Challenge: Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field.
Approach: They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field .
Outcome: The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field .

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Challenge: Existing studies overlook the multi-turn instruction following ability of large language models (LLMs) Extensive experiments show that Parrot improves current LLMs by up to 7.2% in multi- turn instruction following.
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Challenge: Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored.
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Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages (2024.acl-long)

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Challenge: Despite the rapid development of large language models, the language capabilities of most open-source LLMs are primarily focused on English due to data constraints.
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An Empirical Study of Instruction-tuning Large Language Models in Chinese (2023.findings-emnlp)

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Challenge: emergence of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI) however, the closed source of LLMs coupled with the requirement for massive computing resources has deterred researchers from reaching the LLM training stage.
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Document-Level Machine Translation with Large Language Models (2023.emnlp-main)

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Challenge: Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing tasks.
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Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)

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Challenge: Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored.
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Don’t Trust ChatGPT when your Question is not in English: A Study of Multilingual Abilities and Types of LLMs (2023.emnlp-main)

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Challenge: Existing studies have shown that large language models can perform a wide variety of language tasks when presented in English.
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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.
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Tuna: Instruction Tuning using Feedback from Large Language Models (2023.findings-emnlp)

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Challenge: LLms like LLaMA have shown to be cost-effective for generating better responses . however, the instruction-tuned model has only seen one response per instruction .
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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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