N-gram Prediction and Word Difference Representations for Language Modeling

• URL: http://arxiv.org/abs/2409.03295v1
• Date: Thu, 5 Sep 2024 07:03:23 GMT
• Title: N-gram Prediction and Word Difference Representations for Language Modeling
• Authors: DongNyeong Heo, Daniela Noemi Rim, Heeyoul Choi,
• Abstract summary: We introduce a simple N-gram prediction framework for the Causal Language Model (CLM) task. We also introduce word difference representation (WDR) as a surrogate and contextualized target representation during model training. To further enhance the quality of next word prediction, we propose an ensemble method that incorporates the future N words' prediction results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal language modeling (CLM) serves as the foundational framework underpinning remarkable successes of recent large language models (LLMs). Despite its success, the training approach for next word prediction poses a potential risk of causing the model to overly focus on local dependencies within a sentence. While prior studies have been introduced to predict future N words simultaneously, they were primarily applied to tasks such as masked language modeling (MLM) and neural machine translation (NMT). In this study, we introduce a simple N-gram prediction framework for the CLM task. Moreover, we introduce word difference representation (WDR) as a surrogate and contextualized target representation during model training on the basis of N-gram prediction framework. To further enhance the quality of next word prediction, we propose an ensemble method that incorporates the future N words' prediction results. Empirical evaluations across multiple benchmark datasets encompassing CLM and NMT tasks demonstrate the significant advantages of our proposed methods over the conventional CLM.

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