Debias It Yourself: Teaching LLMs Cognitive Bias Mitigation Interventions

arXiv:2609.40124 · cs.CL · Submitted 2026-09-30 · Read on arXiv

cs.CL

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/chahatraj/DebiasItYourself

Terminology

• Benchmark — 64× in this paper · explained in 2 episodes
• Accuracy — 28× in this paper · explained in 1 episode
• Generalization — 12× in this paper · explained in 13 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Instruction Tuning — 6× in this paper · explained in 2 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Generalizability — 4× in this paper · explained in 6 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Steering — 2× in this paper · explained in 2 episodes
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Activation Steering — 1× in this paper · explained in 4 episodes
• Active Learning — 1× in this paper · explained in 6 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• BBQ Ambiguous — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Constitutional AI: Harmlessness from AI Feedback
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Disclosure and Mitigation of Gender Bias in LLMs
• The Capacity for Moral Self-Correction in Large Language Models
• LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models

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