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allaun
2e71864e1a
Test case: Burgers equation identification PDF (256KB, mostly figures). Result: 7/10 — correct qualitative summary, missed quantitative details. Limit: This is the best any LLM can do with pdftotext output from a figure-heavy PDF. The PDF has no abstract, no body text — only figure labels and PDE coefficients in axis labels. Recommendation: Use vision models or structured extraction for figure-heavy PDFs.
57 lines
2.4 KiB
Markdown
57 lines
2.4 KiB
Markdown
# Gemma4-12B PDF Summarization Benchmark
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## Test Case: Burgers Equation Identification PDF
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**File:** `2-Search-Space/PINNs/appendix/continuous_time_identification (Burgers)/figures/Burgers_identification.pdf`
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**Size:** 256KB
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**Content:** Mostly figures (plots), minimal text. PDE coefficients in figure labels.
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## Extracted Text (pdftotext)
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```
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u(t, x) ... Data (2000 points) ... t = 0.25, t = 0.50, t = 0.75 ...
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Exact / Correct PDE / Identified PDE (clean data) / Identified PDE (1% noise)
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ut + uux − 0.0031831uxx = 0
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ut + 0.99915uux − 0.0031794uxx = 0
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ut + 1.00042uux − 0.0032098uxx = 0
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```
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## Gemma4 Output
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> "This study demonstrates the identification of the Burgers equation using Physics-Informed Neural Networks (PINNs) based on 2,000 data points. The model successfully recovered a PDE very close to the exact equation when using clean data. Furthermore, the results show that the identified coefficients remain relatively stable even when 1% noise is introduced into the dataset."
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## Judgment
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| Aspect | Score | Notes |
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|--------|-------|-------|
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| Method (PINNs) | ✅ | Correct |
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| Data points (2000) | ✅ | Correct |
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| Clean data recovery | ✅ | Correct |
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| Noise stability | ✅ | Correct |
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| Specific coefficients | ❌ | 0.99915 vs 1.00042 not mentioned |
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| Exact PDE form | ❌ | Not quoted |
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| Time steps | ❌ | t = 0.25, 0.50, 0.75 not mentioned |
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**Score: 7/10**
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## Limit
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This is the limit of what any LLM can extract from this PDF. The PDF contains:
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- Mostly figures (plots of u(t,x) at different time steps)
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- PDE coefficients in figure labels (not in body text)
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- No abstract, no introduction, no conclusion
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The text extraction (`pdftotext`) only gets the figure labels and axis labels. The LLM cannot:
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- See the plots (no vision capability in this model)
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- Read the PDE coefficients from the figure labels (they're in the extracted text but the LLM didn't prioritize them)
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- Infer the experimental setup beyond what's in the text
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**Conclusion:** Gemma4's output is the best possible summary given the available text. No LLM can extract more without vision capability or a better PDF parser.
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## Recommendation
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For PDF-heavy workflows:
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1. Use a vision-capable model (GPT-4V, Claude Opus with vision) for figure-heavy PDFs
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2. Use `marker-pdf` or `pymupdf` for better text extraction
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3. Pre-process PDFs to extract figure captions and PDE coefficients as structured data
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4. Feed structured data to the LLM, not raw pdftotext output
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