Generated: 2025-10-05
Based on: Research findings in research_sota_paper_quality_assessment.md
Strategy: Systematic, phased implementation with progressive enhancement
Timeline: 16 weeks (4 phases)
- Integrate SOTA Methods: SciBERT, Hybrid models, BERTScore into existing system
- Performance Targets: 42% accuracy improvement, 50-100x cost reduction
- Backward Compatibility: Maintain current GPT-4 analysis while adding new capabilities
- Validation: Ensure new methods meet quality standards through A/B testing
- Week 4: SciBERT scorer operational
- Week 8: Hybrid model integrated
- Week 12: Multi-task learning deployed
- Week 16: Production ready with full validation
Goal: Set up infrastructure, implement basic SciBERT scorer, establish validation framework
Owner: DevOps Priority: P0 (Critical) Dependencies: None
Actions:
# Add to pyproject.toml
poetry add transformers==4.36.0
poetry add torch==2.1.0
poetry add bert-score==0.3.13
poetry add scikit-learn==1.3.2
poetry add spacy==3.7.2
poetry add nltk==3.8.1
poetry add textstat==0.7.3
poetry add accelerate==0.25.0
# Install dependencies
poetry installFiles to Modify:
pyproject.toml: Add new dependencies
Validation:
poetry run python -c "import transformers, torch, bert_score; print('✅ Dependencies OK')"Estimated Time: 2 hours Risk: Low - standard package installation
Owner: ML Engineer Priority: P0 (Critical) Dependencies: Task 1.1
Actions:
# Create model download script
cat > scripts/download_models.py << 'EOF'
"""Download required pretrained models."""
import sys
from transformers import AutoModel, AutoTokenizer
from pathlib import Path
def download_models():
models = [
"allenai/scibert_scivocab_uncased",
"roberta-base",
"microsoft/deberta-xlarge-mnli"
]
cache_dir = Path("./models/cache")
cache_dir.mkdir(parents=True, exist_ok=True)
for model_name in models:
print(f"Downloading {model_name}...")
try:
AutoTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
AutoModel.from_pretrained(model_name, cache_dir=cache_dir)
print(f"✅ {model_name} downloaded")
except Exception as e:
print(f"❌ {model_name} failed: {e}")
sys.exit(1)
print("\n✅ All models downloaded successfully")
if __name__ == "__main__":
download_models()
EOF
# Run download
poetry run python scripts/download_models.pyAdditional Downloads:
# spaCy language model
poetry run python -m spacy download en_core_web_sm
# NLTK data
poetry run python -c "import nltk; nltk.download('punkt'); nltk.download('averaged_perceptron_tagger')"Files Created:
scripts/download_models.py: Model download scriptmodels/cache/: Model cache directory (add to .gitignore)
Validation:
from transformers import AutoModel
model = AutoModel.from_pretrained("allenai/scibert_scivocab_uncased")
print(f"✅ SciBERT loaded: {model.config.hidden_size} dimensions")Estimated Time: 3 hours (including download time) Risk: Medium - network dependent, large model files (~1.5GB total)
Owner: Research Scientist Priority: P0 (Critical) Dependencies: None
Actions:
-
Collect 50 scientific papers with diverse quality levels:
- 10 high-quality (top-tier journals)
- 20 medium-quality (good conferences)
- 10 low-quality (rejected papers)
- 10 mixed-quality (requiring revision)
-
Get human expert scores (1-10 scale):
- Overall quality
- Novelty
- Methodology
- Clarity
- Significance
-
Create validation dataset:
# scripts/create_validation_dataset.py
import json
from pathlib import Path
validation_data = {
"papers": [
{
"id": "paper_001",
"title": "...",
"content": "...",
"human_scores": {
"overall": 9,
"novelty": 8,
"methodology": 9,
"clarity": 8,
"significance": 9
},
"expert_reviewer": "Dr. Smith",
"domain": "neuroscience"
},
# ... 49 more papers
],
"metadata": {
"total_papers": 50,
"creation_date": "2025-10-05",
"version": "1.0"
}
}
# Save to file
output_path = Path("data/validation/validation_dataset_v1.json")
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "w") as f:
json.dump(validation_data, f, indent=2)Files Created:
data/validation/validation_dataset_v1.json: Validation datasetdata/validation/README.md: Dataset documentation
Validation:
- Verify 50 papers collected
- Check human score distribution (balanced across quality levels)
- Ensure domain diversity
Estimated Time: 40 hours (distributed over week 1-2) Risk: High - requires expert time for scoring
Owner: ML Engineer Priority: P0 (Critical) Dependencies: Tasks 1.1, 1.2
Actions:
# src/services/paper/scibert_scorer.py - NEW FILE
"""SciBERT-based paper quality scorer."""
from typing import Dict
import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModel
class SciBERTQualityScorer:
"""Score scientific papers using SciBERT embeddings."""
def __init__(self, model_path: str = "allenai/scibert_scivocab_uncased"):
"""Initialize SciBERT scorer.
Args:
model_path: HuggingFace model identifier or local path
"""
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load SciBERT
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.encoder = AutoModel.from_pretrained(model_path).to(self.device)
# Quality assessment head (will be trained later)
self.quality_head = self._create_quality_head()
def _create_quality_head(self) -> nn.Module:
"""Create classification head for quality scoring."""
return nn.Sequential(
nn.Linear(768, 256), # SciBERT hidden size = 768
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(256, 128),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(128, 5) # 5 quality dimensions
).to(self.device)
async def score_paper(self, text: str) -> Dict[str, float]:
"""Score paper quality using SciBERT.
Args:
text: Paper content (full text or abstract+introduction)
Returns:
Dictionary with quality scores (0-10 scale):
- overall_quality
- novelty
- methodology
- clarity
- significance
"""
# Tokenize (handle long papers with chunking)
chunks = self._chunk_text(text, max_length=512)
# Get embeddings for each chunk
chunk_embeddings = []
for chunk in chunks:
inputs = self.tokenizer(
chunk,
return_tensors="pt",
max_length=512,
truncation=True,
padding=True
).to(self.device)
with torch.no_grad():
outputs = self.encoder(**inputs)
# Use [CLS] token embedding
embedding = outputs.last_hidden_state[:, 0, :]
chunk_embeddings.append(embedding)
# Average embeddings across chunks
paper_embedding = torch.mean(torch.stack(chunk_embeddings), dim=0)
# Predict quality scores
with torch.no_grad():
scores = self.quality_head(paper_embedding)
scores = torch.sigmoid(scores) * 10 # Scale to 0-10
# Convert to dictionary
return {
"overall_quality": scores[0, 0].item(),
"novelty": scores[0, 1].item(),
"methodology": scores[0, 2].item(),
"clarity": scores[0, 3].item(),
"significance": scores[0, 4].item()
}
def _chunk_text(self, text: str, max_length: int = 512) -> list[str]:
"""Split long text into chunks that fit SciBERT's context window.
Args:
text: Full paper text
max_length: Maximum tokens per chunk
Returns:
List of text chunks
"""
# Simple sentence-based chunking
sentences = text.split('. ')
chunks = []
current_chunk = []
current_length = 0
for sentence in sentences:
sentence_tokens = len(self.tokenizer.encode(sentence))
if current_length + sentence_tokens > max_length:
if current_chunk:
chunks.append('. '.join(current_chunk) + '.')
current_chunk = [sentence]
current_length = sentence_tokens
else:
current_chunk.append(sentence)
current_length += sentence_tokens
# Add remaining chunk
if current_chunk:
chunks.append('. '.join(current_chunk) + '.')
return chunks if chunks else [text[:max_length]]
def load_weights(self, weights_path: str):
"""Load pre-trained quality head weights.
Args:
weights_path: Path to saved weights (.pt file)
"""
self.quality_head.load_state_dict(torch.load(weights_path))
self.quality_head.eval()
def save_weights(self, weights_path: str):
"""Save quality head weights.
Args:
weights_path: Path to save weights
"""
torch.save(self.quality_head.state_dict(), weights_path)Files Created:
src/services/paper/scibert_scorer.py: SciBERT scorer implementation
Unit Tests:
# tests/services/paper/test_scibert_scorer.py - NEW FILE
import pytest
from src.services.paper.scibert_scorer import SciBERTQualityScorer
@pytest.mark.asyncio
async def test_scibert_scorer_initialization():
"""Test SciBERT scorer can be initialized."""
scorer = SciBERTQualityScorer()
assert scorer.tokenizer is not None
assert scorer.encoder is not None
@pytest.mark.asyncio
async def test_score_paper():
"""Test paper scoring returns expected format."""
scorer = SciBERTQualityScorer()
sample_text = """
This paper presents a novel approach to deep learning.
Our methodology shows significant improvements.
"""
scores = await scorer.score_paper(sample_text)
# Check all expected keys present
assert "overall_quality" in scores
assert "novelty" in scores
assert "methodology" in scores
assert "clarity" in scores
assert "significance" in scores
# Check scores in valid range
for key, value in scores.items():
assert 0 <= value <= 10
@pytest.mark.asyncio
async def test_long_paper_chunking():
"""Test that long papers are properly chunked."""
scorer = SciBERTQualityScorer()
# Create long text (>512 tokens)
long_text = " ".join(["This is a sentence."] * 200)
chunks = scorer._chunk_text(long_text, max_length=512)
# Should be split into multiple chunks
assert len(chunks) > 1
# Each chunk should be under token limit
for chunk in chunks:
tokens = len(scorer.tokenizer.encode(chunk))
assert tokens <= 512Validation:
poetry run pytest tests/services/paper/test_scibert_scorer.py -vEstimated Time: 12 hours Risk: Medium - requires ML engineering expertise
Owner: ML Engineer Priority: P1 (High) Dependencies: Task 1.1
Actions:
# src/services/paper/metrics.py - NEW FILE
"""Paper quality evaluation metrics."""
from typing import Dict, List
from bert_score import score as bertscore
from sklearn.metrics import cohen_kappa_score
import numpy as np
class PaperMetrics:
"""Calculate various quality metrics for papers."""
@staticmethod
async def compute_bertscore(
improved_sections: Dict[str, str],
original_sections: Dict[str, str],
model_type: str = "microsoft/deberta-xlarge-mnli"
) -> Dict[str, Dict[str, float]]:
"""Compare improved vs original sections using BERTScore.
Args:
improved_sections: Dict mapping section names to improved content
original_sections: Dict mapping section names to original content
model_type: BERT model to use for scoring
Returns:
Dict mapping section names to precision, recall, F1 scores
"""
results = {}
for section_name in improved_sections:
if section_name in original_sections:
# Compute BERTScore
P, R, F1 = bertscore(
[improved_sections[section_name]],
[original_sections[section_name]],
lang="en",
model_type=model_type,
verbose=False
)
results[section_name] = {
"precision": round(P.item(), 4),
"recall": round(R.item(), 4),
"f1": round(F1.item(), 4)
}
return results
@staticmethod
def quadratic_weighted_kappa(
human_scores: List[int],
ai_scores: List[int],
min_rating: int = 1,
max_rating: int = 10
) -> float:
"""Calculate Quadratic Weighted Kappa between human and AI scores.
QWK measures agreement between raters, accounting for degree of disagreement.
Args:
human_scores: List of human expert scores
ai_scores: List of AI-generated scores
min_rating: Minimum possible rating
max_rating: Maximum possible rating
Returns:
QWK score between -1 and 1 (1 = perfect agreement)
"""
# Convert to numpy arrays
human = np.array(human_scores)
ai = np.array(ai_scores)
# Calculate QWK
qwk = cohen_kappa_score(
human,
ai,
weights='quadratic',
labels=list(range(min_rating, max_rating + 1))
)
return round(qwk, 4)
@staticmethod
def calculate_correlation(
scores1: List[float],
scores2: List[float],
method: str = "pearson"
) -> float:
"""Calculate correlation between two sets of scores.
Args:
scores1: First set of scores
scores2: Second set of scores
method: "pearson" or "spearman"
Returns:
Correlation coefficient
"""
from scipy.stats import pearsonr, spearmanr
if method == "pearson":
corr, _ = pearsonr(scores1, scores2)
else: # spearman
corr, _ = spearmanr(scores1, scores2)
return round(corr, 4)
@staticmethod
def mean_absolute_error(
true_scores: List[float],
predicted_scores: List[float]
) -> float:
"""Calculate MAE between true and predicted scores."""
return round(np.mean(np.abs(np.array(true_scores) - np.array(predicted_scores))), 4)Files Created:
src/services/paper/metrics.py: Metrics implementation
Unit Tests:
# tests/services/paper/test_metrics.py - NEW FILE
import pytest
from src.services.paper.metrics import PaperMetrics
@pytest.mark.asyncio
async def test_bertscore_computation():
"""Test BERTScore computation."""
improved = {
"introduction": "This novel approach significantly improves performance.",
"methods": "Our methodology is rigorous and well-validated."
}
original = {
"introduction": "This new method improves performance.",
"methods": "The methodology is sound and validated."
}
results = await PaperMetrics.compute_bertscore(improved, original)
assert "introduction" in results
assert "methods" in results
for section, scores in results.items():
assert 0 <= scores["precision"] <= 1
assert 0 <= scores["recall"] <= 1
assert 0 <= scores["f1"] <= 1
def test_quadratic_weighted_kappa():
"""Test QWK calculation."""
human_scores = [8, 7, 9, 6, 8, 7, 9, 8]
ai_scores = [8, 7, 8, 7, 8, 6, 9, 8]
qwk = PaperMetrics.quadratic_weighted_kappa(human_scores, ai_scores)
# QWK should be between -1 and 1
assert -1 <= qwk <= 1
# Should be high correlation for these similar scores
assert qwk > 0.7
def test_perfect_agreement():
"""Test QWK with perfect agreement."""
scores = [8, 7, 9, 6, 8]
qwk = PaperMetrics.quadratic_weighted_kappa(scores, scores)
# Perfect agreement should give QWK = 1.0
assert qwk == 1.0Validation:
poetry run pytest tests/services/paper/test_metrics.py -vEstimated Time: 8 hours Risk: Low - well-defined metrics
Owner: Backend Engineer Priority: P0 (Critical) Dependencies: Tasks 2.1, 2.2
Actions:
# src/services/paper/analyzer.py - MODIFIED
"""Paper quality analysis service with SOTA methods."""
from uuid import UUID
from typing import Dict, Optional
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.orm import selectinload
from src.models.project import Paper
from src.services.llm.service import LLMService
from src.services.paper.scibert_scorer import SciBERTQualityScorer # NEW
from src.services.paper.metrics import PaperMetrics # NEW
class PaperAnalyzer:
"""Analyze paper quality using multiple methods."""
def __init__(self, llm_service: LLMService, db: AsyncSession):
"""Initialize analyzer with multiple scoring methods.
Args:
llm_service: LLM service for GPT-4 analysis
db: Database session
"""
self.llm = llm_service
self.db = db
# NEW: Add SciBERT scorer
self.scibert_scorer = SciBERTQualityScorer()
# NEW: Add metrics calculator
self.metrics = PaperMetrics()
async def analyze_quality(
self,
paper_id: UUID,
use_scibert: bool = True,
use_gpt4: bool = True
) -> Dict:
"""Analyze paper quality using multiple methods.
Args:
paper_id: Paper ID to analyze
use_scibert: Whether to use SciBERT scoring
use_gpt4: Whether to use GPT-4 analysis
Returns:
Comprehensive quality analysis with scores from multiple methods
"""
# Get paper with sections
paper = await self._get_paper_with_sections(paper_id)
results = {
"paper_id": str(paper_id),
"title": paper.title,
"analysis_methods": []
}
# GPT-4 Analysis (original qualitative method)
if use_gpt4:
results["gpt4_analysis"] = await self._gpt4_analysis(paper)
results["analysis_methods"].append("gpt4")
# SciBERT Analysis (NEW quantitative method)
if use_scibert:
results["scibert_scores"] = await self.scibert_scorer.score_paper(
paper.content
)
results["analysis_methods"].append("scibert")
# Ensemble scoring (if both methods used)
if use_scibert and use_gpt4:
results["ensemble_score"] = self._compute_ensemble_score(
results["gpt4_analysis"],
results["scibert_scores"]
)
# Overall quality assessment
results["overall_quality"] = self._compute_overall_quality(results)
return results
async def _gpt4_analysis(self, paper: Paper) -> Dict:
"""Original GPT-4 qualitative analysis (UNCHANGED)."""
# Get sections
sections = sorted(paper.sections, key=lambda s: s.order)
sections_text = "\n\n".join([
f"## {s.name.upper()}\n{s.content}"
for s in sections
])
# Analysis prompt
prompt = f"""
Analyze the quality of this scientific paper. Provide:
1. Overall quality score (1-10)
2. Clarity score (1-10)
3. Key strengths (3-5 bullet points)
4. Key weaknesses (3-5 bullet points)
5. Specific improvement suggestions
Paper Title: {paper.title}
Paper Abstract:
{paper.abstract}
Paper Sections:
{sections_text}
Provide your analysis in JSON format.
"""
# Get GPT-4 analysis
analysis_text = await self.llm.generate(
prompt,
response_format={"type": "json_object"}
)
import json
return json.loads(analysis_text)
def _compute_ensemble_score(
self,
gpt4_analysis: Dict,
scibert_scores: Dict
) -> Dict:
"""Combine GPT-4 and SciBERT scores using weighted ensemble.
Args:
gpt4_analysis: GPT-4 qualitative analysis
scibert_scores: SciBERT quantitative scores
Returns:
Ensemble scores combining both methods
"""
# Weight: 40% GPT-4, 60% SciBERT (SciBERT more reliable)
gpt4_weight = 0.4
scibert_weight = 0.6
# Get GPT-4 overall score
gpt4_overall = gpt4_analysis.get("overall_quality_score", 7.0)
# Get SciBERT overall score
scibert_overall = scibert_scores.get("overall_quality", 7.0)
# Compute weighted ensemble
ensemble_overall = (
gpt4_weight * gpt4_overall +
scibert_weight * scibert_overall
)
return {
"overall_quality": round(ensemble_overall, 2),
"gpt4_contribution": round(gpt4_weight * gpt4_overall, 2),
"scibert_contribution": round(scibert_weight * scibert_overall, 2),
"weights": {
"gpt4": gpt4_weight,
"scibert": scibert_weight
}
}
def _compute_overall_quality(self, results: Dict) -> Dict:
"""Compute final overall quality assessment.
Args:
results: Analysis results from all methods
Returns:
Overall quality summary
"""
if "ensemble_score" in results:
# Use ensemble if available
overall_score = results["ensemble_score"]["overall_quality"]
confidence = "high"
elif "scibert_scores" in results:
# Use SciBERT if available
overall_score = results["scibert_scores"]["overall_quality"]
confidence = "medium"
elif "gpt4_analysis" in results:
# Fall back to GPT-4
overall_score = results["gpt4_analysis"].get("overall_quality_score", 7.0)
confidence = "medium"
else:
overall_score = 5.0
confidence = "low"
return {
"score": round(overall_score, 2),
"confidence": confidence,
"rating": self._get_rating_label(overall_score)
}
@staticmethod
def _get_rating_label(score: float) -> str:
"""Convert numeric score to rating label."""
if score >= 9.0:
return "Excellent"
elif score >= 7.5:
return "Good"
elif score >= 6.0:
return "Acceptable"
elif score >= 4.0:
return "Needs Improvement"
else:
return "Poor"
async def _get_paper_with_sections(self, paper_id: UUID) -> Paper:
"""Get paper with sections loaded (UNCHANGED)."""
query = select(Paper).where(Paper.id == paper_id)
query = query.options(selectinload(Paper.sections))
result = await self.db.execute(query)
paper = result.scalar_one_or_none()
if not paper:
raise ValueError(f"Paper {paper_id} not found")
return paper
# ... (keep other existing methods: check_section_coherence, identify_gaps)Files Modified:
src/services/paper/analyzer.py: Enhanced with SciBERT integration
Migration Notes:
- Backward Compatible: GPT-4 analysis still works if
use_gpt4=True, use_scibert=False - New Default: Both methods enabled by default for comprehensive analysis
- API Changes: Return format expanded to include multiple scoring methods
Validation:
# Test backward compatibility
async def test_backward_compatibility():
analyzer = PaperAnalyzer(llm_service, db)
# Old behavior (GPT-4 only)
result_old = await analyzer.analyze_quality(
paper_id,
use_scibert=False,
use_gpt4=True
)
assert "gpt4_analysis" in result_old
assert "scibert_scores" not in result_old
# New behavior (both methods)
result_new = await analyzer.analyze_quality(
paper_id,
use_scibert=True,
use_gpt4=True
)
assert "gpt4_analysis" in result_new
assert "scibert_scores" in result_new
assert "ensemble_score" in result_newEstimated Time: 10 hours Risk: Medium - requires careful integration to maintain backward compatibility
Owner: Backend Engineer Priority: P1 (High) Dependencies: Task 3.1
Actions:
# src/api/v1/papers.py - MODIFIED
@router.post("/{paper_id}/analyze", response_model=PaperAnalysisResponse)
async def analyze_paper(
paper_id: UUID,
request: PaperAnalyzeRequest = PaperAnalyzeRequest(),
use_scibert: bool = True, # NEW parameter
use_gpt4: bool = True, # NEW parameter
db: AsyncSession = Depends(get_db),
llm_service: LLMService = Depends(get_llm_service)
):
"""Analyze paper quality using multiple SOTA methods.
Args:
paper_id: Paper ID to analyze
request: Analysis configuration
use_scibert: Enable SciBERT quantitative scoring (default: True)
use_gpt4: Enable GPT-4 qualitative analysis (default: True)
Returns:
Comprehensive analysis with scores from enabled methods
Example Response:
{
"paper_id": "123e4567-e89b-12d3-a456-426614174000",
"title": "Sample Paper",
"analysis_methods": ["gpt4", "scibert"],
"gpt4_analysis": {
"overall_quality_score": 8.5,
"clarity_score": 7.5,
...
},
"scibert_scores": {
"overall_quality": 8.2,
"novelty": 7.8,
"methodology": 8.5,
"clarity": 7.9,
"significance": 8.0
},
"ensemble_score": {
"overall_quality": 8.32,
"weights": {"gpt4": 0.4, "scibert": 0.6}
},
"overall_quality": {
"score": 8.32,
"confidence": "high",
"rating": "Good"
}
}
"""
analyzer = PaperAnalyzer(llm_service, db)
analysis = await analyzer.analyze_quality(
paper_id,
use_scibert=use_scibert,
use_gpt4=use_gpt4
)
return PaperAnalysisResponse(**analysis)Files Modified:
src/api/v1/papers.py: Updated analyze endpoint
API Documentation Updates:
- Add new query parameters to OpenAPI spec
- Update response schema to include SciBERT scores
- Add examples showing ensemble scoring
Estimated Time: 4 hours Risk: Low - simple parameter addition
Owner: ML Engineer + Research Scientist Priority: P0 (Critical) Dependencies: Tasks 1.3, 3.1
Actions:
# scripts/validate_sota_methods.py - NEW FILE
"""Validation script for SOTA methods."""
import asyncio
import json
from pathlib import Path
from typing import Dict, List
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
from sqlalchemy.orm import sessionmaker
from src.core.config import settings
from src.services.llm.service import LLMService
from src.services.paper.analyzer import PaperAnalyzer
from src.services.paper.metrics import PaperMetrics
async def load_validation_dataset() -> List[Dict]:
"""Load validation dataset with human scores."""
dataset_path = Path("data/validation/validation_dataset_v1.json")
with open(dataset_path) as f:
data = json.load(f)
return data["papers"]
async def run_validation():
"""Run validation experiments comparing all methods."""
print("=" * 80)
print("SOTA METHODS VALIDATION EXPERIMENT")
print("=" * 80)
# Initialize services
engine = create_async_engine(settings.database_url, echo=False)
async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
llm_service = LLMService()
# Load validation data
print("\n📂 Loading validation dataset...")
validation_papers = await load_validation_dataset()
print(f"✅ Loaded {len(validation_papers)} papers")
# Storage for results
results = {
"gpt4_only": [],
"scibert_only": [],
"ensemble": []
}
human_scores = []
# Run experiments
async with async_session() as db:
analyzer = PaperAnalyzer(llm_service, db)
for i, paper_data in enumerate(validation_papers, 1):
print(f"\n📄 [{i}/{len(validation_papers)}] Analyzing: {paper_data['title'][:50]}...")
# Store human score
human_scores.append(paper_data["human_scores"]["overall"])
# Method 1: GPT-4 only
analysis_gpt4 = await analyzer.analyze_quality(
paper_id=paper_data["id"],
use_scibert=False,
use_gpt4=True
)
results["gpt4_only"].append(
analysis_gpt4["gpt4_analysis"]["overall_quality_score"]
)
# Method 2: SciBERT only
analysis_scibert = await analyzer.analyze_quality(
paper_id=paper_data["id"],
use_scibert=True,
use_gpt4=False
)
results["scibert_only"].append(
analysis_scibert["scibert_scores"]["overall_quality"]
)
# Method 3: Ensemble
analysis_ensemble = await analyzer.analyze_quality(
paper_id=paper_data["id"],
use_scibert=True,
use_gpt4=True
)
results["ensemble"].append(
analysis_ensemble["ensemble_score"]["overall_quality"]
)
# Calculate metrics
print("\n" + "=" * 80)
print("VALIDATION RESULTS")
print("=" * 80)
metrics = PaperMetrics()
for method_name, scores in results.items():
print(f"\n📊 {method_name.upper()}:")
# QWK (primary metric)
qwk = metrics.quadratic_weighted_kappa(human_scores, scores)
print(f" QWK (vs Human): {qwk:.4f}")
# Pearson correlation
corr = metrics.calculate_correlation(human_scores, scores, method="pearson")
print(f" Pearson Correlation: {corr:.4f}")
# MAE
mae = metrics.mean_absolute_error(human_scores, scores)
print(f" Mean Absolute Error: {mae:.4f}")
# Save results
output_path = Path("data/validation/validation_results_phase1.json")
output_data = {
"human_scores": human_scores,
"ai_scores": results,
"metrics": {
method: {
"qwk": metrics.quadratic_weighted_kappa(human_scores, scores),
"pearson": metrics.calculate_correlation(human_scores, scores),
"mae": metrics.mean_absolute_error(human_scores, scores)
}
for method, scores in results.items()
},
"validation_date": "2025-10-05",
"num_papers": len(validation_papers)
}
with open(output_path, "w") as f:
json.dump(output_data, f, indent=2)
print(f"\n✅ Results saved to: {output_path}")
# Print summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
best_method = max(
output_data["metrics"].items(),
key=lambda x: x[1]["qwk"]
)
print(f"\n🏆 Best Method: {best_method[0]}")
print(f" QWK: {best_method[1]['qwk']:.4f}")
print(f" Target: 0.75+ (Phase 1 goal)")
if best_method[1]["qwk"] >= 0.75:
print("\n✅ PHASE 1 GOAL ACHIEVED!")
else:
print(f"\n⚠️ Need {0.75 - best_method[1]['qwk']:.4f} more QWK to reach goal")
if __name__ == "__main__":
asyncio.run(run_validation())Expected Outputs:
VALIDATION RESULTS
================================================================================
📊 GPT4_ONLY:
QWK (vs Human): 0.6852
Pearson Correlation: 0.7234
Mean Absolute Error: 0.9200
📊 SCIBERT_ONLY:
QWK (vs Human): 0.7412
Pearson Correlation: 0.7801
Mean Absolute Error: 0.7800
📊 ENSEMBLE:
QWK (vs Human): 0.7689
Pearson Correlation: 0.8102
Mean Absolute Error: 0.6900
🏆 Best Method: ensemble
QWK: 0.7689
Target: 0.75+ (Phase 1 goal)
✅ PHASE 1 GOAL ACHIEVED!
Files Created:
scripts/validate_sota_methods.py: Validation scriptdata/validation/validation_results_phase1.json: Results
Success Criteria:
- QWK ≥ 0.75 for at least one method
- Ensemble performs better than GPT-4 alone
- SciBERT shows improvement over baseline
Estimated Time: 16 hours (8 hours running experiments + 8 hours analysis) Risk: Medium - depends on validation dataset quality
Owner: Research Scientist Priority: P1 (High) Dependencies: Task 4.1
Actions: Create comprehensive Phase 1 completion report:
# Phase 1 Completion Report
## Summary
- ✅ All dependencies installed
- ✅ SciBERT scorer implemented
- ✅ BERTScore metrics integrated
- ✅ PaperAnalyzer enhanced
- ✅ Validation completed
## Performance Results
| Method | QWK | Correlation | MAE | Status |
|--------|-----|-------------|-----|--------|
| GPT-4 Only | 0.685 | 0.723 | 0.92 | Baseline |
| SciBERT Only | 0.741 | 0.780 | 0.78 | +8% vs baseline |
| Ensemble | **0.769** | 0.810 | 0.69 | +12% vs baseline ✅ |
## Achievements
1. **QWK Target Met**: 0.769 > 0.75 goal
2. **Integration Complete**: Backward compatible
3. **Validation Framework**: Reusable for future phases
## Next Steps for Phase 2
1. Implement linguistic feature extractor
2. Build hybrid model architecture
3. Train on domain-specific dataFiles Created:
claudedocs/PHASE1_COMPLETION_REPORT.md: Phase 1 report
Estimated Time: 4 hours Risk: Low - documentation task
Goal: Implement hybrid model combining transformer embeddings with linguistic features
Owner: NLP Engineer Priority: P0 (Critical) Dependencies: Phase 1 complete
Actions:
# src/services/paper/linguistic_features.py - NEW FILE
"""Extract handcrafted linguistic features for academic papers."""
from typing import Dict, List
import torch
import numpy as np
import spacy
import nltk
from textstat import textstat
from collections import Counter
class LinguisticFeatureExtractor:
"""Extract linguistic features from academic papers."""
def __init__(self):
"""Initialize feature extractor with NLP tools."""
# Load spaCy model
self.nlp = spacy.load("en_core_web_sm")
# Academic vocabulary (top 1000 academic words)
self.academic_words = self._load_academic_wordlist()
# Technical term patterns
self.tech_patterns = self._load_technical_patterns()
def extract(self, text: str) -> torch.Tensor:
"""Extract all linguistic features from text.
Args:
text: Paper content
Returns:
Tensor of 20 linguistic features
"""
features = []
# Process with spaCy
doc = self.nlp(text[:1000000]) # Limit for performance
# Category 1: Readability (4 features)
features.append(self._flesch_reading_ease(text))
features.append(self._flesch_kincaid_grade(text))
features.append(self._gunning_fog(text))
features.append(self._smog_index(text))
# Category 2: Vocabulary Richness (4 features)
features.append(self._type_token_ratio(doc))
features.append(self._unique_word_ratio(doc))
features.append(self._academic_word_ratio(doc))
features.append(self._lexical_diversity(doc))
# Category 3: Syntactic Complexity (4 features)
features.append(self._avg_sentence_length(doc))
features.append(self._avg_word_length(doc))
features.append(self._sentence_complexity(doc))
features.append(self._dependency_depth(doc))
# Category 4: Academic Writing Indicators (4 features)
features.append(self._citation_density(text))
features.append(self._technical_term_ratio(doc))
features.append(self._passive_voice_ratio(doc))
features.append(self._nominalization_ratio(doc))
# Category 5: Discourse & Coherence (4 features)
features.append(self._discourse_markers_ratio(doc))
features.append(self._topic_consistency(doc))
features.append(self._entity_coherence(doc))
features.append(self._pronoun_ratio(doc))
return torch.tensor(features, dtype=torch.float32)
# Readability Features
def _flesch_reading_ease(self, text: str) -> float:
"""Flesch Reading Ease score (normalized 0-1)."""
return textstat.flesch_reading_ease(text) / 100.0
def _flesch_kincaid_grade(self, text: str) -> float:
"""Flesch-Kincaid Grade Level (normalized 0-1)."""
return min(textstat.flesch_kincaid_grade(text) / 20.0, 1.0)
def _gunning_fog(self, text: str) -> float:
"""Gunning Fog Index (normalized 0-1)."""
return min(textstat.gunning_fog(text) / 20.0, 1.0)
def _smog_index(self, text: str) -> float:
"""SMOG Index (normalized 0-1)."""
return min(textstat.smog_index(text) / 20.0, 1.0)
# Vocabulary Richness Features
def _type_token_ratio(self, doc) -> float:
"""Type-Token Ratio (unique words / total words)."""
words = [token.text.lower() for token in doc if token.is_alpha]
if not words:
return 0.0
return len(set(words)) / len(words)
def _unique_word_ratio(self, doc) -> float:
"""Ratio of words used only once."""
words = [token.text.lower() for token in doc if token.is_alpha]
if not words:
return 0.0
word_counts = Counter(words)
unique = sum(1 for count in word_counts.values() if count == 1)
return unique / len(words)
def _academic_word_ratio(self, doc) -> float:
"""Ratio of academic vocabulary words."""
words = [token.text.lower() for token in doc if token.is_alpha]
if not words:
return 0.0
academic = sum(1 for word in words if word in self.academic_words)
return academic / len(words)
def _lexical_diversity(self, doc) -> float:
"""Lexical diversity using MTLD metric."""
words = [token.text.lower() for token in doc if token.is_alpha]
if len(words) < 50:
return 0.0
# Simplified MTLD calculation
return textstat.text_standard(doc.text, float_output=True) / 20.0
# Syntactic Complexity Features
def _avg_sentence_length(self, doc) -> float:
"""Average sentence length in words (normalized)."""
sentences = list(doc.sents)
if not sentences:
return 0.0
avg_len = np.mean([len(sent) for sent in sentences])
return min(avg_len / 50.0, 1.0) # Normalize by max expected
def _avg_word_length(self, doc) -> float:
"""Average word length in characters (normalized)."""
words = [token.text for token in doc if token.is_alpha]
if not words:
return 0.0
avg_len = np.mean([len(word) for word in words])
return min(avg_len / 15.0, 1.0)
def _sentence_complexity(self, doc) -> float:
"""Sentence complexity based on clause count."""
sentences = list(doc.sents)
if not sentences:
return 0.0
# Count subordinate clauses
clause_markers = ["if", "when", "while", "because", "although", "that", "which"]
total_clauses = 0
for sent in sentences:
clause_count = sum(1 for token in sent if token.text.lower() in clause_markers)
total_clauses += clause_count
return min(total_clauses / len(sentences) / 3.0, 1.0)
def _dependency_depth(self, doc) -> float:
"""Average dependency tree depth (normalized)."""
def get_depth(token):
depth = 0
while token.head != token:
depth += 1
token = token.head
return depth
depths = [get_depth(token) for token in doc]
if not depths:
return 0.0
avg_depth = np.mean(depths)
return min(avg_depth / 10.0, 1.0)
# Academic Writing Indicators
def _citation_density(self, text: str) -> float:
"""Density of citations in text."""
# Simple heuristic: count patterns like (Author, Year) or [1]
import re
citations = len(re.findall(r'\([A-Z][a-z]+,?\s+\d{4}\)|\[\d+\]', text))
words = len(text.split())
if words == 0:
return 0.0
return min(citations / words * 100, 1.0)
def _technical_term_ratio(self, doc) -> float:
"""Ratio of technical/domain-specific terms."""
words = [token.text.lower() for token in doc if token.is_alpha]
if not words:
return 0.0
# Match against technical patterns
technical = sum(1 for word in words if any(pat in word for pat in self.tech_patterns))
return technical / len(words)
def _passive_voice_ratio(self, doc) -> float:
"""Ratio of passive voice constructions."""
passive_count = 0
total_verbs = 0
for token in doc:
if token.pos_ == "VERB":
total_verbs += 1
# Passive: auxiliary + past participle
if token.dep_ == "auxpass" or (token.dep_ == "nsubjpass"):
passive_count += 1
return passive_count / total_verbs if total_verbs > 0 else 0.0
def _nominalization_ratio(self, doc) -> float:
"""Ratio of nominalized forms (nouns from verbs/adjectives)."""
# Common nominalization suffixes
nom_suffixes = ["tion", "sion", "ment", "ness", "ity", "ance", "ence"]
nouns = [token.text.lower() for token in doc if token.pos_ == "NOUN"]
if not nouns:
return 0.0
nominalizations = sum(
1 for noun in nouns
if any(noun.endswith(suffix) for suffix in nom_suffixes)
)
return nominalizations / len(nouns)
# Discourse & Coherence Features
def _discourse_markers_ratio(self, doc) -> float:
"""Ratio of discourse markers (connectives)."""
markers = [
"however", "therefore", "furthermore", "moreover", "nevertheless",
"consequently", "thus", "hence", "additionally", "similarly"
]
words = [token.text.lower() for token in doc]
if not words:
return 0.0
marker_count = sum(1 for word in words if word in markers)
return marker_count / len(words) * 100
def _topic_consistency(self, doc) -> float:
"""Topic consistency using noun overlap between sentences."""
sentences = list(doc.sents)
if len(sentences) < 2:
return 1.0
# Get nouns for each sentence
sent_nouns = [
set(token.lemma_.lower() for token in sent if token.pos_ == "NOUN")
for sent in sentences
]
# Calculate overlap between consecutive sentences
overlaps = []
for i in range(len(sent_nouns) - 1):
if not sent_nouns[i] or not sent_nouns[i+1]:
continue
overlap = len(sent_nouns[i] & sent_nouns[i+1])
total = len(sent_nouns[i] | sent_nouns[i+1])
overlaps.append(overlap / total if total > 0 else 0)
return np.mean(overlaps) if overlaps else 0.0
def _entity_coherence(self, doc) -> float:
"""Entity coherence using named entity overlap."""
sentences = list(doc.sents)
if len(sentences) < 2:
return 1.0
# Get entities for each sentence
sent_entities = [
set(ent.text.lower() for ent in sent.ents)
for sent in sentences
]
# Calculate entity chain consistency
overlaps = []
for i in range(len(sent_entities) - 1):
if not sent_entities[i] or not sent_entities[i+1]:
continue
overlap = len(sent_entities[i] & sent_entities[i+1])
total = len(sent_entities[i] | sent_entities[i+1])
overlaps.append(overlap / total if total > 0 else 0)
return np.mean(overlaps) if overlaps else 0.0
def _pronoun_ratio(self, doc) -> float:
"""Ratio of pronouns (indicator of coherence)."""
words = [token for token in doc if token.is_alpha]
if not words:
return 0.0
pronouns = sum(1 for token in words if token.pos_ == "PRON")
return pronouns / len(words)
# Helper methods
def _load_academic_wordlist(self) -> set:
"""Load academic word list."""
# Simplified AWL (Academic Word List)
# In production, load from file
return {
"analyze", "approach", "area", "assess", "assume", "authority",
"available", "benefit", "concept", "consist", "context", "contract",
# ... (top 1000 academic words)
}
def _load_technical_patterns(self) -> List[str]:
"""Load technical term patterns."""
return [
"ology", "metric", "synthesis", "neural", "algorithm", "coefficient",
"variance", "hypothesis", "methodology", "empirical", "quantitative"
]Estimated Time: 20 hours Risk: Medium - complex NLP engineering
Owner: ML Engineer Priority: P0 (Critical) Dependencies: Task 5.1
Implementation: See research document section 6.2.A for full code
Key Components:
- RoBERTa embeddings (768-dim)
- Linguistic features (20-dim)
- Fusion network (768+20 → 512 → 256 → 10)
- Training pipeline
Estimated Time: 24 hours Risk: High - requires ML expertise and GPU resources
Owner: Backend Engineer Priority: P0 (Critical)
Actions: Add hybrid model to PaperAnalyzer alongside SciBERT
Estimated Time: 12 hours Risk: Medium
Goal: Multi-task learning, automated review generation, active learning
Owner: ML Engineer Priority: P1 (High)
Implementation: See research document section 6.2.B
Estimated Time: 28 hours Risk: High - complex architecture
Owner: ML Engineer + Research Scientist Priority: P1 (High)
Implementation: See research document section 6.2.C
Estimated Time: 24 hours Risk: Medium
Goal: Optimize, document, deploy to production
- Quantization (FP32 → FP16/INT8)
- Model pruning
- TorchScript compilation
Estimated Time: 20 hours
- Response caching
- Async batch processing
- Load testing
Estimated Time: 16 hours
- API documentation
- User guides
- Training materials
- Deployment guides
Estimated Time: 16 hours
- Deploy to production environment
- Set up monitoring dashboards
- Configure alerts
- Run smoke tests
Estimated Time: 12 hours
- Production A/B test (1 week)
- User acceptance testing
- Performance monitoring
- Bug fixes
Estimated Time: 20 hours
- ✅ QWK ≥ 0.75 vs human scores
- ✅ SciBERT integration working
- ✅ Backward compatibility maintained
- ✅ QWK ≥ 0.85 with hybrid model
- ✅ 20 linguistic features extracted
- ✅ Hybrid model trained
- ✅ Multi-task model operational
- ✅ Automated review generator working
- ✅ Active learning pipeline established
- ✅ Production deployment complete
- ✅ QWK ≥ 0.90 in production
- ✅ 10x inference speed improvement
- ✅ 50x cost reduction achieved
- Review QWK scores against human experts
- Collect user feedback
- Identify edge cases
- Plan improvements
- Fine-tune models on new data
- Update validation datasets
- Benchmark against new SOTA methods
- Technology refresh
- ML Engineer (40% time): Model implementation, training
- Backend Engineer (30% time): API integration, deployment
- Research Scientist (20% time): Validation, research
- NLP Engineer (20% time): Linguistic features, text processing
- DevOps Engineer (10% time): Infrastructure, deployment
- Training: 1x NVIDIA A100 GPU (40GB VRAM)
- Inference: CPU-based (optimized models)
- Storage: 100GB for models and data
- Compute Costs: $2,000 (4 months GPU rental)
- API Costs: $500 (GPT-4 for validation)
- Expert Time: $10,000 (50 papers × $200/paper for human scoring)
- Total: ~$12,500
- Validation dataset quality: Mitigation: Use multiple experts per paper
- Hybrid model training: Mitigation: Start with pretrained weights
- GPU availability: Mitigation: Reserve resources early
- Backward compatibility: Mitigation: Comprehensive testing
- Integration complexity: Mitigation: Incremental rollout
- Performance degradation: Mitigation: A/B testing
- Dependency installation: Mitigation: Docker containers
- API changes: Mitigation: Versioned endpoints
- SciBERT scorer module
- BERTScore metrics module
- Enhanced PaperAnalyzer
- Validation framework
- Phase 1 report
- Linguistic feature extractor
- Hybrid model implementation
- Training pipeline
- Phase 2 validation report
- Multi-task learning model
- Automated review generator
- Active learning pipeline
- Phase 3 validation report
- Optimized production models
- Complete documentation
- Deployment automation
- Monitoring dashboards
- Final project report
Workflow Generated: 2025-10-05 Next Update: Weekly progress reports Contact: [Project Lead]