Summary of Adaptive Activation Steering: a Tuning-free Llm Truthfulness Improvement Method For Diverse Hallucinations Categories, by Tianlong Wang et al.
Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories
by Tianlong Wang, Xianfeng Jiao, Yinghao Zhu, Zhongzhi Chen, Yifan He, Xu Chu, Junyi Gao, Yasha Wang, Liantao Ma
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent studies have shown that Large Language Models (LLMs) possess an innate understanding of truthfulness, but often struggle to consistently express it and generate false statements. To bridge this “knowing” vs. “telling” gap, we propose Adaptive Activation Steering (ACT), a tuning-free method that adjusts LLM’s activations in the direction of truthfulness during inference. ACT utilizes diverse truthfulness-related steering vectors and adjusts the steering intensity adaptively to address various categories of hallucinations. We demonstrate ACT’s effectiveness across multiple models, including LLaMA, LLaMA2, Alpaca, Vicuna, LLaMA2-Chat, and LLaMA3, achieving significant improvements in truthfulness (up to 142%). Additionally, we verify ACT’s scalability across larger models (13B, 33B, 65B), highlighting its adaptability to large-scale language models. This research aims to ensure the truthfulness of generated content by fine-tuning LLMs for truthful expression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores how Large Language Models (LLMs) can generate more accurate and trustworthy content. Right now, these models are good at understanding what’s true or false, but they often don’t express themselves honestly. To fix this problem, the researchers developed a new method called Adaptive Activation Steering (ACT). ACT helps LLMs to adjust their “thought process” so that they can generate more truthful statements. The team tested ACT on several different models and found that it made them significantly better at telling the truth. They also showed that ACT works well even with very large language models, which is important for creating trustworthy AI systems. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Llama
