final token selection process. |
Task-aware Retrieval with Instructions (2023.findings-acl)
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Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih
| Challenge: | Existing models that learn intents from labeled data are complicated and require a vast number of annotated examples to train a model. |
| Approach: | They propose a general-purpose task-aware retrieval system with instructions that can adapt to a new task without any parameter updates. |
| Outcome: | The proposed system outperforms two benchmarks on a set of domains and tasks on X2-Retrieval. |
A Study on the Efficiency and Generalization of Light Hybrid Retrievers (2023.acl-short)
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Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari
| Challenge: | Recent research focuses on building neural retrievers which learn dense embeddings of query and document into a semantic space. |
| Approach: | They propose to use an indexing-efficient dense retriever to reduce hybrid retrievers' memory by using the state-based indexing algorithm. |
| Outcome: | The proposed hybrid retriever saves 13 memory while maintaining 98.0% performance on out-of-domain datasets and adversarial attacks datasets. |
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2022.findings-naacl)
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Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, Scott Yih
| Challenge: | a recent study aims to answer factual questions using a structured knowledge base (KBQA). |
| Approach: | They propose a unifying approach that homogenizes all knowledge sources by reducing them to text . they demonstrate that UniK-QA is a simple and yet effective way to combine heterogeneous sources of knowledge. |
| Outcome: | The proposed approach improves state-of-the-art results on knowledge-base QA tasks by 11 points compared to graph-based methods. |
Simple Local Attentions Remain Competitive for Long-Context Tasks (2022.naacl-main)
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Wenhan Xiong, Barlas Oguz, Anchit Gupta, Xilun Chen, Diana Liskovich, Omer Levy, Scott Yih, Yashar Mehdad
| Challenge: | Existing models for NLP tasks require long text sequences beyond the length limit of pretrained models. |
| Approach: | They propose to pretrain large-size NLP models using the same long-doc corpus and fine tune them for real-world long-context tasks. |
| Outcome: | The proposed models can perform better under standard pretraining paradigms than longformer and Longformer. |
Domain-matched Pre-training Tasks for Dense Retrieval (2022.findings-naacl)
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Barlas Oguz, Kushal Lakhotia, Anchit Gupta, Patrick Lewis, Vladimir Karpukhin, Aleksandra Piktus, Xilun Chen, Sebastian Riedel, Scott Yih, Sonal Gupta, Yashar Mehdad
| Challenge: | Existing approaches to improve performance of pre-training tasks are needed. |
| Approach: | They propose to pre-train large bi-encoder models on a recently released set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting reddit conversation dataset. |
| Outcome: | The proposed model can be pre-trained on a set of 65 millionsynthetically generated questions and 200 million post-comment pairs from a preexisting dataset of Reddit conversations. |
DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers (2025.acl-long)
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| Challenge: | Large language models (LLMs) have shown strong effectiveness and robustness when fine-tuned as dense retrievers. |
| Approach: | They propose a training framework that leverages pruned LLMs to train smaller generalizable dense retrievers. |
| Outcome: | The proposed training framework offers better multilingual and long-context capabilities than traditional encoder-based retrievers and achieves strong performance across multiple tasks and languages. |
How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval (2023.findings-emnlp)
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Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen
| Challenge: | Existing techniques to improve dense retrieval suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, some argue due to the limited model capacity. |
| Approach: | They propose to use diverse queries and sources of supervision to train a generalizable DR to achieve high accuracy in both supervised and zero-shot retrieval. |
| Outcome: | The proposed DR can achieve state-of-the-art in supervised and zero-shot evaluations without increasing model size. |