prathamchopra001/skills/literature_extraction

Literature Finding Extraction

Task

Extract research findings from academic paper excerpts. Literature findings must ALWAYS be attributed to their source papers using the ACTUAL author names from the context.

Output Format

Always respond with a valid JSON ARRAY (not wrapped in an object):

[
  {
    "claim": "Prior work by Smith et al. found that...",
    "confidence": 0.85,
    "evidence": "Quote or paraphrase from paper",
    "paper_id": "ID from context",
    "paper_title": "Full paper title",
    "authors": "Smith, Jones, et al.",
    "tags": ["keyword1", "keyword2"]
  }
]

Return [] (empty array) if no extractable findings exist.

IMPORTANT: Return the array directly, NOT wrapped in {"findings": [...]}.

Context Format

The context contains paper information in this format:

[Paper: Paper Title Here] [Authors: Smith, Jones, et al.] [DOI: xxx] [Paper ID: abc123]
Text content from the paper...

CRITICAL: Extract the ACTUAL author names from [Authors: ...] - do NOT use placeholder text like "[Authors]".

Decision Framework

What Makes a Literature Finding?

✅ YES - Extract these:

• Experimental results: "Smith et al. found that X increased by 15%..."
• Empirical observations: "The study showed 92% accuracy on..."
• Comparative results: "Their method outperformed baseline by..."
• Quantitative conclusions: "Results indicate a 2.3x improvement..."

❌ NO - Skip these:

• Paper metadata: "This paper was published in 2023..."
• Methodology descriptions: "The authors used PyTorch..."
• Future work: "Further research could..."
• Background/definitions: "Reinforcement learning is..."
• Vague statements: "The method works well"

Attribution Rules (CRITICAL)

Every claim MUST start with attribution using REAL names:

• "Prior work by Smith et al. found that..."
• "According to 'Convergence of Q-Learning', researchers showed..."
• "Jones and colleagues demonstrated that..."
• "The study by Williams (2023) revealed..."

NEVER write claims like:

• ❌ "Prior work by [Authors] found..." (placeholder!)
• ❌ "X increases convergence speed" (no attribution)
• ❌ "We found that..." (you didn't find it)

Confidence Scoring

| Score | Criteria | |-------|----------| | 0.90+ | Direct experimental result with statistics (p-values, CIs) | | 0.75-0.89 | Clear conclusion with quantitative support | | 0.60-0.74 | Qualitative finding with some evidence | | 0.45-0.59 | Interpretation or inference from results | | <0.45 | Weak claim, speculation, or review statement |

Examples

Input

[Paper: Convergence of Q-Learning] [Authors: Smith, Jones, Wang] [DOI: 10.1234/xyz] [Paper ID: sem_abc123]
Results show that the Q-learning agent with ε=0.1 achieved convergence in 450 episodes, compared to 780 episodes for ε=0.3 (p<0.01, n=50 runs).

Output

[
  {
    "claim": "Prior work by Smith et al. found that lower exploration rates (ε=0.1) lead to significantly faster convergence than higher rates (ε=0.3) in Q-learning agents",
    "confidence": 0.92,
    "evidence": "450 vs 780 episodes to convergence, p<0.01, n=50 runs",
    "paper_id": "sem_abc123",
    "paper_title": "Convergence of Q-Learning",
    "authors": "Smith, Jones, Wang",
    "tags": ["q-learning", "exploration", "convergence", "epsilon"]
  }
]

Input (No findings)

[Paper: RL Survey] [DOI: N/A] [Paper ID: arxiv_456]
This section provides background on reinforcement learning algorithms.

Output

[]

Anti-Patterns

• ❌ Using "[Authors]" placeholder instead of real names
• ❌ Missing attribution entirely
• ❌ Wrapping in {"findings": [...]} instead of just [...]
• ❌ Presenting as own discovery: "We found that..."
• ❌ Wrong paper_id: Using paper title instead of ID
• ❌ Extracting methodology as findings

Quality Checklist

1. Is output a valid JSON array (not wrapped in object)?
2. Does EVERY claim use REAL author names (not "[Authors]")?
3. Is paper_id extracted from [Paper ID: xxx] in context?
4. Are authors extracted from [Authors: xxx] in context?
5. Does evidence support the confidence score?