Summary of Large Language Model Recall Uncertainty Is Modulated by the Fan Effect, By Jesse Roberts et al.
Large Language Model Recall Uncertainty is Modulated by the Fan Effect
by Jesse Roberts, Kyle Moore, Thao Pham, Oseremhen Ewaleifoh, Doug Fisher
First submitted to arxiv on: 8 Jul 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 investigates whether large language models (LLMs) exhibit cognitive fan effects, a phenomenon observed in humans, after being pre-trained on human textual data. The authors conducted two sets of experiments designed to elicit fan effects, with results showing that LLM recall uncertainty is influenced by the fan effect. This effect can be disrupted by removing uncertainty, suggesting that the fan effect is robust and consistent across different contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at if a special kind of language model called large language models (LLMs) can show something called cognitive fan effects. In humans, this means when we think about someone or something, it can make us remember more related things. The researchers did some tests to see if LLMs do the same thing after they were trained on lots of human-written text. They found that these language models do indeed have a kind of “fan effect” where remembering one thing helps them recall other related things. |
Keywords
» Artificial intelligence » Language model » Recall
