Summary of Hysem: a Context Length Optimized Llm Pipeline For Unstructured Tabular Extraction, by Narayanan Pp et al.
HySem: A context length optimized LLM pipeline for unstructured tabular extraction
by Narayanan PP, Anantharaman Palacode Narayana Iyer
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed pipeline, HySem, enables accurate semantic representation of HTML tables in the pharmaceutical industry. Large Language Models (LLMs) have shown promise, but challenges persist regarding accuracy and context size limitations. HySem addresses these limitations by employing a novel context length optimization technique to generate semantic JSON representations from tables. The approach utilizes a custom fine-tuned model designed for small and medium pharmaceutical enterprises, providing competitive performance against OpenAI GPT-4o. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HySem is a new way to turn table data into a format that computers can understand. Right now, companies in the pharmaceutical industry rely on detailed tables to report information about their products, but these tables are often hard to use because they’re not organized well and contain too much extra information. Large computer models have been trying to solve this problem, but they’ve had trouble being accurate and working with large amounts of data. HySem is a solution that works with smaller companies and uses a special technique to make sure the output is correct. |
Keywords
» Artificial intelligence » Context length » Gpt » Optimization
