EMNLP Findings2025
Can Language Models Follow Multiple Turns of Entangled Instructions?
Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li
Abstract
Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng Li, Bing Yin, Jingbo Shang, Heng Ji. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.
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Builds on15
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Finetuned Language Models are Zero-Shot LearnersJason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu et al.ICLR 2022 · 4,966 citations
- Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic EncodersYupeng Hou, Jiacheng Li, Xiangjun Fu, Zhankui He et al.ACL 2026 · 346 citations
- ProPILE: Probing Privacy Leakage in Large Language ModelsSiwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri et al.NeurIPS 2023 · 229 citations
- Beyond Memorization: Violating Privacy via Inference with Large Language ModelsRobin Staab, Mark Vero, Mislav Balunovic, Martin T. VechevICLR 2024 · 211 citations
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