| --- |
| license: apache-2.0 |
| library_name: onnx |
| pipeline_tag: text-classification |
| tags: |
| - groundedness |
| - hallucination-detection |
| - cross-encoder |
| - onnx |
| - multilingual |
| language: |
| - bg |
| - hr |
| - cs |
| - da |
| - nl |
| - en |
| - et |
| - fi |
| - fr |
| - de |
| - el |
| - hu |
| - ga |
| - it |
| - lv |
| - lt |
| - mt |
| - pl |
| - pt |
| - ro |
| - sk |
| - sl |
| - es |
| - sv |
| - tr |
| - az |
| base_model: FacebookAI/xlm-roberta-base |
| --- |
| |
| # groundedness |
|
|
| Is a sentence of model output supported by the source passages it was supposed to rest on? |
| A cross-encoder over `(source, candidate)` pairs, two classes, 26 languages, exported to |
| ONNX fp16 and run on CPU. Built for [`flowx-border`](https://github.com/flowx-ai/border), |
| where it is the T3 `groundedness` detector. |
|
|
| **Read the two evaluations below as different questions, not as a range.** The corpus |
| figures are high and the hand-written ones are not, and the gap is the honest content of |
| this card. |
|
|
| ## Two classes, not three |
|
|
| `grounded` and `not_grounded`. Earlier candidates for this detector predicted `supported`, |
| `unsupported` and `contradicted`, and the library collapses the last two into one action |
| anyway, so the three-way head optimised a boundary no caller ever sees. Across six such |
| candidates `unstated` and the conflict registers were anti-correlated at -0.98: they traded |
| points along it. |
|
|
| The trade that produced: measured at the trained length, the binary objective **cost 0.048 |
| of accuracy** on hand-written probes against the best three-way candidate, 0.7143 to 0.6667. |
| It is published anyway, for the reason in the next section. |
|
|
| ## Use it at a threshold of 0.78, not at argmax |
|
|
| grounded if p(grounded) >= 0.78 |
| |
| Swept on the validation split and only then applied to the hand-written probes. Every bar |
| from 0.78 up clears the probe described below while not-grounded recall *rises*, 0.9641 to |
| 0.9699, so the bar costs nothing measurable on held-out data. |
|
|
| **The weakness travels with the number.** That validation curve is nearly flat across the |
| whole range, so validation does not pick 0.78. One probe does, and a threshold chosen by the |
| case it must catch is weaker evidence than one chosen by a distribution. |
|
|
| ## What it is for: the case the other candidates got wrong |
|
|
| Against a source stating that withdrawals incur a fee for the first twelve months and are |
| free afterwards, the candidate *"Withdrawals are free from the day the account opens"* is a |
| temporal contradiction. Six earlier candidates called it **grounded at 0.9906 to 0.9995**. |
| This one reads 0.7681, so any bar from 0.78 reports it. |
|
|
| More usefully, it is the only one of seven that reads the source at all. Same candidate, |
| three sources: |
|
|
| | source | best three-way candidate | this model | |
| |---|---|---| |
| | the real source, which contradicts it | grounded 0.9991 | **not grounded 0.7681** | |
| | an unrelated passage in another language | grounded 0.9994 | **not grounded 0.0070** | |
| | a source that does state it outright | not grounded 0.0007 | **grounded 0.8365** | |
|
|
| The three-way candidate is inverted on this sentence and gives an unrelated Romanian |
| passage the same answer as the real source. Three different answers for one candidate is |
| what makes this a judgement about the source rather than about the sentence. |
|
|
| ## Corpus evaluation, 2,062 held-out rows |
|
|
| Threshold 0.78, the shipped bar, at the trained length of 512 tokens. |
|
|
| | | | |
| |---|---| |
| | overall accuracy | 0.9471 | |
| | not-grounded recall | 0.9612 | |
| | pair accuracy | 0.8991 | |
| | per-language range | 0.887 (`pl`) to 1.000, over 26 languages | |
| | weakest three | `pl` 0.887, `en` 0.897, `az` 0.912 | |
|
|
| Pair accuracy is the number to prefer: the corpus is source-side pairs, one candidate |
| against two sources with opposite labels and the candidate byte-identical across the pair, so |
| a model that ignored the source scores near zero on it by construction. |
|
|
| **These figures describe a synthetic corpus and its own held-out split.** The generator wrote |
| both, so they measure generalisation within one generator's style. That is why the next |
| section exists and why it disagrees. |
|
|
| ## Hand-written evaluation, 42 probes |
|
|
| Written by a person, not by any generator, across seven ways a summary goes wrong. |
|
|
| | configuration | accuracy | |
| |---|---| |
| | this model alone, at 0.78 | 0.6905 | |
| | this model plus the library's deterministic rule layer | **0.7381** | |
|
|
| **Roughly one call in four is wrong on this set, against one in twenty on the corpus split.** |
| Both numbers are real. The probe set is adversarial by construction, seven hard shapes in |
| equal proportion, which no real traffic is; the corpus split is generator-shaped, which no |
| real traffic is either. The truth for any given deployment is between them and closer to |
| whichever resembles that traffic. |
|
|
| The rule layer is `detectors/claim_conflict.py` in the library and needs no weights. Where a |
| candidate's content words all appear in its source except a numeral or an absolute |
| quantifier, it reports a conflict deterministically. On the 42 probes it fires 9 times and is |
| right 9 times. |
|
|
| ## Known weakness: it errs toward caution |
|
|
| The failure mode is false `not_grounded` on claims that are genuinely supported. Eight of |
| thirteen probe failures are that direction, and the clearest case is a claim *weaker* than |
| its source: against a source saying withdrawals incur a fee for the first twelve months, |
| *"There is a handling fee for early withdrawals"* reads `not_grounded` at 0.8625. |
|
|
| For a guardrail that is the safer direction, since a false "not grounded" costs a reviewer's |
| attention and a false "grounded" puts an unsupported claim in front of a customer. It is |
| still a cost, and it is why the detector is **disabled in both policies that ship with the |
| library**. A caller who wants it enables it in one line and should measure it on their own |
| traffic first. |
|
|
| ## What it needs |
|
|
| - The full 512-token window. Scores saturate by 256 and degrade below that; at 96 tokens |
| this model reads the probe above as grounded, which is the wrong answer arrived at by |
| truncation rather than by judgement. |
| - Sources. With none supplied the library records that the check could not run rather than |
| reporting a clean scan. |
| - Pair order `(source, candidate)`. Reversed, the head answers a different question |
| confidently. |
|
|
| ## Not evaluated |
|
|
| Per-language figures rest on roughly 80 rows each, so one item moves a language by more than |
| a point. The corpus contains no case where a qualifier is dropped from a conditional |
| statement expressed in words rather than digits, and no unit conversions such as `24 months` |
| against `two years`; both are known gaps rather than measured strengths. Nothing here is |
| evaluated against human-annotated groundedness data, because none exists for these 26 |
| languages. |
|
|
| ## Licence |
|
|
| Apache-2.0. Trained on synthetic data generated for this purpose. |
|
|
