A Perspective on LLM Data Generation with Few-shot Examples: from Intent to Kubernetes Manifest (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have transformed how complex tasks can be automated . traditional cloud computing operations involve complex manual configurations . |
| Approach: | They propose a pipeline for generating K8s manifests directly from user-described intents expressed in natural language using LLMs. |
| Outcome: | The proposed pipeline can generate K8s manifests directly from user-described intents expressed in natural language using LLMs. |
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| Challenge: | Large language models (LLMs) have advanced the field of NLP significantly, but deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. |
| Approach: | They propose a unified data creation pipeline that requires only a single formatting example. |
| Outcome: | The proposed pipeline can generate data with a single formatting example. |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
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Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
| Approach: | They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge. |
| Outcome: | The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. |
Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation (2024.acl-long)
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| Challenge: | Existing benchmarks for data-to-text generation are saturated, and there is no way to test them. |
| Approach: | They propose a tool for collecting structured data from public APIs to analyze the behavior of open large language models on the task of data-to-text generation. |
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Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
Use Random Selection for Now: Investigation of Few-Shot Selection Strategies in LLM-based Text Augmentation (2025.findings-emnlp)
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| Challenge: | generative large language models are increasingly used for data augmentation tasks . text samples are mostly selected randomly and a comprehensive overview of other sample selection strategies is lacking. |
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
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LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
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| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
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A Rigorous Evaluation of LLM Data Generation Strategies for Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are increasingly used to generate synthetic textual data for training smaller specialized models. |
| Approach: | They evaluate the performance of large language models and their generation strategies in 11 different languages using 3 NLP tasks and 4 open-source LLMs. |
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LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
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