Papers by Shaoliang Nie
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-text Rationales (2023.acl-long)
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Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, Xiang Ren
| Challenge: | Existing metrics like task performance of the LM generating the rationales or similarity between generated and gold rationale are not good indicators of their human utility. |
| Approach: | They propose to use a large language model to generate rationales with better human utility by estimating its conciseness and novelty. |
| Outcome: | The proposed model can measure human utility to a better extent by estimating its usefulness in answering similar unseen instances. |
MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)
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Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu
| Challenge: | Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout. |
| Approach: | They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction. |
| Outcome: | The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)
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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu
| Challenge: | Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement. |
| Approach: | They propose a method that involves tuning a small set of soft prompts for pre-trained language models. |
| Outcome: | The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark. |
MSD: Saliency-aware Knowledge Distillation for Multimodal Understanding (2021.findings-emnlp)
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| Challenge: | Current knowledge distillation models are limited and lack performance on multimodal datasets. |
| Approach: | They propose a multimodal knowledge distillation framework to transfer knowledge from a teacher on multimodal tasks by learning the teacher's behavior within each modality. |
| Outcome: | The proposed framework achieves better performance than KD on four multimodal datasets. |
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation (2023.emnlp-main)
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Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang, Shaoliang Nie
| Challenge: | Personalized text generation (PTG) is a key component of our digital lives but can inadvertently associate different levels of linguistic quality with users’ protected attributes. |
| Approach: | They propose a framework to achieve measure-specific counterfactual fairness in explanation generation by focusing on one of the most studied settings: generating natural language explanations for recommendations. |
| Outcome: | The proposed framework achieves measure-specific counterfactual fairness in explanation generation. |
Detection, Disambiguation, Re-ranking: Autoregressive Entity Linking as a Multi-Task Problem (2022.findings-acl)
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| Challenge: | Existing methods for entity linking do not use a knowledge base or candidate sets. |
| Approach: | They propose an autoregressive entity linking model that is trained with two auxiliary tasks and learns to re-rank generated samples at inference time. |
| Outcome: | The proposed model improves on two biomedical datasets and a news domain dataset without the use of a knowledge base or candidate sets. |
ER-Test: Evaluating Explanation Regularization Methods for Language Models (2022.findings-emnlp)
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| Challenge: | Explanation regularization (ER) aims to improve NLM generalization by pushing the NLM’s machine rationales to align with human rationale. |
| Approach: | They propose a framework for evaluating ER models’ OOD generalization along three dimensions: unseen datasets, contrast set tests, and functional tests. |
| Outcome: | The proposed framework evaluates ER models’ OOD generalization across unseen datasets, contrast set tests, and functional tests. |