Summary of Towards Transparency: Exploring Llm Trainings Datasets Through Visual Topic Modeling and Semantic Frame, by Charles De Dampierre et al.
Towards Transparency: Exploring LLM Trainings Datasets through Visual Topic Modeling and Semantic Frame
by Charles de Dampierre, Andrei Mogoutov, Nicolas Baumard
First submitted to arxiv on: 3 Jun 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 This paper addresses the critical issue of biased and low-quality content generated by Large Language Models (LLMs). While computation and model architecture have seen significant advancements, dataset curation efforts are still in their infancy. To improve the refinement of textual datasets, the authors propose Bunka, a software that leverages AI and Cognitive Science. The paper demonstrates how Topic Modeling, 2-dimensional Cartography, and Frame Analysis can increase transparency and quality. Specifically, it shows how these techniques can accelerate fine-tuning processes on different benchmarks. The authors argue that better tools are needed to explore and enhance the quality and transparency of LLM training datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about improving the way computers learn from text data. Right now, many decisions are being made by computers based on what they’ve learned from this data. But often, this data isn’t very good or biased, which means the computer’s answers might not be accurate. The authors propose a new software called Bunka that uses artificial intelligence and cognitive science to make text data better. They show how this can help make computers more transparent and give them more accurate answers. Overall, the goal is to create better tools for making sure the data computers learn from is high-quality and unbiased. |
Keywords
» Artificial intelligence » Fine tuning
