Papers with APD

3 papers
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction (2022.acl-short)

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Challenge: Modern writing assistance applications always contain a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences.
Approach: They propose a simple yet effective approach to Align-and-Predict Decoding for most popular sequence-to-sequence models to offer more flexibility for the precision-recall trade-off.
Outcome: The proposed model can be used in both English and Chinese GEC models and achieve state-of-the-art results.
A Systematic Comparison of Contextualized Word Embeddings for Lexical Semantic Change (2024.naacl-long)

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Challenge: Contextualized embeddings are the preferred tool for modeling Lexical Semantic Change (LSC) current evaluations focus on a specific task known as Graded Change Detection (GCD) however, performance comparisons between different approaches are often misleading due to diverse settings.
Approach: They evaluate the performance of contextualized embeddings for Lexical Semantic Change (LSC) they break the problem into Word-in-Context (WiC) and Word Sense Induction (WSI) tasks .
Outcome: The proposed model outperforms other models on eight available benchmarks for Lexical Semantic Change (LSC) while comparable to GPT-4.
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM (2024.emnlp-main)

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Challenge: Contrastive decoding (CD) improves the next-token distribution of a large expert language model (LM) using a small amateur LM.
Approach: They propose a new unsupervised decoding method called Asymptotic Probability Decoding (APD) that extrapolates the probability curves from the LMs of different sizes to infer the asymptototic probabilities from an infinitely large LM.
Outcome: The proposed method improves the next-token distribution of a large expert language model using a small amateur LM.

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