Summary of Exploration Of Masked and Causal Language Modelling For Text Generation, by Nicolo Micheletti et al.
Exploration of Masked and Causal Language Modelling for Text Generation
by Nicolo Micheletti, Samuel Belkadi, Lifeng Han, Goran Nenadic
First submitted to arxiv on: 21 May 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 This paper compares the effectiveness of two approaches to text generation: Masked Language Modelling (MLM) and Causal Language Modelling (CLM). Large Language Models (LLMs) have achieved state-of-the-art performance in NLP tasks, but MLM’s ability to generate tokens anywhere in the text makes it a promising approach for text generation. The study pre-trains models on three datasets – medical discharge summaries, movie plot synopses, and authorship verification data – and evaluates their quality using quantitative metrics and human evaluation. The results show that MLM outperforms CLM across all datasets, with better coherence in generated text. While the study finds no strong correlation between text quality and downstream task performance, it highlights MLM’s potential for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares two ways to generate text: Masked Language Modelling (MLM) and Causal Language Modelling (CLM). Large language models have been very good at understanding natural language. The paper looks at which method is better at generating text. It trains the models on three different kinds of data – medical summaries, movie plots, and information about authors. Then it checks how well the generated text does using numbers and people evaluating it. The results show that MLM is much better than CLM at making good text. This study shows that using MLM to generate text could be a useful area to explore. |
Keywords
* Artificial intelligence * Nlp * Text generation
