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From the 1 of 18 linked papers with an AI index.

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20242026
most citedPredictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

4 citations · 4 across the 6 of their papers we have counts for

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cs.CL2026★ 4 cited

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency

Matthew L. Smith, Jonathan P. Shock, Samuel T. Segun +2

While scaling laws govern aggregate large language model performance, no scaling law has linked factual recall to both model size and training-data composition. We evaluated 38 mod…

cs.CL2026

Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?

Prateek Rajput, Yewei Song, Iyiola E. Olatunji +2

Can large language models reliably express a human-like personality, or are they merely mimicking surface cues without a stable underlying profile? To investigate this, we induce p…