Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, Yoav Goldberg
| Challenge: | In this paper, we examine whether pretrained language models are consistent with factual knowledge. |
| Approach: | They propose a method to improve consistency of pretrained language models . consistency is a desirable property of a good language understanding model, they argue . |
| Outcome: | The proposed model improves consistency and shows that it is effective. |
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Factual Consistency of Multilingual Pretrained Language Models (2022.findings-acl)
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| Challenge: | Recent work shows that monolingual English language models fill-in-the-blank differently for paraphrases describing the same fact. |
| Approach: | They propose a resource to analyze consistency of English language models . they find that mBERT is as inconsistent as English BERT in paraphrases . |
| Outcome: | The proposed model is as inconsistent as English BERT in English paraphrases, but it is more so for all the other 45 languages. |
Calibrating Factual Knowledge in Pretrained Language Models (2022.findings-emnlp)
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| Challenge: | Existing studies show that Pretrained Language Models can store factual knowledge, but facts stored in PLMs are not always correct. |
| Approach: | They propose a lightweight method to calibrate factual knowledge in PLMs without re-training from scratch. |
| Outcome: | The proposed method can be used to calibrate factual knowledge in PLMs without re-training from scratch. |
Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models (2023.emnlp-main)
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| Challenge: | Multilingual large-scale pretrained language models store factual knowledge, but large variations are observed across languages. |
| Approach: | They propose a ranking-based consistency metric to evaluate cross-lingual consistency of factual knowledge in multilingual PLMs. |
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How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis (2022.findings-acl)
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Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu
| Challenge: | Recent studies show that pre-trained language models can fill in the missing factual words in cloze-style prompts such as ”Dante was born in [MASK]” . |
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Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models (2023.eacl-main)
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Peter Hase, Mona Diab, Asli Celikyilmaz, Xian Li, Zornitsa Kozareva, Veselin Stoyanov, Mohit Bansal, Srinivasan Iyer
| Challenge: | Pretrained language models store a large amount of factual information that can be elicited by prompting or finetuning. |
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| Outcome: | The proposed methods improve models' consistency and accuracy, the authors show . their methods outperform existing methods in more difficult settings, the paper shows . |
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models (2020.emnlp-main)
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| Challenge: | Language models (LMs) capture factual knowledge by filling in the blanks of cloze-style prompts. |
| Approach: | They propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge and verify its effectiveness on several benchmark languages. |
| Outcome: | The proposed method improves the ability of multilingual LMs to access knowledge and verify its effectiveness on several benchmark languages. |
Can Pretrained Language Models (Yet) Reason Deductively? (2023.eacl-main)
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| Challenge: | Acquiring factual knowledge with Pretrained Language Models (PLMs) has attracted increasing attention, showing promising performance in many knowledge-intensive tasks. |
| Approach: | They conduct a comprehensive evaluation of the learnable deductive reasoning capability of pretrained language models and compare their performance against simple adversarial surface form edits. |
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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. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |
Are Knowledge and Reference in Multilingual Language Models Cross-Lingually Consistent? (2025.findings-emnlp)
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| Challenge: | Cross-lingual consistency should be considered to assess cross-lingual transferability, maintain factuality of model knowledge across languages, and preserve parity of language model performance. |
| Approach: | They examine pretrained and tuned models with code-mixed coreferential statements that convey identical knowledge across languages. |
| Outcome: | The proposed model shows different levels of consistency in multilingual models, subject to language families, linguistic factors, scripts, and bottlenecks on a particular layer. |