Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. |
| Approach: | They propose to provide datasets covering all major training stages including pretraining, instruction fine-tuning, and reasoning distillation with cybersecurity-specific self-reflection data. |
| Outcome: | Extensive ablation studies show that LLMs acquire their knowledge during pretraining, while reasoning distillation leads to a 15% gain in security certification (CISSP). |
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| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
| Approach: | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs . |
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LLMSurgeon: Diagnosing Data Mixture of Large Language Models (2026.acl-long)
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Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen
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Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls (2025.emnlp-main)
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Feiyang Kang, Newsha Ardalani, Michael Kuchnik, Youssef Emad, Mostafa Elhoushi, Shubhabrata Sengupta, Shang-Wen Li, Ramya Raghavendra, Ruoxi Jia, Carole-Jean Wu
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Parametric Knowledge is Not All You Need: Toward Honest Large Language Models via Retrieval of Pretraining Data (2026.findings-acl)
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| Challenge: | Large language models are highly capable of answering questions, but they are often unaware of their own knowledge boundary, i.e., knowing what they know and what they don’t know. |
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| Challenge: | Large Language Models (LLMs) are increasingly applied across various domains, but the ways they leverage their training data during inference remains only partially understood. |
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| Challenge: | Existing studies do not examine how leaked instances in training datasets influence LLMs’ output and detection capabilities. |
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Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation (2024.acl-long)
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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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| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
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OmniCompliance-100K: A Multi-Domain, Rule-Grounded, Real-World Safety Compliance Dataset (2026.findings-acl)
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| Challenge: | Existing LLM safety datasets rely on ad-hoc taxonomies and lack rule-grounded, real-world cases. |
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