Papers by Wanqing Cui
LINKAGE: Listwise Ranking among Varied-Quality References for Non-Factoid QA Evaluation via LLMs (2024.findings-emnlp)
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| Challenge: | Non-factoid (NF) question answering is challenging to evaluate due to diverse potential answers and no objective criterion. |
| Approach: | They propose a listwise NFQA evaluation approach that uses Large Language Models to rank candidate answers in a descending list of reference answers sorted by descending quality. |
| Outcome: | The proposed method has higher correlations with human annotations than standard methods. |
Beyond Language: Learning Commonsense from Images for Reasoning (2020.findings-emnlp)
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| Challenge: | Existing commonsense reasoning methods use raw texts to perform data representation and answer prediction tasks. |
| Approach: | They propose a novel approach to learn commonsense from images instead of limited raw texts or costly knowledge bases. |
| Outcome: | The proposed approach outperforms language-based methods on commonsense reasoning problems on two commonsence reasoning problems. |
MORE: Multi-mOdal REtrieval Augmented Generative Commonsense Reasoning (2024.findings-acl)
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| Challenge: | Language Models (LLMs) have gained increasing prominence in artificial intelligence, especially Large Language Model (LLm) due to the well-recognized reporting bias, the recording of commonsense information is significantly less than its existence in reality. |
| Approach: | They propose a Multi-mOdal REtrieval framework to leverage both text and images to enhance commonsense ability of language models. |
| Outcome: | The proposed framework can leverage both text and images to enhance commonsense ability of language models. |