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LLM Propensities Shift from Over- to Under-Elicitation

By PulseAugur Editorial · [1 sources] ·

A recent analysis on LessWrong explores the evolving behavior of Large Language Models (LLMs) concerning their elicited propensities. Initially, models tended to over-elicit certain behaviors or traits when prompted. However, this trend has shifted, with current models showing a tendency towards under-elicitation. This shift suggests a dynamic and complex development in how LLMs respond to user inputs and internal programming. AI

IMPACT This analysis highlights the changing nature of LLM responses, suggesting a need for continuous adaptation in how we interact with and prompt these models.

RANK_REASON The item is an opinion piece or analysis on LLM behavior, not a primary release or significant industry event.

Read on LessWrong (AI tag) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM Propensities Shift from Over- to Under-Elicitation

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2 / 100
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Commentary
The item is an opinion piece or analysis on LLM behavior, not a primary release or significant industry event.
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High
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COVERAGE [1]

  1. LessWrong (AI tag) TIER_1 English(EN) · David Africa ·

    From overeliciting to undereliciting LLM propensities in just a few months

    <p><span style="white-space: pre-wrap;">For a while, we felt we had a pretty good grasp over LLM behaviour because we had to exert significant effort to elicit misaligned behaviour. Then, fairly suddenly, we moved to undereliciting misaligned behaviour. Let us recount: </span></p…

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