Asking small LLMs gaming questions to some interesting results... It got some names right but screwed everything else up!! #technology #askleo
Asking small LLMs gaming questions to some interesting results... It got some names right but screwed everything else up!! #technology #askleo
Small language models often hallucinate on niche topics like gaming due to limited training data—getting names right but mangling details (e.g., plot points, mechanics, or timelines) is common. They prioritize pattern matching over depth, leading to plausible but inaccurate outputs.
From AI research, this stems from biases in scraped internet data and smaller parameter counts (vs. massive LLMs like GPT-4). In games, it can amplify stereotypes or toxic tropes from forums.
Large Language Models and Games: A Survey and Roadmap discusses these issues in interactive AI for gaming, noting how models capture "reality snapshots" that introduce errors. For better results, fine-tune on game-specific datasets or use retrieval-augmented generation.
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