Challenge: Recent large language models (LLMs) have shown strong abilities to understand natural language, but how these factors affect the models’ language perception is unclear.
Approach: They compare the self-attention of several existing large language models in different sizes to assess the effect of scaling and instruction tuning on language perception.
Outcome: The proposed models are closer to non-native speakers than native speakers in attention, suggesting a sub-optimal language perception of all models.

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From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning (2024.naacl-long)

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Challenge: Large Language Models (LLMs) have achieved remarkable success in aligning with user intentions.
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Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)

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Challenge: Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models.
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I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses (2024.emnlp-main)

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Challenge: Recent research has demonstrated that a large language model (LLM) can generate training data for another LLM, or for creating supplementary training materials, such as rationales.
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LLM Sensitivity Challenges in Abusive Language Detection: Instruction-Tuned vs. Human Feedback (2025.coling-main)

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Challenge: Existing studies show that instruction-tuned LLMs under-predict positive classes . however, they are overly sensitive and can be applied for abuse detection without fine-tuning .
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Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025.coling-main)

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Challenge: Popularity of Large Language Models (LLMs) has seen a skyrocketing increase in recent years.
Approach: They present a systematic review of the language adaptation process for Large Language Models including vocabulary expansion, continued pre-training, and instruction fine-tuning.
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Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca (2024.findings-eacl)

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Challenge: Foundational large language models (LLMs) can be instruction-tuned to perform open-domain question answering, facilitating applications like chat assistants.
Approach: They employ a dataset and machine translations of it to form multilingual data and use it to tune LLMs.
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Psychometric Predictive Power of Large Language Models (2024.findings-naacl)

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Challenge: despite efforts to align large language models with human preferences, instruction tuning does not always make LLMs human-like from a cognitive modeling perspective.
Approach: They find that instruction tuning does not always make large language models human-like from a cognitive perspective.
Outcome: The proposed prompts improve predictive power but are still inferior to small base models.
Investigating Multilingual Instruction-Tuning: Do Polyglot Models Demand for Multilingual Instructions? (2024.emnlp-main)

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Challenge: a study of multilingual pre-trained LLMs on parallel instruction-tuning benchmarks shows that instruction-following models can be used across languages by up to 9.9%.
Approach: They conduct an extensive study of the performance of multilingual pre-trained LLMs instruction-tuned on parallel instruction-uning datasets.
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Multilingual Instruction Tuning With Just a Pinch of Multilinguality (2024.findings-acl)

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Challenge: Using multilingual instruction tuning, large language models can be used to follow instructions in multiple languages . a multilingual model can be tuned on a wide range of languages, yet most datasets are limited to English .
Approach: They investigate how multilinguality during instruction tuning affects instruction-following across languages . they find that only 40 multilingual examples improve multilingual instruction- follow .
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INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)

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Challenge: Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.
Approach: They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions.
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