| Challenge: | We examine the pre-training dynamics of language models, focusing on their ability to copy text from preceding context. |
| Approach: | They propose that Transformer-based language models develop copying abilities similarly to grokking . they argue that the connection between groking and context copying can improve in-context performance. |
| Outcome: | The proposed model development is similar to grokking, but the speed is independent of tokens trained. |
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On the In-context Generation of Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have the ability of in-context generation (ICG) when given an in-text prompt, they can implicitly recognize the pattern of the examples and complete the prompt in the desired way. |
| Approach: | They propose a plausible latent variable model to model the distribution of pretrained corpora and formalize ICG as a problem of next topic prediction. |
| Outcome: | The proposed model can model the distribution of pretrained corpora and then formalize ICG as a problem of next topic prediction. |
What Context Features Can Transformer Language Models Use? (2021.acl-long)
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| Challenge: | Recent studies show that transformer-based language models benefit from conditioning on contexts of hundreds to thousands of previous tokens. |
| Approach: | They propose to use lexical and structural information to ablate usable information in transformer language models. |
| Outcome: | The proposed model improves when conditioning on contexts of thousands of previous tokens. |
How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN (2023.tacl-1)
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| Challenge: | Current language models generate high-quality text, but are they copying it or have they learned generalizable linguistic abstractions? |
| Approach: | They propose a suite of analyses for assessing the novelty of generated text . they focus on sequential structure (n-grams) and syntactic structure (syntactical structure). |
| Outcome: | The proposed model-generated text is as novel as the baseline human-generated model- generated text, but it is copied substantially, the authors show . |
Identifying Semantic Induction Heads to Understand In-Context Learning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance, but lack of transparency in their inference logic raises concerns about their trustworthiness. |
| Approach: | They conduct a detailed analysis of the operations of attention heads to understand their in-context learning of LLMs. |
| Outcome: | The proposed analysis of attention heads reveals that they increase the output logits of object tokens and recall objects . the proposed model is a novel approach to understand the in-context learning of large language models. |
Grokking of Hierarchical Structure in Vanilla Transformers (2023.acl-short)
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| Challenge: | a recent study has shown that neural sequence models like transformers can generalize hierarchically when training for extended periods. |
| Approach: | They show that transformers can learn to generalize hierarchically after long training periods . they call this phenomenon structural grokking, which exhibits inverted U-shaped scaling in model depth . |
| Outcome: | The proposed model generalizes better than both very deep and very shallow models on multiple datasets. |
Investigating Transferability in Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Recent work on deep NLP models has centered on probing, a method that involves training classifiers for different tasks on model representations. |
| Approach: | They propose a method for determining the impact of each pretrained layer on transfer task performance by ablation. |
| Outcome: | The proposed method shows that pretraining models improve performance on downstream tasks . the results highlight the limitations of methods that operate on frozen models or single data samples. |
Rethinking the Role of Scale for In-Context Learning: An Interpretability-based Case Study at 66 Billion Scale (2023.acl-long)
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| Challenge: | 70% of attention heads and 20% of the feed forward networks can be removed with minimal decline in task performance. |
| Approach: | They propose to investigate whether in-context learning is not uniform across all components of a large language model. |
| Outcome: | The proposed model can remove 70% of attention heads and 20% of feed forward networks with minimal decline in task performance. |
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. |
| Outcome: | The proposed approach can detect features emergence, maintenance, and discontinuation during training stages. |
Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases (2025.acl-long)
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| Challenge: | Pretraining language models on formal language can improve their acquisition of natural language, but it is computationally limited. |
| Approach: | They hypothesize that effective transfer occurs when the formal language captures hierarchical dependencies present in natural language and remains within the computational limitations of the model architecture. |
| Outcome: | The proposed model can achieve lower loss on natural language and better generalization with a 33% smaller token budget than training on a matched amount of natural language. |
ICLEval: Evaluating In-Context Learning Ability of Large Language Models (2025.coling-main)
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| Challenge: | Existing evaluation frameworks focus on language abilities and knowledge, often overlooking the assessment of ICL ability. |
| Approach: | They propose to evaluate the ICL ability of Large Language Models (LLMs) using the ICLEval benchmark. |
| Outcome: | The proposed benchmark demonstrates that ICL ability is universally present in different LLMs and model size is not the sole determinant of ICL efficacy. |