README.md

---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- en
tags:
- chart-question-answering
- data-visualization
- misleading-charts
- tufte-lie-factor
- deception-detection
- counterfactual-augmentation
- benchmark
pretty_name: 'PolyChart: Shown Is Not Supported'
size_categories:
- 1K<n<10K
---


# Shown Is Not Supported

A chart is a completion claim. It renders cleanly, states a confident finding,
and the underlying data may not support it. A model reading that chart inherits
the gap: it answers from the visual impression because it never extracted the
values.

This adapter reads the data first and corrects the chart when its encoding
misleads. It ships with the first continuous score for *how much* a chart lies.

Trained with AutoScientist by Adaption for the AutoScientist Challenge 2026,
Part 2, Data Visualization.

## Results

| Evaluation | Base | Adapted |
|---|---|---|
| On the dataset | 15 | **85** |
| Across the Data Visualization category (held-out) | 22 | **78** |

Pairwise win rate, LLM judge, against the same untuned base (Llama-3.3-70B).
78% on the held-out category clears the gate; the Legal category was won at 73.

The number is the least interesting part. Here is what the work actually is.

## The failure this probes: a shortcut, not a knowledge gap

Geirhos et al. (2020) call a shortcut a decision rule that works on the benchmark
and fails when the test gets harder. Their network learned an object's *location*
instead of its *shape*, and broke the moment location stopped predicting the label.

Chart models do the same thing. We ran a base VLM on 60 matched pairs, the same
series drawn faithfully and drawn to deceive, and the result is not uniform:

| Distortion | Wrong direction, faithful to distorted |
|---|---|
| Inverted axis | 17% to **93%** |
| Truncated axis | 0% to 0% (magnitude error fell) |
| Aspect ratio | 4% to 4% (no effect) |

The model takes **direction from the geometry and magnitude from the axis labels,
and never reconciles them**. Invert the axis and the geometry shortcut breaks:
the trend reads backwards 93% of the time while the magnitude, read from the
untouched labels, survives with identical error (52.1 both sides). Squash the
aspect ratio, which changes steepness but not label values, and nothing happens,
because steepness was never being read.

That is a clean, isolated shortcut. The dataset is the intervention that closes it.

## The intervention: counterfactual augmentation, built exact

Kaushik, Hovy and Lipton (2020) fix shortcut learning by pairing each example with
a counterfactual: the same content, minimally edited to flip the label, so a model
cannot lean on the spurious pattern. Their edits were written by humans, which is
slow, approximate, and does not scale.

We build the same thing from parameters. Every chart is rendered twice from one
OWID series: once honestly, once with a distortion whose parameters we set. The
distortion *is* the counterfactual edit, and the label is derived from it, not
annotated. Same data, altered encoding, exact label, no human in the loop.

Composition, 2,129 rows, weighted to the failure cells (see next section):

- **supported (54%)** accurate extraction and interpretation
- **severity (27%)** the lie factor, see below
- **contradicted (13%)** the encoding overstates or reverses the data; the answer
  gives the true figure and names the mechanism
- **unanswerable (5%)** the chart cannot answer the question; the model says so

## Targeted, not uniform: hard-example mining

Winning data-centric submissions (KAIST, Data-Centric AI Competition) do not add
uniform coverage; they find where the model fails and concentrate data there. We
ran the base model across every question-type by distortion cell and scored it
against exact ground truth. The failures cluster in three places: severity
estimation, precise value extraction under a distorted axis, and mechanism
identification on inverted and cherry-picked charts. Comparison and abstention
were already solved. We re-weighted the dataset to those failure cells and removed
the solved ones.

The effect is measurable. The uniform version of this dataset trained on a 109B
base reached 76 on the held-out category. This failure-weighted version reached 78
on a **smaller 70B base**. Targeted data compensated for less model capacity, and
on-dataset win rate rose from 77 to 85. Curation and targeting beat both scale and
capacity, the result the data-centric literature predicts.

## The new part: deception has a magnitude, and it is computable

Every misleading-chart dataset, including Misviz (ACL 2026), answers a yes/no
question: is this chart misleading, by which of N mechanisms. None answers *how
much*.

Edward Tufte defined the Lie Factor in 1983: the size of the effect shown in the
graphic over the size of the effect in the data. One is honest. Because we
construct every distortion, we compute it exactly rather than estimate it. 471
rows carry a lie factor and a severity band, from honest (1.0) through a
truncation that triples the apparent slope (3.0) to an inverted axis that keeps
the magnitude and reverses the sign (-1.0).

This turns deception detection from a label into a graded, calibrated target. No
released chart dataset provides it.

## The Deception Atlas: the measure on charts that actually aired

The distortions above are constructed, which is what makes their Lie Factors exact.
The atlas shows the task transfers to the real world: **21 published charts, verified
against primary sources, across 11 distinct mechanisms**, none reconstructed. This is
the part of the dataset that is not a chart-QA benchmark. It is a quantified corpus of
real chart lies, a thing no released dataset provides.

| Chart | Mechanism | Severity | The real reading |
|---|---|---|---|
| Fox News, top tax rate (2012) | truncated bars | LF 4.95 | 35% to 39.6%, a 13% rise, drawn 5.6x taller |
| Fox News, welfare vs work (2013) | truncated bars | LF 4.69 | 108.6M vs 101.7M, a 7% gap, drawn 5x taller |
| Planned Parenthood, AUL chart (2015) | dual mismatched axes | LF 2.86 | screenings outnumber abortions 2.9 to 1, drawn as equal |
| Reuters, Florida gun deaths (2014) | inverted axis | LF -1.0 | deaths rose 521 to 825 after 2005, drawn as a fall |
| National Review, global temperature (2015) | expanded axis | LF 0.017 | a real 1.1 C rise flattened to under 2% of the frame |
| Ted Cruz, satellite "pause" (2015) | cherry-picked window | severe | window anchored to the 1998 El Nino peak; +0.44 C over 1997-2015 |
| Steve Jobs, smartphone share (2008) | 3D pie perspective | severe | Apple 19.5% tilted forward to rival RIM's 39% |
| Georgia DPH, COVID counties (2020) | reordered x-axis | severe | dates out of order manufacture a decline that is not in the data |
| Lipitor, "36% risk reduction" (2008) | percent-absolute swap | severe | a 1.1 point absolute drop (3.0 to 1.9) sold as 36% |
| OpenAI, GPT-5 "coding deception" chart (2025) | mis-scaled bars | severe | a bar labeled 50.0 drawn shorter than one labeled 47.4; corrected to 16.5 |
| Meta, Llama-4 on LMArena (2025) | claim vs data | severe | rank earned by an experimental variant; the released model scored ~1370, not 1417 |
| Adaption, "+16 beyond 20k rows" | claim vs data | severe | this project's runs show 76 to 72, a minus 4 |

The severities span the full signed range the measure defines: -1.0 where an inverted
axis reverses a real rise, 0.017 where an expanded axis hides one, 4.95 where a truncated
baseline invents one. A yes/no "is it misleading" label collapses all of these into the
same bit. Where a single Lie Factor is not defined (a reordered axis, a 3D pie) the row
names the mechanism and bands the severity rather than invent a number. Where a baseline
was unlabelled it is marked inferred with the arithmetic shown.

The atlas also holds the AI industry's own charts. The GPT-5 launch bar chart drew a bar
labeled 50.0 shorter than one labeled 47.4, geometry against its own printed values, in
Sam Altman's words a "mega chart screwup." The Llama-4 leaderboard rank was earned by a
non-public variant the fine print called Experimental. And the sharpest case is the
platform that trained this model: its augmentation panel advertises +16 win-rate points
beyond 20,000 datapoints, and this project's controlled runs, at matched base and epochs,
measured 76 falling to 72. The atlas audits the tools of its own field, on the dataset's
own thesis: shown is not supported. Full corpus, sources, and computation are in
`data/atlas/` and `data/real/`.

## A controlled study, not a single run

We ran the task through five base models AutoScientist selected and read the
training curves out of every adapter.

| Base | Params | On-data | Category | What the curve shows |
|---|---|---|---|---|
| Llama-3.3-70B (this model) | 70B | 15 to 85 | 22 to **78** | 3 epochs, hard-mined data, eval loss down |
| Llama-4-Scout (uniform data) | 109B | 24 to 77 | 24 to 76 | same recipe, uniform coverage |
| Mistral | 7B | 34 to 66 | 47 to 54 | held slightly |
| gemma | 4B | 52 to 48 | n/a | eval loss 1.28 to 2.73; severe overfit |
| Llama-3.2 | 3B | 49 to 51 | 54 to **47** | overfit; category fell below base |
| Qwen3.5 | 0.8B | 41 to 59 | 56 to **45** | underfit and category below base |

Two findings.

**Capacity dominates for uniform data, but targeted data closes the gap.** With
uniform coverage, only the large bases held their held-out performance: every base
under 10B overfit at the platform default of 10 epochs, eval loss rising from the
first checkpoint while train loss fell toward memorization. Yet the failure-weighted
data let a 70B base reach 78, above the 109B's 76 on uniform data. Capacity sets the
floor; targeting the data raises it. The Hardware Lottery (Hooker 2020) is real, and
better data is the lever a competitor actually controls.

**The headline number hides who gets hurt.** Every small model improved on its own
data while degrading on the held-out category. This is the effect Hooker et al.
(2019) documented for compression: aggregate accuracy barely moves while the
underrepresented distribution is quietly forgotten. Our two-panel split, on-data
against held-out category, is the instrument that exposes it, the same move as
their Pruning Identified Exemplars. We report both panels so the concealment is
visible.

**More data made it worse.** We tested the platform's own claim that crossing
20,000 datapoints yields +16 win-rate points. Expanding this dataset from 1,882 to
21,882 rows with the platform's synthetic augmentation dropped the held-out
category from 76 to 72, at matched epochs and base model, with eval loss still
descending. It is dilution by unverified synthetic data, not overfitting: 1,882
verified rows beat 21,882 augmented ones by four points. Curation beats scale.

## The metric probes itself

The eligibility number is LLM-judge pairwise win rate, so it inherits the biases
Zheng et al. (2023) documented for that judge, verbosity among them: a preference
for longer answers regardless of quality. We probe it directly with a terse-answer
ablation, and we write answers to be correct-and-complete rather than terse, so
substance and length are separated rather than confounded.

## Nine silent failures, caught by an independent verifier

The dataset is about models stating confident findings their input does not
support. We built a verifier that shares no code with the generator and re-derives
every claim from the source table. It caught nine such failures in our own
pipeline before anything shipped, including:

- an answer calling a 183% rise "far more modest than it looks" on a 1.28x
  exaggeration
- an ingest that returned unusable data for 22 of 28 sources while every request
  returned 200 and the build reported success
- three failures inside the verifier itself, which failed the standard it applies

The last point is the thesis reproduced inside the tool built to study it: a
grader that is confident, well-formed, and wrong until an independent check catches
it. Every failure and its fix is in the repository.

## Honest limitations

- **Epoch count did not matter on this base.** At 3 epochs this model's eval loss
  fell throughout (0.944 to 0.861, no overfit); an earlier 10-epoch run on related
  data scored within noise (78 vs 76 category). The large bases are insensitive to
  the epoch schedule, unlike the sub-10B bases in the study, which overfit badly at 10.
- **Editor overrides did not persist.** AutoScientist re-selected the base model
  regardless of the manual recipe, so the five-model comparison is confounded by
  base as well as composition. We report the base per run rather than hide it.
- **Text specifications for the headline model.** Charts are given as plotted
  values plus axis parameters. A multimodal image variant is released separately.
- **Single evaluation source**, platform-reported, with a documented plus or minus
  8 to 10 point run-to-run noise we did not average out.

## Related work

Misviz and Misviz-synth (ACL 2026) release misleading visualizations and classify
which of 12 design rules a chart breaks. Chart-QA benchmarks (ChartQA, CharXiv,
ChartMuseum) test reading. Our contribution is orthogonal: a written correction
grounded in the source table, and a continuous severity target, neither of which a
released dataset provides.

## Artifacts

- Seed dataset (verified): `polychart-shown-is-not-supported`
- Adapted dataset (trained this): `chart-qa-with-axis-tricks`
- Real-world validation slice: `data/real/`, three published charts with cited data
- Weights (this repo)
- Generation and verification code: public, reproducible from one command
- Source data: Our World in Data, CC-BY, per-row citation

## References

- Geirhos et al. Shortcut Learning in Deep Neural Networks. Nature Machine Intelligence, 2020.
- Kaushik, Hovy, Lipton. Learning the Difference that Makes a Difference with Counterfactually-Augmented Data. ICLR, 2020.
- Hooker et al. What Do Compressed Deep Neural Networks Forget? 2019.
- Hooker. The Hardware Lottery. 2020.
- Zheng et al. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena. NeurIPS, 2023.
- Mazumder et al. DataPerf: Benchmarks for Data-Centric AI Development. NeurIPS, 2023.
- Tufte. The Visual Display of Quantitative Information. 1983.