Papers with overconfidence issue
A Close Look into the Calibration of Pre-trained Language Models (2023.acl-long)
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| Challenge: | Pre-trained language models (PLMs) may fail in giving reliable estimates of their predictive uncertainty. |
| Approach: | They conduct fine-grained control experiments to study the dynamic change in PLMs’ calibration performance in training. |
| Outcome: | The proposed methods significantly reduce PLMs’ confidence in wrong predictions. |
RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs (2025.emnlp-main)
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| Challenge: | Existing language models that address ambiguity are limited in their ability to address it . ambiguities are an inherent feature of human language, according to research . |
| Approach: | They propose a dataset targeting referential ambiguity in image-based question answering . they find that current language models lack robust strategies to deal with ambiguities . |
| Outcome: | The proposed dataset shows that state-of-the-art models fail to address ambiguity . ambiguities are an inherent feature of human language, according to research . |