Authors: Haris Jabbar
Published on: July 14, 2023
Impact Score: 8.07
Arxiv code: Arxiv:2307.07262
Summary
- What is new: The introduction of a linguistically motivated tokenization scheme, MorphPiece, which outperforms traditional stat-based tokenizers in various NLP tasks.
- Why this is important: Existing tokenizers do not consider linguistic features, affecting the efficiency of language models.
- What the research proposes: Developing MorphPiece, a new tokenization method based on morphological segmentation.
- Results: MorphGPT, utilizing MorphPiece, showed superior or comparable performance to GPT-2 across different benchmarks, with fewer training iterations.
Technical Details
Technological frameworks used: GPT-style causal language model
Models used: MorphGPT, OpenAI GPT-2
Data used: GLUE Benchmark, Massive Text Embedding Benchmark (MTEB), FLOTA
Potential Impact
NLP-driven technology companies, language model developers, educational tech, and AI-driven analytics firms.
Want to implement this idea in a business?
We have generated a startup concept here: Morphling AI.
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