| Challenge: | a popular technique for analyzing neural representations involves predicting information of interest from the activation patterns. |
| Approach: | They propose to use Representational Similarity Analysis and Tree Kernels to quantify how strongly activation patterns correspond to symbolic representations. |
| Outcome: | The proposed methods show that they exhibit the expected pattern of results on a synthetic language. |
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| Challenge: | a technique developed by neuroscientists compares activity patterns of different measurement modalities . a recent study examined the correspondence between popular pretrained language encoders and human processing difficulty . |
| Approach: | They employ a technique to compare activity patterns of different measurement modalities . they establish a correspondence between widely-employed pretrained language encoders and human processing difficulty . |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
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Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling (2022.naacl-main)
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| Challenge: | Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way. |
| Approach: | They examine whether syntactic and semantic graph representations can complement and improve neural language modeling. |
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Linking artificial and human neural representations of language (D19-1)
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| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
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Analyzing analytical methods: The case of phonology in neural models of spoken language (2020.acl-main)
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| Challenge: | Recent studies have focused on the strengths and weaknesses of various methods for analyzing phonology representations. |
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Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)
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| Challenge: | Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. |
| Approach: | This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods. |
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Mapping Brains with Language Models: A Survey (2023.findings-acl)
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| Challenge: | accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism. |
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Similarity Analysis of Contextual Word Representation Models (2020.acl-main)
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| Challenge: | Existing and novel similarity measures are used to analyze contextual word representations . different architectures have rather similar representations, but different individual neurons. |
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Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Contextualized word embeddings can incorporate contextual information, whereas other embeddables cannot. |
| Approach: | They propose an approach to address this question using Representational Similarity Analysis (RSA) they investigate whether verb embeddings encode verb’s subject, pronoun embedds antecedent and full-sentence representations encode sentence’s head word . |
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Computational Expressivity of Neural Language Models (2024.acl-tutorials)
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| Challenge: | Language models (LMs) are at the forefront of NLP research due to their versatility across diverse tasks. |
| Approach: | This tutorial will provide a framework for formal analysis of modern language models using tools from formal language theory. |
| Outcome: | This tutorial will provide a framework for formal analysis of modern language models using tools from formal language theory (FLT). |