BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models

arXiv:2408.10608 · cs.CL, cs.AI · Submitted 2024-08-20 · Read on arXiv

cs.CL, cs.AI

Submitted: 2024-08-20

Updated: 2026-09-09

Comments: 18 pages, including appendices. A version of this work has been accepted for publication in IEEE Transactions on Fuzzy Systems (TFS)

Code: https://github.com/open-compass/opencompass

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 7× in this paper · explained in 1 episode
• Entanglement — 6× in this paper · explained in 4 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Benchmark — 5× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Alignment Theory — 2× in this paper · explained in 1 episode
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Machine Unlearning — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Bayesian-Theory-based Bias Removal — 1× in this paper
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Entanglement Score — 1× in this paper
• Generative AI — 1× in this paper · explained in 2 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Root Mean Square Error — 1× in this paper
• Unified Framework — 1× in this paper · explained in 5 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series

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