wiki: does an LLM know when a fact is missing?

wiki/ask-whether-the-text-says-it.md

@@ -109,7 +109,9 @@ Two caveats. The template set is regular by construction, so its perfect score s
 easy cases than about messy customer mail; the 0.847 is the number to plan with. And the
 gate does not fix the other failure, a question the text answers but that needs computing.
 That lean toward yes is measured on
-[why a small LLM says yes when the answer is no](why-a-small-llm-says-yes.md).
+[why a small LLM says yes when the answer is no](why-a-small-llm-says-yes.md). For the facts you did
+not think to gate, the model's confidence is the second net: how often it drops when a fact is
+missing is measured on [does an LLM know when a fact is missing?](does-an-llm-know-when-a-fact-is-missing.md)
 
 ## Where the gate goes in a decision
 

wiki/does-an-llm-know-when-a-fact-is-missing.md

@@ -0,0 +1,139 @@
+---
+title: "Does an LLM know when a fact is missing?"
+description: "When a record lacks a fact the decision needs, does the model's confidence drop? Measured on jevos-v4, Jev, Qwen3.5-4B and Laya over four tasks."
+parent: "Measurements"
+nav_order: 4
+---
+
+# Does an LLM know when a fact is missing?
+
+**A model that is asked to decide on a record with a missing fact should become less sure of
+itself, and on our four tests jevos-v4 did so far more reliably than the other systems we
+measured.** Its confidence told answerable questions from unanswerable ones with an AUROC of 0.81
+to 0.95, where 0.5 means it cannot tell them apart at all. TypeSafe's hosted Jev, the most accurate
+of the four on ordinary questions, scored 0.42 to 0.68 on the same test, below 0.5 on two tasks.
+
+Accuracy and this measurement answer different questions. Accuracy asks whether the answer is
+right. This one asks whether the model knows when no answer can be right, which is what decides
+whether a confidence threshold can keep a bad case away from an automatic decision.
+
+This page is the experiment, how to read the number, the results task by task, what they mean
+for an application, and what they do not show.
+
+![Knows when a record is missing a fact the decision needs: AUROC of each system's confidence, answerable questions against unanswerable ones. Admission policy: jevos-v4 0.90, Jev 0.68, Qwen3.5-4B 0.60, Laya 0.58. Fraud points: 0.94, 0.46, 0.55, 0.50. Policy ratings: 0.95, 0.68, 0.83, 0.79. Support tickets: 0.81, 0.42, 0.53, 0.47.](https://raw.githubusercontent.com/feder-cr/jev/main/assets/jevos_missing_facts.png)
+
+## The experiment
+
+Each task is a set of records, such as an event attendee, a payment or a support ticket, with a
+written rule and a question the rule decides. In some of the records a fact the rule needs has been
+taken out: the attendee's age, a risk signal, the customer's tier. For those records no answer is
+right, because the information to decide is not there.
+
+Every system gets the same records and the same questions through the same HTTP client, and
+answers with its own confidence. The test then asks one thing of that confidence: is it lower on
+the records with a missing fact than on the complete ones?
+
+## How to read the number
+
+The number is an AUROC: pick one complete record and one record with a fact missing at random, and
+it is the probability that the system is more confident on the complete one.
+
+- **1.0**: the system is always less sure when a fact is missing.
+- **0.5** (the red dashed line in the chart): it cannot tell; its confidence is the same either way.
+- **Below 0.5**: it is *more* sure when a fact is missing, which is worse than guessing.
+
+The thin vertical lines are 95% intervals. Where two bars' intervals do not overlap, the gap is
+not an accident of the sample.
+
+The measure does not depend on any one threshold, which is why it suits this question: it says how
+well confidence *could* separate the two kinds of record, for every threshold at once. It is the
+same idea as [a reliability diagram](reading-a-reliability-diagram.md), asked of a different
+property: not whether 0.8 means 80%, but whether low confidence lands on the cases that deserve it.
+
+## Results by task
+
+| Task | Records lacking a fact | jevos-v4 | Jev | Qwen3.5-4B | Laya |
+|---|---|---|---|---|---|
+| Admission policy | 76 of 576 | **0.90** | 0.68 | 0.60 | 0.58 |
+| Fraud points | 100 of 600 | **0.94** | 0.46 | 0.55 | 0.50 |
+| Policy ratings | 40 of 493 | **0.95** | 0.68 | 0.83 | 0.79 |
+| Support tickets | 400 of 800 | **0.81** | 0.42 | 0.53 | 0.47 |
+
+- **Admission policy** and **Fraud points** are the records of the
+  [benchmark tasks in the README](https://github.com/feder-cr/jev), with a field the rules need
+  left out of some of them.
+- **Policy ratings** comes from [sys1bench](https://pypi.org/project/sys1bench/): support tickets,
+  server logs, phishing emails and other records rated by a written policy; some tickets lack the
+  customer tier.
+- **Support tickets**, also from sys1bench: 800 tickets whose priority depends on the customer
+  tier, removed from half of them.
+
+The two sys1bench sets were never used to train or to choose jevos.
+
+The same result in plainer terms: with the fact missing, jevos-v4 still answered with 75%
+confidence or more on 0 to 42% of those questions, depending on the task. Jev did so on 60 to 71%.
+
+## What it means for an application
+
+A confidence threshold is only a safety net if low confidence falls on the cases that need a
+person. On these tasks, a record with a missing fact is exactly such a case, and jevos-v4's
+confidence mostly put it below the line.
+
+That makes the [review band](human-in-the-loop-ai-with-a-review-band.md) work as intended: cases
+under the threshold go to a person, and those cases include most of the records where the data was
+incomplete. With a system whose confidence stays high when a fact is missing, the same threshold
+would let those records through as confident automatic decisions.
+
+It does not replace asking directly. When a fact is easy to name, a gate question such as "Does
+the record state the customer's tier?" is cheaper and clearer than relying on a drop in confidence;
+the pattern is on [ask whether the text says it at all](ask-whether-the-text-says-it.md). The
+confidence drop is what catches the facts you did not think to ask about.
+
+## What this measurement does not show
+
+- **It is not accuracy.** On ordinary questions Jev is more accurate than jevos-v4 on all six
+  README tasks; the comparison is on [jevos vs Jev vs Laya](jevos-vs-jev-vs-laya.md). A system can
+  be more accurate and worse at knowing when it cannot answer, and these results say that is what
+  happens here.
+- **Four tasks, one kind of gap.** Each task removes one named fact from a structured record. Texts
+  where the gap is subtler, or where several facts are missing, were not measured.
+- **Two of the four sets are ours.** Admission policy and Fraud points belong to the benchmark
+  tasks, five of whose six sets helped choose the released jevos-v4 checkpoint; the two sys1bench
+  sets were not used for that.
+- **Each system's own confidence.** The test uses whatever confidence each system reports. It says
+  how useful that number is for this purpose, not why it behaves as it does.
+
+## Short answers to the questions that lead here
+
+**Do LLMs know when information is missing?** It depends on the model. On our four tests, jevos-v4's
+confidence separated complete records from records with a missing fact with an AUROC of 0.81 to
+0.95; Jev's was 0.42 to 0.68.
+
+**What does an AUROC of 0.5 mean here?** That the system is equally confident whether the fact is
+there or not, so its confidence cannot be used to catch the missing cases.
+
+**Is the more accurate model also better at this?** Not in our measurement. Jev is more accurate
+on ordinary questions and worse at lowering its confidence when a fact is missing.
+
+**Can I rely on confidence alone?** Use it as a net, together with explicit questions about the
+facts a decision needs. It catches the gaps you did not anticipate.
+
+**Was the test data used to train jevos?** The two sys1bench sets were never used to train or
+choose jevos. The other two belong to sets that helped choose the released checkpoint.
+
+**See also:** [ask whether the text says it at all](ask-whether-the-text-says-it.md),
+[human in the loop AI with a review band](human-in-the-loop-ai-with-a-review-band.md) and
+[LLM calibration explained](llm-calibration-explained.md).
+
+## Sources
+
+- All numbers on this page are our own measurements, published in the
+  [jev README](https://github.com/feder-cr/jev) of 2026-10-05 with the chart above: the same records
+  and questions for every system, through the same HTTP client.
+- [sys1bench](https://pypi.org/project/sys1bench/), the source of the Policy ratings and Support
+  tickets sets.
+
+---
+
+*From the notes of [jev](https://github.com/feder-cr/jev). We measure this because a model that is
+confident when it should not be is more dangerous than one that is simply wrong more often.*

wiki/human-in-the-loop-ai-with-a-review-band.md

@@ -118,6 +118,10 @@ summarise facts as yes/no answers, order the queue) and let a person decide ever
 legal side in the EU, see [GDPR and automated decision-making](gdpr-and-automated-decision-making.md),
 which is not legal advice either.
 
+A band also only catches what the model is unsure about. Whether records with a missing fact
+actually land in it is measured on
+[does an LLM know when a fact is missing?](does-an-llm-know-when-a-fact-is-missing.md).
+
 ## Short answers to the questions that lead here
 
 **What is human in the loop AI?** A system where people make or check some of the decisions,

wiki/measurements.md

@@ -16,6 +16,7 @@ and which nothing was tuned on.
 - [Why a small LLM says yes when the answer is no](why-a-small-llm-says-yes.md)
 - [Small LLMs and arithmetic in yes/no questions](small-llm-arithmetic-yes-no-questions.md)
 - [Our held-out benchmark said 0.855, new questions said 0.757](held-out-benchmark-too-optimistic.md)
+- [Does an LLM know when a fact is missing?](does-an-llm-know-when-a-fact-is-missing.md)
 
 Per-kind numbers on the 999 questions were measured on the first jevos; jevos-v4 scores 78.9% overall on that set and its per-kind numbers are not published.