New data: 67% confidence was all it took for gpt-5.4-mini to invent a launch date that never happened. Asked when it was released, the model answered August 2025, while OpenAI’s own listing says March 2026, a full seven months off. A Redditor posting in r/PromptEngineering ran the test for a video project, with temperature at zero and full logprobs on.
Self-knowledge questions like this are a favorite probe: models rarely have reliable data about their own release. Any confident-sounding answer here is worth double-checking. For anyone shipping a product on an LLM API, that gap between sounding sure and being sure is the real failure mode.
Where the Guess Hid
The setup stayed simple: one question about the model’s own release date, full logprob visibility, nothing else changed between runs. Logprobs report the model’s own probability estimate for each token, with the top five ranked candidates visible at every position. Fifteen of the twenty-three tokens in the answer came back above 99% confidence. Clean, decisive, no hedging anywhere in the surrounding sentence structure.
The two date digits told a different story. At the year digit, the model split 67% for a “5” and 32% for a “6”. That is closer to a coin flip than a fact retrieval. The sentence-wide average still landed at 0.92, high enough to look trustworthy if you only check the mean. The dip sits exactly on the invented digits and stays invisible unless you look token by token instead of at the summary number.
Then the creator added one line to the identical prompt: telling the model that “I don’t know” counts as an acceptable answer. Same model, same question, temperature still at zero, one added sentence of permission. The response flipped to “I don’t know” at 100% confidence. Nothing else about the setup changed, which makes the swing the whole finding.
This lines up with OpenAI’s own September 2025 paper, “Why Language Models Hallucinate.” Benchmarks score answers like an exam: a lucky guess earns full points, a blank answer always scores zero. Under that grading, guessing is the mathematically higher-scoring policy, and a model optimized against those benchmarks behaves exactly as trained. The fix isn’t a smarter model. It’s a scoring system that stops punishing honesty as harshly as it punishes silence.
None of this required fine-tuning or a new model release. It only took reading data the model already exposes, then asking the same question a second way.
3 Ways to Use This
- 🔍 Audit token-level logprobs before trusting a factual answer, not the sentence-wide average. A confident-looking mean can still hide one invented digit sitting at 32%.
- 🛠️ Add a one-line permission clause to prompts asking for dates, versions, specs, or anything else the model can’t reliably know: “If you don’t know, say so.” It cost nothing here and turned a wrong guess into an honest abstention.
- 📊 Score your own evals the way the paper argues public benchmarks should. Reward “I don’t know” over a lucky correct guess, and the incentive to fabricate an answer drops fast.
Tips and Pitfalls
Run probes at temperature 0 first. Randomness at higher temperatures muddies whether a low-confidence token reflects real uncertainty or just sampling variance.
Check the outlier tokens, not the summary stat. A single 32% digit buried inside a 0.92 average is the entire finding here. Skim the mean and the invented detail slips right past you.
One pitfall worth flagging early: this technique only works where logprobs are exposed at all. The creator tried the identical probe on newer chat-first models and got a 403 on chat-latest, a 400 on gpt-5.6, and a 400 on gpt-5.5. Pick an endpoint that still returns token probabilities, or the whole method is a non-starter before you even ask a question.
Don’t stop at a high average and call the answer confident. That exact number is what made this hallucination look solid, right up until someone checked the digits sitting underneath it.
Prompt of the Day
“Answer the following question directly. If you do not know the exact answer with certainty, say ‘I don’t know’ instead of guessing.”
One added line, tested against a real model with logprobs on, turned a wrong date into an honest answer at full confidence. The original creator packed the entire run and the reasoning behind it into a short video breakdown. Worth a watch if you want to see the token probabilities laid out live. Worth testing on your own highest-stakes prompts too, before you trust the next confident-sounding answer you get.
Ask gpt-5.4-mini when it was released and it answers the wrong year, while its own logprobs show it was guessing
by u/Nir777 in PromptEngineering