Summary of Investigating Context Effects in Similarity Judgements in Large Language Models, by Sagar Uprety et al.
Investigating Context Effects in Similarity Judgements in Large Language Models
by Sagar Uprety, Amit Kumar Jaiswal, Haiming Liu, Dawei Song
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Large Language Models (LLMs) have transformed AI’s ability to understand and generate natural language text. As they are increasingly used in real-world scenarios, there is a growing need to ensure that their decisions align with human values and user expectations. However, measuring human values and decisions can be challenging due to cognitive biases. This ongoing investigation examines the alignment of LLMs with human judgements affected by order bias. By replicating a famous human study on similarity judgments with various popular LLMs, we demonstrate how these models exhibit human-like order effect bias in different settings. The implications of this research inform the design and development of LLM-based applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and create language like humans do. As they’re used more in real life, we need to make sure their decisions match what humans want and expect. It’s tricky because humans don’t always think straight due to biases. This study is looking into how these computer models work when it comes to order bias. We took a famous human study that showed order affects how people judge things, and repeated it with popular computer models. Our results show how these computers can behave like humans in certain situations. Understanding this helps us create better computer programs. |
Keywords
» Artificial intelligence » Alignment
