Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection (2025.acl-long)
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| Challenge: | Existing work has been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary. |
| Approach: | They propose to evaluate a set of tasks using decoding-free candidate selection methods on a comprehensive set of questions. |
| Outcome: | The proposed methods are evaluated on a set of tasks including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with 10k+ options. |
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| Challenge: | Light Latent-space Decoding (L2D) is an efficient and efficient latent- space decoding method. |
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
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| Challenge: | Recent work on tokenizer-free models shows promising results in cross-lingual transfer . previous work focused on reporting accuracy on a limited set of tasks and data settings . |
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Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
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Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
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Context-Aware Assistant Selection for Improved Inference Acceleration with Large Language Models (2024.emnlp-main)
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| Challenge: | Generative large language models generate a high-dimensional probability distribution over all tokens in their vocabulary. |
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On Decoding Strategies for Neural Text Generators (2022.tacl-1)
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| Challenge: | a recent study suggests that decoding strategies may be more important than the model architecture itself when generating text from probabilistic models. |
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