Summary of N-gram Prediction and Word Difference Representations For Language Modeling, by Dongnyeong Heo et al.
N-gram Prediction and Word Difference Representations for Language Modeling
by DongNyeong Heo, Daniela Noemi Rim, Heeyoul Choi
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to causal language modeling (CLM), which underlies the success of large language models (LLMs). However, the current training method for next word prediction may lead to an excessive focus on local dependencies within a sentence. To address this issue, the authors introduce a simple N-gram prediction framework and a word difference representation (WDR) as a surrogate target during model training. Additionally, they propose an ensemble method that incorporates future N words’ prediction results to enhance next word prediction quality. The proposed methods are evaluated across multiple benchmark datasets for CLM and neural machine translation (NMT) tasks, demonstrating significant advantages over conventional CLM approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at predicting the next word in a sentence. Right now, these models can get stuck focusing on small details instead of understanding the bigger picture. The authors come up with some new ideas to help language models do a better job of predicting words. They test their ideas using several different datasets and find that they work really well. This is important because it could lead to improvements in things like machine translation and text summarization. |
Keywords
» Artificial intelligence » N gram » Summarization » Translation
