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LLMs' certainty vs. guessing analyzed via token probabilities

By PulseAugur Editorial · [1 sources] ·

Researchers explored how to determine if a Large Language Model (LLM) is guessing or knows an answer by analyzing token probabilities. They found that lower entropy, indicated by high probabilities for top alternative tokens, suggests certainty, while higher entropy implies guessing. When tested, GPT-4o-mini demonstrated honest uncertainty on creative tasks, whereas GPT-4.1-nano showed miscalibration, making it less suitable for autonomous decision-making. AI

IMPACT This research could lead to better calibration of LLMs, improving their reliability for autonomous tasks by distinguishing confident predictions from guesses.

RANK_REASON The cluster details an analysis of LLM behavior using token probabilities to distinguish between guessing and knowing, which is a research-oriented topic. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

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

LLMs' certainty vs. guessing analyzed via token probabilities

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The cluster details an analysis of LLM behavior using token probabilities to distinguish between guessing and knowing, which is a research-oriented topic. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Alex ·

    LLM guesses or knows

    <p>We wanted to know when LLM is guessing versus when it actually knows the answer.</p> <p>LLM models expose logprobs - after every word they generate, you can request the top alternative tokens and their probabilities. Low entropy means the model was certain, high means it was g…

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