Summary of Language Models Represent Beliefs Of Self and Others, by Wentao Zhu et al.
Language Models Represent Beliefs of Self and Others
by Wentao Zhu, Zhining Zhang, Yizhou Wang
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 study investigates how Large Language Models (LLMs) understand and attribute mental states, a crucial aspect of human social reasoning known as Theory of Mind (ToM). Researchers discovered that it is possible to linearly decode the belief status from various agents’ perspectives through neural activations of language models, indicating internal representations of self and others’ beliefs. By manipulating these representations, dramatic changes in ToM performance were observed, underscoring their pivotal role in social reasoning. The study also found generalizability across diverse social reasoning tasks with different causal inference patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study tries to figure out how big language models understand what other people are thinking and feeling. It’s like trying to read minds! The researchers found that these models can actually tell when someone is believing or not believing something just by looking at the way their brain works. This is important because it helps us understand how we think about other people and make decisions based on what they might be thinking. The study also shows that this understanding can be used in many different situations, like trying to figure out why someone did something. |
Keywords
» Artificial intelligence » Inference
