Papers by Wenlong Zhao
MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs (2026.acl-long)
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Xiangyu Zhao, Wanghan Xu, Bo Liu, Yuhao Zhou, Fenghua Ling, Ben Fei, Xiaoyu Yue, Lei Bai, Wenlong Zhang, Xiao-Ming Wu
| Challenge: | Existing datasets often rely on synthetic data or figure-caption pairs, failing to capture the depth and complexity of geoscientific reasoning. |
| Approach: | They propose a multimodal scientific dataset and benchmark curated from open-access publications. |
| Outcome: | MSEarth features over 289K figures with captions enriched by contextual discussions and reasoning from original papers. |
Test-Time Strategies for More Efficient and Accurate Agentic RAG (2026.acl-srw)
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Abhinav Sharma, Brian Zhang, Deepti Guntur, Zhiyang Zuo, Shreyas Chaudhari, Wenlong Zhao, Franck Dernoncourt, Puneet Mathur, Ryan A. Rossi, Nedim Lipka
| Challenge: | Retrieval-Augmented Generation (RAG) systems face challenges with complex, multi-hop questions. |
| Approach: | They propose to integrate contextualization module and de-duplication module to improve the accuracy of retrieved documents and to reduce the number of turns by 10.5%. |
| Outcome: | The proposed approach achieves a 5.6% increase in EM score and reduces the average number of turns by 10.5% compared to the baseline. |
WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models (2024.lrec-main)
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| Challenge: | a global dataset for multi-cultural value prediction task is lacking in the computer science community . a multi-culture awareness of LMs is critical to generating safe and personalized responses . |
| Approach: | They present a global multi-cultural value prediction task using a world value survey dataset . they construct more than 20 million examples of the type "(demographic attributes, value question) answer" they show that the task is challenging for strong open and closed-source models . |
| Outcome: | The proposed model can generate a rating response to a value question based on demographic contexts on 11.1%, 25.0%, 72.2%, and 75.0% of the questions. |
ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution (2023.findings-eacl)
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Ankita Gupta, Marzena Karpinska, Wenlong Zhao, Kalpesh Krishna, Jack Merullo, Luke Yeh, Mohit Iyyer, Brendan O’Connor
| Challenge: | Existing datasets vary in definition of coreferences and are curated for linguistic experts. |
| Approach: | They propose to use ezCoref to create a crowdsourcing-friendly coreference annotation methodology that teaches annotators only cases that are treated similarly across existing datasets. |
| Outcome: | The proposed method reannotates 240 passages from seven existing english coreference datasets while teaching annotators only cases that are treated similarly across them. |
UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generation (2024.emnlp-main)
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| Challenge: | e-commerce tasks such as multimodal retrieval and multimodal generation are largely ignored due to the diversity of the multimodal fashion domain. |
| Approach: | They propose a framework that integrates image generation with retrieval and text generation tasks. |
| Outcome: | The proposed framework outperforms state-of-the-art models across fashion tasks. |
ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction (2022.emnlp-main)
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| Challenge: | Existing CCE methods treat contracts as plain text, creating a barrier to understanding complex contracts. |
| Approach: | They propose a framework to model implicit relations in legal contracts to improve contract understanding . they propose Term-Definition Relation captures the relation between important terms and their definitions . |
| Outcome: | The proposed framework improves on two CCE tasks in conventional and zero-shot settings. |
Comparing Neighbors Together Makes it Easy: Jointly Comparing Multiple Candidates for Efficient and Effective Retrieval (2024.emnlp-main)
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| Challenge: | Experimental results show that using only bi-encoders as an intermediate reranker can improve top-1 accuracy with negligible slowdown (7%). |
| Approach: | They propose a framework that compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
| Outcome: | The proposed framework compares a query and multiple embeddings of similar candidates through shallow self-attention layers, delivering rich representations contextualized to each other. |
Machine Reading Comprehension using Case-based Reasoning (2023.findings-emnlp)
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Dung Thai, Dhruv Agarwal, Mudit Chaudhary, Wenlong Zhao, Rajarshi Das, Jay-Yoon Lee, Hannaneh Hajishirzi, Manzil Zaheer, Andrew McCallum
| Challenge: | Current state-of-the-art machine readers do not support case-based reasoning . |
| Approach: | They propose a method that extracts a set of similar cases from a nonparametric memory and then predicts an answer by selecting the span in the test context that is most similar to the contextualized representations of answers. |
| Outcome: | The proposed method outperforms baselines on NaturalQuestions and NewsQA by 11.5 and 8.4 EM. |
Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation (2024.acl-long)
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Jiachen Zhao, Wenlong Zhao, Andrew Drozdov, Benjamin Rozonoyer, Md Arafat Sultan, Jay-Yoon Lee, Mohit Iyyer, Andrew McCallum
| Challenge: | Low-resource tasks such as semi-supervised sequence generation require expert knowledge and cost. |
| Approach: | They propose a method for semi-supervised sequence generation where few examples are too scarce to fine tune a model. |
| Outcome: | The proposed method can generalize better than its teacher to unseen examples on semi-supervised sequence generation tasks. |
Compressing Transformer-Based Semantic Parsing Models using Compositional Code Embeddings (2020.findings-emnlp)
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Prafull Prakash, Saurabh Kumar Shashidhar, Wenlong Zhao, Subendhu Rongali, Haidar Khan, Michael Kayser
| Challenge: | Existing task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders. |
| Approach: | They propose to learn compositional code embeddings to greatly reduce the sizes of BERT and RoBERTa encoders. |
| Outcome: | The proposed model reduces the size of BERT and RoBERTa encoders while maintaining performance. |
IGA: An Intent-Guided Authoring Assistant (2021.emnlp-main)
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Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, Mohit Iyyer
| Challenge: | Pretrained language models have improved writing assistance functions such as autocomplete, but more complex and controllable writing assistants have yet to be explored. |
| Approach: | They build an intent-guided authoring assistant that follows fine-grained author directives by specifying different writing intents. |
| Outcome: | The proposed system generates output satisfying the author's intent and can be rephrased to their liking. |
Editing Common Sense in Transformers (2023.emnlp-main)
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Anshita Gupta, Debanjan Mondal, Akshay Sheshadri, Wenlong Zhao, Xiang Li, Sarah Wiegreffe, Niket Tandon
| Challenge: | Currently, the performance of transformer-based model editing methods is limited to statements about encyclopedic knowledge with a single correct answer. |
| Approach: | They propose to improve MEMIT's model editing algorithm by varying edit tokens and improving the layer selection strategy to improve commonsense knowledge. |
| Outcome: | The MEMIT editing algorithm outperforms baseline models on PEP3k and 20Q datasets while fine-tuning baselines shows significant trade-offs. |