Martin Josifoski, Maxime Peyrard, Frano Rajič, Jiheng Wei, Debjit Paul, Valentin Hartmann, Barun Patra, Vishrav Chaudhary, Emre Kiciman, Boi Faltings
| Challenge: | Existing studies only compare decoding algorithms in narrow scenarios, and their findings do not generalize across tasks. |
| Approach: | They propose a taxonomy of misalignment mitigation strategies to provide a unifying view of decoding as a tool for alignment. |
| Outcome: | The proposed taxonomy combines likelihood and utility assumptions to provide general statements about decoding as a tool for alignment across tasks. |
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| Challenge: | Decoding strategies affect the probability distribution underlying the output of a language model and can therefore affect both generation quality and uncertainty. |
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A Thorough Examination of Decoding Methods in the Era of LLMs (2024.emnlp-main)
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| Challenge: | Decoding methods are essential for converting language models from next-token predictors into practical task solvers. |
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Understanding Cross-Lingual Alignment—A Survey (2024.findings-acl)
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
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MetaAlign: Align Large Language Models with Diverse Preferences during Inference Time (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) need to be aligned with human expectations to ensure their safety and utility in most applications. |
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Jiaming Ji, Kaile Wang, Tianyi Alex Qiu, Boyuan Chen, Jiayi Zhou, Changye Li, Hantao Lou, Josef Dai, Yunhuai Liu, Yaodong Yang
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LIONs: An Empirically Optimized Approach to Align Language Models (2024.emnlp-main)
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DeAL: Decoding-time Alignment for Large Language Models (2025.acl-long)
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| Challenge: | Large Vision-Language Models have demonstrated remarkable capabilities in processing both visual and textual information. |
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