| Challenge: | PTLMs are used to extract knowledge from text on demand. |
| Approach: | They compare visual-linguistic and language-only visual-language models in a zero-shot commonsense question answering inference task. |
| Outcome: | The proposed models are highly promising on certain types of commonsense knowledge associated with the visual world. |
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What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge (2022.acl-srw)
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| Challenge: | Existing evaluation methods to measure what language models learn from multimodal training are lacking. |
| Approach: | They propose two evaluation tasks to measure commonsense knowledge in language models by using visual data to evaluate multimodal models and unimodal baselines. |
| Outcome: | The proposed evaluation tasks show that training on a visual modality improves on the visual commonsense knowledge in language models. |
Does Vision-and-Language Pretraining Improve Lexical Grounding? (2021.findings-emnlp)
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| Challenge: | Large pretrained language models (LMs) have been criticized for lack of grounding, i.e., connecting words to their meanings in the physical world. |
| Approach: | They compare vision-and-language (VL) models trained jointly on text and image or video data to find out how they compare to text-only counterparts. |
| Outcome: | The proposed model outperforms the text-only variants on a commonsense question answering task. |
Language Models as Knowledge Bases? (D19-1)
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Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander Miller
| Challenge: | Recent advances in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. |
| Approach: | They present a method for pretraining language models on large textual corpora . they find that they can store relational knowledge and answer queries structured as "fill-in-the-blank" queries. |
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Commonsense Knowledge Transfer for Pre-trained Language Models (2023.findings-acl)
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| Challenge: | Recent advances in pre-trained language models have transformed the landscape of natural language processing. |
| Approach: | They propose a framework to transfer commonsense knowledge stored in a neural commonsensing model to a general-purpose pre-trained language model. |
| Outcome: | Empirical results show that the proposed framework improves the model’s performance on downstream tasks that require commonsense reasoning. |
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks (2022.emnlp-main)
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| Challenge: | Recent work attempts to explicitly incorporate human-defined linguistic priors into fine-tuning tasks. |
| Approach: | They replace parsed graphs or trees with trivial ones to investigate linguistic priors . they propose to use trivial graphs as baselines to design advanced knowledge fusion methods . |
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A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)
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Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh
| Challenge: | Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. |
| Approach: | They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge. |
| Outcome: | The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance. |
Vision-Language Pretraining: Current Trends and the Future (2022.acl-tutorials)
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| Challenge: | Recent vision-language models are being used for downstream tasks that require large datasets and supervised datasets. |
| Approach: | They focus on recent vision-language pretraining paradigms and their strengths and shortcomings . they compare the different family of models used for vision- language pretraining . |
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ROME: Evaluating Pre-trained Vision-Language Models on Reasoning beyond Visual Common Sense (2023.findings-emnlp)
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| Challenge: | a vision-language model with commonsense knowledge can reason beyond common sense . however, pre-trained vision-linguistic models are incapable of interpreting counter-intuitive content . |
| Approach: | They introduce a probing dataset to evaluate vision-language models' reasoning abilities . they use images that defy commonsense knowledge to test their reasoning abilities. |
| Outcome: | The proposed dataset evaluates whether pre-trained vision-language models can reason beyond common sense . it contains images that defy commonsense knowledge with regards to color, shape, material, size and position . |
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer (2022.acl-long)
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| Challenge: | Pre-trained language models lack visual knowledge of common objects due to reporting bias. |
| Approach: | They investigate whether integrating visual knowledge into a language model can fill the gap . they use captions and images to transfer visual knowledge to 5 downstream tasks . |
| Outcome: | The proposed model can improve performance on 5 tasks that may need visual knowledge to solve the problem. |
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning (2024.emnlp-main)
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| Challenge: | Recent advances in language and vision assistants have showcased impressive capabilities but suffer from a lack of transparency, limiting broader research and reproducibility. |
| Approach: | They propose to redefine the design of vision-language models by identifying key components and creating efficient models with constrained inference costs. |
| Outcome: | The proposed models achieve significant improvements in inference throughput while maintaining high performance. |