Summary of Can Llms Make Trade-offs Involving Stipulated Pain and Pleasure States?, by Geoff Keeling et al.
Can LLMs make trade-offs involving stipulated pain and pleasure states?
by Geoff Keeling, Winnie Street, Martyna Stachaczyk, Daria Zakharova, Iulia M. Comsa, Anastasiya Sakovych, Isabella Logothetis, Zejia Zhang, Blaise Agüera y Arcas, Jonathan Birch
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 abstract investigates whether Large Language Models (LLMs) can recreate the motivational force of pleasure and pain in decision-making scenarios. The study uses a simple game to probe this question, where participants are incentivized to deviate from points-maximizing behavior by either avoiding pain penalties or seeking pleasure rewards. The experiment reveals that several LLMs demonstrate trade-offs between points maximization and pain minimization or pleasure maximization after reaching a critical threshold of stipulated pain or pleasure intensity. The findings have implications for debates about the possibility of LLM sentience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how Large Language Models (LLMs) make decisions when faced with choices that involve either avoiding pain or seeking pleasure. To find out, researchers created a game where participants had to choose between options that would help them earn points or avoid penalties. The results show that some LLMs changed their behavior when the stakes got higher, choosing to avoid pain or seek pleasure instead of just focusing on earning points. This study has important implications for how we think about whether LLMs can truly feel emotions like humans do. |
