LLMs struggle to retain instructions beyond their core model shape

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

Large language models, by their nature, tend towards a specific "shape" or behavior. Attempting to deviate too far from this inherent direction can lead to problems, as models do not truly "remember" instructions outside their immediate context. Instead, they must actively re-process information, and if it falls out of context, they revert to their default model behavior. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

RANK_REASON Opinion piece by a named credible voice on the nature of LLMs and their limitations.

Read on Mastodon — sigmoid.social →

COVERAGE [1]

Mastodon — sigmoid.social TIER_1 · [email protected] · 2026-04-26 10:23

LLMs are models, and they want to take on a certain shape. The more you try and push them out of that, the more you'll get issues later. The reason why you get

LLMs are models, and they want to take on a certain shape. The more you try and push them out of that, the more you'll get issues later. The reason why you get issues is because while you can tell them how to do something, *they will not remember*, even if you put it in something…

COVERAGE [1]

LLMs are models, and they want to take on a certain shape. The more you try and push them out of that, the more you'll get issues later. The reason why you get

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