Papers with information fusion
PibE-MPP: A Play-it-by-Ear Masking Performance Plug-in for LLMs (2026.findings-acl)
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Mengwei Wang, Simin Niu, Xun Liang, Yuefeng Ma, Sensen Zhang, Jiawei Yang, Shichao Song, Hanyu Wang, Huayi Lai
| Challenge: | Random masking is a widely adopted classic baseline in large language models (LLMs). |
| Approach: | They propose a play-it-by-ear masking performance plug-in which enables LLMs to adaptively select masking target combinations for each task. |
| Outcome: | The proposed performance plug-in retains the advantages and mitigates the drawbacks of random masking in large language models. |
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension (P18-1)
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| Challenge: | Various advanced neural models have been proposed for reading comprehension, but most models ignore its relations with other answer candidates. |
| Approach: | They propose to model reading comprehension as an extract-then-select two-stage procedure . they first extract answer candidates from passages, then select the final answer by combining information from all candidates. |
| Outcome: | The proposed approach improves state-of-the-art performance on open-domain reading comprehension datasets. |
Attention as Grounding: Exploring Textual and Cross-Modal Attention on Entities and Relations in Language-and-Vision Transformer (2022.findings-acl)
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| Challenge: | Existing work has focused on what is captured by multi-modal architectures. |
| Approach: | They propose a multi-modal transformer that learns syntactic and semantic representations about entities and relations grounded in objects at the level of masked self-attention and cross-modal attention. |
| Outcome: | The proposed model learns syntactic and semantic representations about objects and relations cross-modally and unimodally. |