Using LLM for Research Purposes: A Guide

Описание и применение

Автоматизированный пайплайн на Python использует LLM для скриншота, извлечения и синтеза научных статей, сокращая ручной труд.

Задача

Researchers need to survey large literature without reading every paper in full.

Результат

The script automatically screens abstracts, extracts structured findings, and produces a concise, markdown literature review, reducing manual effort and ensuring claims are grounded in source text.

Рабочий процесс

• Initialize OpenAI-compatible client pointing at Oxlo.ai.
• Create a JSON corpus of paper abstracts.
• Define a system prompt that enforces no hallucination and structured output.
• Screen each abstract for relevance using a low‑temperature LLM and score 0‑10.
• Extract structured findings from papers scoring ≥5, returning JSON with key_finding, method, limitation, confidence.
• Synthesize a markdown literature review from aggregated findings using a reasoning‑capable LLM.
• Print the final report.
• Optionally extend to PDF parsing and larger models for full‑text papers.

Доказательства

We read each abstract and ask the model whether it addresses our research question.

Источник

Extract structured findings from this paper. Return valid JSON: { "key_finding": "string", "method": "string", "limitation": "string", "confidence": "high|medium|low" }

Источник

Write a short literature review with these sections: 1. Executive Summary (2-3 sentences) 2. Thematic Synthesis ...

Источник