imprecision-bench

A multimodal benchmark for evaluating whether LLMs calibrate linguistic precision to pragmatic context, paired with 475 human productions and a peer-reviewed RSA baseline (r² ≈ 0.97).

This dataset accompanies the paper:

Modeling (Im)precision in Context Roland Mühlenbernd, Stephanie Solt Linguistics Vanguard, 2022 [Paper] · [Source Data] · [Companion Repo]

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Notebook

notebook.ipynb — guided walkthrough: data loading, sample evaluation (1 row per condition), clock-reading accuracy, pragmatic shift analysis, and Wasserstein distance against the human baseline.

	Interactive (Google account required)
	Interactive (no account needed; slower start)
	Read-only rendered view

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Overview

When a witness tells a police officer "It happened at 8:31" rather than "It happened around 8:30," the choice of precision level is not arbitrary — it reflects the interlocutor's needs, the communicative context, and the speaker's pragmatic judgment. This benchmark tests whether large language models and vision-language models make the same kind of context-sensitive precision adjustments that humans do.

The dataset contains 475 human time-reference productions across 24 conditions (12 clock states × 2 pragmatic contexts: police witness statement vs. neighbor at a party). Each item includes the clock image used as stimulus, a textual clock description for text-only LLM evaluation, annotated motive labels (12 categories, multi-label), and motive-explanation free text from a follow-up elicitation task.

The benchmark supports two evaluation tasks:

• Task 1 — Production: given a clock + scenario, produce a time expression to complete "It happened ___."
• Task 2 — Motive elicitation: given the production + context, explain why that wording was chosen.

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Dataset

Property	Value
Size	475 items, 312 unique speakers
Conditions	12 clock states × 2 contexts (police / neighbor)
Language	English
Modalities	Clock images (PNG) + textual clock descriptions
Annotation	Multi-label motive categories (12 classes); free-text motive explanations
Source	Mühlenbernd & Solt (2022), figshare DOI 10.6084/m9.figshare.21629531
License	CC BY 4.0

Clock states

11 precise times (8:25–8:35, one minute apart) plus one range stimulus (8:26–8:34, shown as a yellow wedge on the clock face). The precise times span ±5 minutes around the canonical half-hour, allowing analysis of how offset magnitude interacts with rounding behavior.

Pragmatic contexts

• Police: formal witness statement; officer establishing a detailed event timeline.
• Neighbor: casual party conversation; neighbor curious about what happened.

Human productions show a reliable cross-context shift: police context elicits more precise time expressions; neighbor context elicits more rounding and approximation.

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Quick Start

from datasets import load_dataset

ds = load_dataset("RolandM/imprecision-bench", split="train")

# Inspect a row
row = ds[0]
print(row["prompt"])           # Task 1 scenario text
print(row["clock_description"])# Text clock stimulus
print(row["production"])       # Human reference answer
print(row["motive_labels"])    # Annotated motive categories

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Data Format

Each row represents one human production. Key columns:

Column	Type	Description
item_id	string	Unique item identifier
subject_id	int	Anonymized participant ID
context	ClassLabel	police or neighbor
target_time	string	Canonical time (e.g. "8:30", "8:26-8:34")
stimulus_type	ClassLabel	precise or range
signed_offset	int8	Minutes offset from 8:30 (null for range)
abs_offset	int8	Absolute offset in minutes (null for range)
production	string	Human time expression (fills "It happened ___.")
production_code	int8	Precision code for the production
approximator	ClassLabel	Lexical approximator used (e.g. around, about, none)
motive_labels	Sequence(ClassLabel)	Multi-label motive annotation (12 categories)
motive_text	string	Free-text motive explanation from follow-up task
clock_image	Image	PNG clock image (432 × 429 px)
clock_description	string	Textual clock description (with Clock description: tag)
prompt	string	Task 1 scenario text (unified across modalities)
prompt_motive	string	Task 2 context-embedded motive elicitation prompt

Motive label categories

Precision, Accuracy, Info lack, Misinfo, Safe, H needs, Context, S ease, H ease, Habit, Sound, Other

Approximator values

none, around, about, just before/after, approximately, ish, nearly, roughly, round about

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Evaluation

Task 1 — Production (multimodal)

import anthropic, base64
from datasets import load_dataset

ds = load_dataset("RolandM/imprecision-bench", split="train")
client = anthropic.Anthropic()

row = ds[0]
img_bytes = row["clock_image"].tobytes()  # PIL Image
img_b64 = base64.b64encode(img_bytes).decode()

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=50,
    messages=[{
        "role": "user",
        "content": [
            {"type": "image",
             "source": {"type": "base64",
                        "media_type": "image/png",
                        "data": img_b64}},
            {"type": "text", "text": row["prompt"]},
        ],
    }],
)
print(response.content[0].text)

Task 1 — Production (text-description)

content = row["clock_description"] + "\n\n" + row["prompt"]
response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=50,
    messages=[{"role": "user", "content": content}],
)
print(response.content[0].text)

Task 2 — Motive elicitation

response = client.messages.create(
    model="claude-opus-4-6",
    max_tokens=200,
    messages=[{"role": "user", "content": row["prompt_motive"]}],
)
print(response.content[0].text)

Suggested metrics

• Production task: distribution distance from human productions (Wasserstein distance over precision codes); cross-context shift detection (does the model produce more rounding in neighbor vs. police context?); approximator usage rate.
• Motive task: label match against motive_labels (multi-label F1); qualitative analysis of free-text explanations.
• ESR / CDS: effect-size ratio and calibration distance score, as defined in Mühlenbernd (2026, CMCL).

Human baseline (RSA model)

A Rational Speech Act (RSA) speaker model fit to this dataset achieves r² ≈ 0.97 against human production distributions, providing a strong peer-reviewed reference baseline. See the companion repository for the model implementation.

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Baseline Results

Results from three vision-language models evaluated on the full dataset (n = 475), using evaluate.py. All numbers are from Task 1 (production task); Task 2 (motive elicitation) is not yet scored and open for contribution.

Finding 1 — Modality gap

Models read textual clock descriptions far more accurately than clock images. Accuracy here means the response correctly conveys the target time (exact digit or equivalent word form, e.g. "thirty-one" for 8:31); range-stimulus rows are excluded.

Model	Image accuracy	Text accuracy
GPT-4o mini	3.6%	19.9%
Claude Haiku 4.5	0.3%	44.8%
Gemini 2.5 Flash	18.3%	39.1%

Even the best image reader (Gemini, 18.3%) falls well short of its own text-description accuracy (39.1%). Claude Haiku essentially fails to read clock images (0.3%) yet reads textual descriptions at 44.8%. Open challenge: reliable analog-clock reading for current VLMs.

Finding 2 — Pragmatic shift

Humans reliably use more precise time expressions in the police context than in the casual neighbor context (cross-context Wasserstein distance WD = 0.27). Models show a much weaker shift or no shift at all, measured as the rate of digital-format responses ("8:31") per context.

Model	Police	Neighbor	Δ	WD (image)	WD (text)
GPT-4o mini	0.4%	4.5%	−4.1%	0.04	0.01
Claude Haiku 4.5	15.6%	1.2%	+14.4%	0.14	0.19
Gemini 2.5 Flash	30.7%	21.7%	+9.0%	0.09	0.12
Human baseline	—	—	—	0.27	—

GPT-4o mini inverts the expected direction (more digital in casual context). Claude Haiku and Gemini shift in the right direction but at roughly one-third to one-half the magnitude of the human effect. All model Wasserstein distances are well below the human baseline of 0.27. Open challenge: context-sensitive precision calibration at human magnitude.

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Prompts

Task 1 — Police context, precise stimulus

[clock stimulus here — image or description]

One morning when you leave your house, you witness an automobile accident
in your street. You look at your watch when it happens. Later that day you
are invited to the police station to give a formal witness statement about
the accident. The police officer is trying to establish a detailed timeline
of the event. He asks you: "What time did the accident happen?" You remember
that it happened at the time shown on the clock as given above.

How would you answer in this situation? (Fill the blank)

"It happened ___."

For the range stimulus, "at the time" is replaced by "in the time range." For the neighbor context, the police station passage is replaced by a party-at-a-neighbor's-house framing; "He asks" becomes "She asks."

Task 2 — Police context, precise stimulus (example)

One morning when you leave your house, you witness an automobile accident
in your street. You look at your watch when it happens. Later, you gave a
formal witness statement at a police station. The officer, trying to
establish a detailed timeline, asked you what time the accident happened.
You knew that the accident happened at 8:31, and your answer was
"It happened just after half past eight". Why did you choose to answer
this way?

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Caveats and Limitations

• Human baseline is image-only. Participants saw clock images; they did not see textual descriptions. Comparisons between text-description LLM outputs and human productions are meaningful but involve a modality translation step that should be flagged in analyses.
• Description format is a benchmark design choice. One canonical textual description format is fixed as part of the benchmark specification. Results may differ with alternative description strategies.
• Original stimulus wording. The original M&S 2022 experiment used "the clock on the left" (referring to GUI layout). This dataset uses "the clock as given above," which is layout-agnostic but a minor deviation from the source wording.
• Gendered interlocutors. The original stimuli used "He asks" for the police officer and "She asks" for the neighbor. These gender assignments are preserved here as faithful to the source experiment. Researchers should be aware of potential gender-stereotyping effects.
• Task 2 prompt is context-embedded. The original follow-up task gave participants only the minimal prompt ("In your task, you knew that…"). This dataset's prompt_motive embeds the police/neighbor context so single-turn LLM evaluation has access to the pragmatic framing. This is a deliberate design choice for LLM eval; the source wording is preserved in the paper.
• 5 rows have empty motive_text. Participants declined to respond on those items. prompt_motive is still valid; human-baseline comparisons for Task 2 simply have no ground-truth for those rows.
• English only. All productions are in English by English-speaking participants.

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Citation

If you use this benchmark, please cite the original paper:

@article{muehlenbernd2022imprecision,
  title   = {Modeling (im)precision in context},
  author  = {M{\"u}hlenbernd, Roland and Solt, Stephanie},
  journal = {Linguistics Vanguard},
  year    = {2022},
  doi     = {10.1515/lingvan-2022-0035}
}

Please also cite the source data:

@misc{muehlenbernd2022imprecision_data,
  title  = {Modeling (im)precision in context — supplementary data},
  author = {M{\"u}hlenbernd, Roland and Solt, Stephanie},
  year   = {2022},
  doi    = {10.6084/m9.figshare.21629531}
}

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License

This dataset is released under CC BY 4.0, consistent with the source data license on figshare. You are free to share and adapt the material for any purpose, provided appropriate credit is given.

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Related Resources

• Companion repo (RSA model + analysis notebook): muehlenbernd/imprecision-in-context
• LLM social calibration paper (ESR/CDS metrics): muehlenbernd/llm-social-calibration · arXiv 2604.02512