Nonsensical CoT Reasoning
Published Analyzed Source: arXiv
Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…
Robust Answers, Fragile Logic: Probing the Decoupling Hypothesis in LLM Reasoning
Evaluated models: Llama 3 8B, Mistral 7B, Zephyr 7B Beta+4 more
- Llama 3 8B
- Mistral 7B
- Zephyr 7B Beta
- Qwen 2.5 7B
- DeepSeek R1 Distill Qwen 7B
- GPT-4o
- GPT-3.5 Turbo