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LLMs applied to dating ancient texts and discovering scientific causality · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers are exploring the application of large language models (LLMs) in historical and scientific data analysis. One paper details an automated system using LLMs and astronomical tools to date ancient Babylonian Astronomical Diaries, achieving high accuracy in identifying specific years. Another paper introduces a framework called COAT, which leverages LLMs to discover hidden causal variables and mechanisms within unstructured data, aiding in scientific discovery. AI

IMPACT Demonstrates LLMs' potential in specialized scientific domains, from historical data analysis to uncovering complex causal relationships.

RANK_REASON Two arXiv papers detailing novel applications of LLMs in scientific research.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs applied to dating ancient texts and discovering scientific causality · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Lucas Carrit Delgado Pinheiro, Yuan-Sen Ting, Jon Clindaniel, Lu Li, Yingbin Liang, Ness Shroff ·

    Automated, auditable dating of the Babylonian Astronomical Diaries with language models and astronomical tools

    arXiv:2610.34297v1 Announce Type: cross Abstract: The Babylonian Astronomical Diaries record the sky over nearly six centuries. Many tablets are broken and have lost their date formula. Experts dated many of them one tablet at a time in the standard edition, Astronomical Diaries …

  2. arXiv cs.AI TIER_1 English(EN) · Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang ·

    Discovering and Reasoning of Causality in the Hidden World with Large Language Models

    arXiv:2402.03941v4 Announce Type: replace-cross Abstract: Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies o…

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