Summary of Hitgram: a Platform For Experimenting with N-gram Language Models, by Shibaranjani Dasgupta et al.
HITgram: A Platform for Experimenting with n-gram Language Models
by Shibaranjani Dasgupta, Chandan Maity, Somdip Mukherjee, Rohan Singh, Diptendu Dutta, Debasish Jana
First submitted to arxiv on: 14 Dec 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 introduces HITgram, a lightweight platform for experimenting with n-gram models, designed to address the limitations of large language models (LLMs) on resource-constrained environments. HITgram supports unigrams to 4-grams, incorporating features like context-sensitive weighting, Laplace smoothing, and dynamic corpus management to improve prediction accuracy even for unseen word sequences. The platform is scalable and efficient, achieving impressive processing speeds, including generating 2-grams from a 320MB corpus in just 62 seconds. This technology has the potential to broaden its utility through planned enhancements such as multilingual support, advanced smoothing, parallel processing, and model saving. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HITgram is a new way for scientists to study language models without needing powerful computers. It’s like a special tool that makes it easier to work with words and phrases. The tool can help make predictions about what comes next in a sentence or text. It works fast too – it can look at a big book of words (like a 320MB file) and find patterns in just 62 seconds! This tool is important because it helps people who don’t have access to powerful computers join the conversation about language models. |
Keywords
» Artificial intelligence » N gram
