Subspace Chronicles: How Linguistic Information Emerges, Shifts and Interacts during Language Model Training (2023.findings-emnlp)
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| Challenge: | Contemporary advances in NLP are built on the representational power of latent embedding spaces learned by self-supervised language models (LMs). |
| Approach: | They use a new information theoretic probing suite to analyze representational subspaces in language models. |
| Outcome: | The proposed approach compared performance of nine tasks across 2M pre-training steps and five seeds. |
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| Challenge: | Probing studies have explored where in neural language models linguistic information is located . standard approach is to focus on the layers whose representations give the highest performance on probing tasks . |
| Approach: | They propose a method that asks where task-relevant information emerges in the model by focusing on the layers that give the highest performance. |
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Feature Interactions Reveal Linguistic Structure in Language Models (2023.findings-acl)
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| Challenge: | Existing features attribution methods for post-hoc interpretability ignore the existence of interactions between the effects of features on the prediction. |
| Approach: | They propose a grey box method to train models to perfection on a formal language classification task using PCFGs. |
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Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
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| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
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Understanding Learning Dynamics Of Language Models with SVCCA (N19-1)
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| Challenge: | a new study shows that neural models implicitly encode linguistic features . but no research shows how these encodings arise as the models are trained . |
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Probing Across Time: What Does RoBERTa Know and When? (2021.findings-emnlp)
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| Challenge: | Current approaches to natural language processing rely on fixed artifacts such as language models . current studies have focused on how these models acquire and demonstrate knowledge . |
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Interpretability of Language Models via Task Spaces (2024.acl-long)
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| Challenge: | linguistic interpretability is a method used to assess language models' ability to interpret outputs. |
| Approach: | They propose a method to assess LMs' language conceptualisations by 'similarity probing' and a technique to fine tune them via gradient differentials to disentangle the learning signals of linguistic phenomena. |
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Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
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The Grammar-Learning Trajectories of Neural Language Models (2022.acl-long)
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| Challenge: | In this paper, we show that neural language models with different initialization, architecture, and training data acquire linguistic phenomena in a similar order, despite their different end performance. |
| Approach: | They propose to use mutual inductive bias to study linguistic representations implicit in NLMs. |
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Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining (2026.acl-long)
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| Challenge: | Large language models learn non-trivial abstractions during pretraining, but it is not well understood when and how these specific linguistic abilities emerge. |
| Approach: | They propose a method to track the evolution of linguistic features during pretraining by using sparse crosscoders to discover and align features across model checkpoints. |
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How Do Language Models Acquire Character-Level Information? (2026.eacl-long)
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| Challenge: | Language models (LMs) implicitly encode character-level information, despite not being explicitly provided during training. |
| Approach: | They analyze how language models acquire character-level knowledge by comparing them to standard settings. |
| Outcome: | The results show that LMs do not treat words as opaque tokens, but instead treat them as tokens. |