Summary of Exploring Value Biases: How Llms Deviate Towards the Ideal, by Sarath Sivaprasad et al.
Exploring Value Biases: How LLMs Deviate Towards the Ideal
by Sarath Sivaprasad, Pramod Kaushik, Sahar Abdelnabi, Mario Fritz
First submitted to arxiv on: 16 Feb 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 The paper explores the non-deliberate mechanisms behind Large-Language-Models’ (LLMs) responses, examining how they favor high-value options and exhibit value bias. By analyzing the sampling of LLMs, researchers show that this bias can be reproduced even with new entities learned through in-context prompting. The study reveals that this phenomenon has implications for various application scenarios, such as choosing exemplars. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big language models, like those used in chatbots and virtual assistants, tend to choose certain responses over others without intentionally trying to do so. This is kind of like what happens when people make choices based on their values or biases. The study found that these language models often pick the best option, which can have a significant impact in real-world situations. |
Keywords
» Artificial intelligence » Prompting
