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.

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Challenge: Using a method to identify next-token neurons, we find that some attention heads recognize contexts relevant to predicting a token and activate a downstream token-predicting neuron accordingly.
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Analyzing the Attention Heads for Pronoun Disambiguation in Context-aware Machine Translation Models (2025.coling-main)

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Challenge: In Context-aware Machine Translation, the context sentences are available to the system and can be used to maintain coherence of translation and resolve ambiguities.
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Exploring Attention Attractors in Large Language Models (2026.acl-long)

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Challenge: Existing studies have suggested that attention attractors function as "summary tokens" while others speculate that tokens with weaker semantics attract high attention, they act as attention sinks that offload excessive attention.
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Plausibility Processing in Transformer Language Models: Focusing on the Role of Attention Heads in GPT (2023.findings-emnlp)

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Challenge: Using attention heads, we can explore how Transformer language models process semantic knowledge, especially regarding the plausibility of noun-verb relations.
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Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)

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Challenge: a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks.
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A Critical Study of What Code-LLMs (Do Not) Learn (2024.findings-acl)

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Challenge: Large Language Models trained on code corpora have limitations such as suggesting codes with syntactic errors, variable misuse etc.
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Attention Can Reflect Syntactic Structure (If You Let It) (2021.eacl-main)

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Challenge: a recent study has attempted to decode linguistic structure from the Transformer . but, much of the work focused on English, a language with rigid word order and a lack of inflectional morphology.
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How Do Multilingual Language Models Remember Facts? (2025.findings-acl)

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Challenge: Prior research has focused on English monolingual models, but how these mechanisms generalize to non-English languages remains unexplored.
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How do Large Language Models Learn In-Context? Query and Key Matrices of In-Context Heads are Two Towers for Metric Learning (2024.emnlp-main)

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Challenge: In-context learning (ICL) is an emergent ability of large language models.
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Induction Heads as an Essential Mechanism for Pattern Matching in In-context Learning (2025.findings-naacl)

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Challenge: Large language models have shown a remarkable ability to learn and perform complex tasks through in-context learning (ICL).
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