Papers by Junwei Liang
Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)
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Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu
| Challenge: | Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space . |
| Approach: | They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space . |
| Outcome: | The proposed approach improves on existing methods in the latent space of text. |
QSpell 250K: A Large-Scale, Practical Dataset for Chinese Search Query Spell Correction (2025.naacl-industry)
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| Challenge: | Chinese Search Query Spell Correction is a task designed to identify and correct typographical errors within queries. |
| Approach: | They propose a large-scale benchmark specifically developed for Chinese Query Spell Correction. |
| Outcome: | The proposed benchmark covers a broad range of topics, including formal entities, everyday colloquialisms and idiomatic expressions. |
FinTextQA: A Dataset for Long-form Financial Question Answering (2024.acl-long)
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| Challenge: | Existing financial question answering datasets lack scope diversity and question complexity. |
| Approach: | They propose to use a dataset for long-form question answering in finance to evaluate QA systems. |
| Outcome: | The proposed dataset includes 1,262 high-quality, source-attributed QA pairs extracted and selected from finance textbooks and government agency websites. |
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)
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Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu
| Challenge: | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss. |
| Approach: | They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence. |
| Outcome: | The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks. |
Best Practices for Distilling Large Language Models into BERT for Web Search Ranking (2025.coling-industry)
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| Challenge: | Recent studies have highlighted the potential of Large Language Models (LLMs) as zero-shot relevance rankers. |
| Approach: | They propose to use a ranking loss to transfer ranking knowledge from LLMs to smaller models like BERT. |
| Outcome: | The proposed model has been successfully integrated into a commercial web search engine as of February 2024. |
An Examination of the Compositionality of Large Generative Vision-Language Models (2024.naacl-long)
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| Challenge: | Recent studies have focused on the compositionality of vision-language models (VLMs) however, the performance of GVLMs in multimodal compositional reasoning remains under-explored. |
| Approach: | They propose a syntactical bias score to quantify GVLMs' syntaktical bias . they propose 'SADE' task to assess GVLs's robustness against inclination toward syntical correctness. |
| Outcome: | The proposed benchmarks are based on evaluation metrics and current benchmarks. |
OPERA: Operation-Pivoted Discrete Reasoning over Text (2022.naacl-main)
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Yongwei Zhou, Junwei Bao, Chaoqun Duan, Haipeng Sun, Jiahui Liang, Yifan Wang, Jing Zhao, Youzheng Wu, Xiaodong He, Tiejun Zhao
| Challenge: | Existing methods to predict logical forms ignore the utilization of symbolic operations and lack reasoning ability and interpretability. |
| Approach: | They propose an operation-pivoted discrete reasoning framework that uses symbolic operations as neural modules to facilitate reasoning ability and interpretability. |
| Outcome: | Extensive experiments on DROP and RACENum datasets show the reasoning ability of OPERA. |
Self-supervised Product Title Rewrite for Product Listing Ads (2022.naacl-industry)
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| Challenge: | Existing work has investigated the title optimization for Product Listing Ads (PLAs) however, little work has examined the effectiveness of this method. |
| Approach: | They propose a method to rewrite product listing ads titles without considering the fluency and information priority. |
| Outcome: | The proposed solution reduces the cost and improves CTR in the offline test and real-world online test by a large amount. |