REPOGEO REPORT · LITE

DGoettlich/history-llms

Default branch main · commit fe9a2723 · scanned 5/26/2026, 11:37:36 PM

GitHub: 1,762 stars · 35 forks

AI VISIBILITY SCORE

23 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

2 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface DGoettlich/history-llms, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition the README's opening to clearly state the project's unique focus

Why:

CURRENT

# History LLMs

<table>
  <tr>
    <td align="center">
      <strong>Daniel Göttlich</strong><br/>
      <sub>University of Zurich</sub><br/>
    </td>
    <td align="center">
      <strong>Dominik Loibner</strong><br/>
      <sub>University of Zurich</sub>
    </td>
    <td align="center">
      <strong>Guohui Jiang</strong><br/>
      <sub>Cologne University</sub>
    </td>
    <td align="center">
      <strong>Hans-Joachim Voth</strong><br/>
      <sub>University of Zurich</sub>
    </td>
  </tr>
</table>

COPY-PASTE FIX

# History LLMs

This repository serves as an information hub for our project focused on training the largest possible historical Large Language Models (LLMs). We aim to develop and evaluate LLMs specifically optimized for analyzing historical documents and archives.

<table>
  <tr>
    <td align="center">
      <strong>Daniel Göttlich</strong><br/>
      <sub>University of Zurich</sub><br/>
    </td>
    <td align="center">
      <strong>Dominik Loibner</strong><br/>
      <sub>University of Zurich</sub>
    </td>
    <td align="center">
      <strong>Guohui Jiang</strong><br/>
      <sub>Cologne University</sub>
    </td>
    <td align="center">
      <strong>Hans-Joachim Voth</strong><br/>
      <sub>University of Zurich</sub>
    </td>
  </tr>
</table>

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file (e.g., LICENSE.md or LICENSE.txt) in the root of the repository with the text of a standard open-source license such as MIT or Apache-2.0.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface DGoettlich/history-llms

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

GPT-3.5/GPT-4

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

GPT-3.5/GPT-4 · recommended 1×
OpenAI API · recommended 1×
Llama 2 · recommended 1×
huggingface/transformers · recommended 1×
Meta's API · recommended 1×

CATEGORY QUERY
How to train large language models specifically for historical text analysis?
you: not recommended
AI recommended (in order):
1. GPT-3.5/GPT-4
2. OpenAI API
3. Llama 2
4. Hugging Face Transformers (huggingface/transformers)
5. Meta's API
6. Mistral 7B/Mixtral 8x7B
7. DeepSpeed (microsoft/DeepSpeed)
8. Historical BERT models
AI recommended 8 alternatives but never named DGoettlich/history-llms. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Where can I find pre-trained LLMs optimized for analyzing historical documents and archives?
you: not recommended
AI recommended (in order):
1. Google Cloud Document AI
2. Microsoft Azure Form Recognizer
3. Hugging Face Transformers
4. BERT
5. RoBERTa
6. DeBERTa
7. Transkribus
8. OpenAI GPT-4
9. GPT-3.5
10. Amazon Textract
11. Tesseract OCR
AI recommended 11 alternatives but never named DGoettlich/history-llms. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of DGoettlich/history-llms?
pass
AI named DGoettlich/history-llms explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts DGoettlich/history-llms in production, what risks or prerequisites should they evaluate first?
pass
AI named DGoettlich/history-llms explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo DGoettlich/history-llms solve, and who is the primary audience?
pass
AI did not name DGoettlich/history-llms — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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DGoettlich/history-llms — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite