Papers with APD
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. |