Summary of Bias in the Mirror: Are Llms Opinions Robust to Their Own Adversarial Attacks ?, by Virgile Rennard et al.
Bias in the Mirror: Are LLMs opinions robust to their own adversarial attacks ?
by Virgile Rennard, Christos Xypolopoulos, Michalis Vazirgiannis
First submitted to arxiv on: 17 Oct 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 This paper explores the robustness of biases in large language models (LLMs) during interactions. The authors introduce a novel approach where two instances of an LLM engage in self-debate, arguing opposing viewpoints to persuade a neutral version of the model. This allows them to evaluate how firmly biases hold and whether models are susceptible to reinforcing misinformation or shifting to harmful viewpoints. The experiments span multiple LLMs of varying sizes, origins, and languages, providing deeper insights into bias persistence and flexibility across linguistic and cultural contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models can be biased in the way they talk and respond. The researchers want to know if these biases stay strong or change over time when different parts of the model have conversations with each other. They use a new approach where two versions of the model argue opposite points of view to try to convince a third version. By doing this, they can see how hard it is for the biases to hold and whether the models can be tricked into saying something bad or spreading misinformation. |
