Summary of Strong and Weak Alignment Of Large Language Models with Human Values, by Mehdi Khamassi et al.
Strong and weak alignment of large language models with human values
by Mehdi Khamassi, Marceau Nahon, Raja Chatila
First submitted to arxiv on: 5 Aug 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 tackles the critical issue of ensuring Artificial Intelligence (AI) systems align with human values without human supervision. Current approaches focus on improving methods using reinforcement learning and human feedback, but neglect the fundamental requirements for alignment. The authors propose distinguishing between strong and weak value alignment. Strong alignment demands cognitive abilities, such as understanding intentions and causal effects, necessary for AI systems like large language models (LLMs) to recognize situations threatening human values. To illustrate this distinction, the paper presents prompts showing ChatGPT’s, Gemini’s, and Copilot’s failures to recognize these situations. Analysis of word embeddings reveals that LLMs’ semantic representations differ from humans’. The authors also propose a thought experiment, “the Chinese room with a word transition dictionary”, extending John Searle’s famous proposal. Finally, the paper highlights promising research directions towards weak alignment, which could produce statistically satisfying answers in common situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how to make sure Artificial Intelligence (AI) systems work with human values without humans controlling them. Right now, people are trying to improve AI by teaching it through rewards and feedback, but they’re missing the point. The authors think that AI needs to be able to understand what’s going on and why things happen in order to work with human values. They give examples of AI systems failing to recognize when something might go wrong. They also look at how AI represents words and find that it’s different from how humans do it. To fix this, the authors propose a new way of thinking about AI and its relationship with human values. This paper is important because we need to make sure AI works for everyone. |
Keywords
» Artificial intelligence » Alignment » Gemini » Reinforcement learning
