Summary of How Do Large Language Models Navigate Conflicts Between Honesty and Helpfulness?, by Ryan Liu et al.
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
by Ryan Liu, Theodore R. Sumers, Ishita Dasgupta, Thomas L. Griffiths
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 LLMs’ ability to approximate truth in communication is crucial for effective dialogue. This paper investigates how large language models handle nuanced trade-offs between honesty and helpfulness in daily conversation. By analyzing LLMs through psychological models and experiments designed to characterize human behavior, researchers test various models and explore the impact of optimization techniques on these trade-offs. The findings suggest that reinforcement learning from human feedback improves both honesty and helpfulness, while chain-of-thought prompting favors helpfulness over honesty. Furthermore, GPT-4 Turbo demonstrates human-like response patterns, including sensitivity to conversational framing and listener decision context. These insights reveal the internalized values of LLMs in conversation and propose that zero-shot prompting can steer these abstract values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can talk like humans. This paper is about how they handle tricky situations where they have to choose between telling the truth and being helpful. Imagine you’re having a chat with someone and you want to be honest, but you also want to make sure your friend feels good. LLMs have to make these kinds of decisions too! The researchers tested different ways that LLMs can learn from people and found that when they get feedback from humans, they do a better job being honest and helpful. They also learned that if you ask the LLM to think like it’s having a conversation with someone, it will be more helpful than truthful. This is cool because it shows that even these super smart computers can understand how we want to communicate in different situations. |
Keywords
* Artificial intelligence * Gpt * Optimization * Prompting * Reinforcement learning from human feedback * Zero shot
