TimeCapsule LLM: A Historical Language Model
TimeCapsule LLM is a unique language model trained solely on datasets from 1800 to 1875, aimed at minimizing modern biases and mimicking historical language styles. Developed by Hayk Grigorian, this model uses a limited, curated dataset to reduce contemporary influences. Initial versions were based on nanoGPT, showing significant improvements in later iterations with Microsoft’s Phi 1.5, linking real events to responses. Version 2 is under development, intending to encompass 90GB of historical texts, highlighting the noticeable gender bias in pronouns and geographical mentions.
Despite challenges, such as hallucinations and incoherent outputs in earlier versions, TimeCapsule LLM has sparked discussions on platforms like Hacker News. The innovative approach raises questions about AI’s potential for historical accuracy and its ability to generate insights that could lead to scientific breakthroughs.
Explore more on GitHub: TimeCapsule LLM.
