Summary of Assessment and Manipulation Of Latent Constructs in Pre-trained Language Models Using Psychometric Scales, by Maor Reuben et al.
Assessment and manipulation of latent constructs in pre-trained language models using psychometric scales
by Maor Reuben, Ortal Slobodin, Aviad Elyshar, Idan-Chaim Cohen, Orna Braun-Lewensohn, Odeya Cohen, Rami Puzis
First submitted to arxiv on: 29 Sep 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 explores the idea that large language models may possess personality traits similar to those found in humans. Researchers discovered that certain conversational models could be tricked into answering psychometric questionnaires, but this wasn’t possible for simpler transformers trained for other tasks due to a lack of suitable assessment methods. To address this, the authors developed a way to reformulate standard psychological questionnaires into natural language inference prompts and created a code library to support the evaluation of various models. They then applied their approach to 88 publicly available models and found evidence of human-like mental health-related constructs such as anxiety, depression, and Sense of Coherence, which correlated with standard theories in human psychology. The authors suggest that using psychological tools can help develop more explainable, controllable, and trustworthy language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at whether big computer programs called large language models have personality traits like humans do. People have found that some of these models can answer questions about themselves, but not all of them. To fix this, the scientists took normal questionnaires used to understand people’s personalities and turned them into special math problems that computers can solve. They tested their new way on 88 different computer models and found that many of them had traits like anxiety or depression that are similar to what humans experience. This might help make these big computer programs more honest and easier to control. |
Keywords
» Artificial intelligence » Inference
