Exploring the Learning Capabilities of Language Models using LEVERWORLDS (2024.emnlp-main)
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| Challenge: | Existing models of stochastic learning involve learning general structure rules and specific properties of the instance. |
| Approach: | They propose a framework that allows the generation of physics-inspired worlds that follow a similar generative process with different distributions and their instances can be expressed in natural language. |
| Outcome: | The proposed framework allows the generation of physics-inspired worlds that follow a similar generative process with different distributions and their instances can be expressed in natural language. |
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| Challenge: | Existing work has tested transformers' ability to represent formal languages, but language models are not classifiers of strings but rather distributions over them. |
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| Challenge: | Multi-task learning (MTL) for generalist models is a promising direction that offers transfer learning potential. |
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In-Context Learning Creates Task Vectors (2023.findings-emnlp)
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Not all quantifiers are equal: Probing Transformer-based language models’ understanding of generalised quantifiers (2023.emnlp-main)
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| Challenge: | Recent popularity of generalised quantifiers and role in linguistics and logic raises the question of how they affect transformer-based language models (TLMs) |
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| Challenge: | Pretrained language models demonstrate strong performance in most NLP tasks when fine-tuned on small task-specific datasets. |
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On the Ability and Limitations of Transformers to Recognize Formal Languages (2020.emnlp-main)
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Modular and Parameter-Efficient Fine-Tuning for NLP Models (2022.emnlp-tutorials)
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Adaptive Transformers for Learning Multimodal Representations (2020.acl-srw)
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| Challenge: | Existing approaches for learning visiolinguistic representations with transformers are over-parametrized and require extensive training. |
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Efficient Integration of External Knowledge to LLM-based World Models via Retrieval-Augmented Generation and Reinforcement Learning (2025.findings-emnlp)
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| Challenge: | Existing attempts to enhance LLM-based world models through prompting or fine-tuning approaches are either requiring human knowledge or computationally extensive. |
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