Summary of Benchmarking Mental State Representations in Language Models, by Matteo Bortoletto et al.
Benchmarking Mental State Representations in Language Models
by Matteo Bortoletto, Constantin Ruhdorfer, Lei Shi, Andreas Bulling
First submitted to arxiv on: 25 Jun 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 The abstract discusses the internal representation of mental states in language models (LMs) and how they can represent beliefs about themselves and others. While previous research has demonstrated this capability, there is limited evaluation on how different model designs and training choices affect these representations. The paper presents an extensive benchmarking study to investigate the robustness of mental state representations and memorization issues within probing experiments. The results show that larger models with fine-tuning perform better in representing others’ beliefs, while prompt variations can impact probing performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Language models are getting smarter at understanding our thoughts! Researchers have been studying how these AI systems understand people’s perspectives, but they haven’t looked closely at what’s going on inside the models. This paper explores how different model sizes and training methods affect how well language models can represent other people’s beliefs. They found that bigger models with special training do a better job of understanding others’ thoughts. The study also shows that even small changes in the way we ask questions can affect how well the model understands us. |
Keywords
» Artificial intelligence » Fine tuning » Prompt
