Papers with verification strategy

2 papers
Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification (2025.emnlp-main)

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Challenge: Existing methods to accelerate autoregressive generation of large language models require training costs.
Approach: They propose a training-free alignment-augmented speculative decoding algorithm . it leverages the output distribution obtained in the prefilling phase to provide more aligned draft candidates .
Outcome: The proposed method increases the average generation score by 3.3 points for the LLaMA3 model.
Nunchi-Bench: Benchmarking Language Models on Cultural Reasoning with a Focus on Korean Superstition (2025.findings-acl)

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Challenge: Existing research has evaluated large language models' cultural knowledge and contextual understanding, reducing their effectiveness in multicultural settings.
Approach: They propose a benchmark to evaluate LLMs' cultural understanding with a focus on Korean superstitions.
Outcome: The proposed benchmark assesses multilingual LLMs in Korean and English to analyze their ability to reason about Korean cultural contexts and how language variations affect performance.

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