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Continuous Interpretive Steering for Scalar Diversity

Paper · 2026 · Ye-eun Cho

Summary

Scalar implicature varies in strength across lexical items. Some reliably suggests "not all," while warm rarely suggests "not hot." This item-level variation is called scalar diversity. Most LLM evaluations of pragmatics change prompts and ask whether a model gives the pragmatic reading at all, which is fragile and ignores this variation.

The author proposes Continuous Interpretive Steering (CIS). It adds a fixed direction to multi-layer hidden representations and treats the steering coefficient as a continuous experimental variable rather than a one-off control knob.

To test it, the paper builds GraSD. It merges weak/strong scale pairs from four psycholinguistic studies, grades them A–E by implicature strength, and expands them with GPT-4o into anchor, logical and pragmatic sentence variants.

Results on four open models:

  • Uniform steering pushes every item toward pragmatic readings by roughly the same large amount, which erases item differences.
  • Graded steering produces shifts that rank-correlate with the established grades.

The work suggests that graded pragmatic sensitivity is present in the representation space. It also shows that aggregate gains alone are a poor sign of real pragmatic competence.

Key points

  • GraSD combines scale pairs from four sources:

    • 43 pairs drawn from van Tiel et al. 2016
    • 43 pairs drawn from Ronai & Xiang 2021
    • 50 pairs drawn from Pankratz & Van Tiel 2021
    • 60 pairs drawn from Ronai & Xiang 2024

    After removing duplicates, 121 <weak, strong> pairs remain. GPT-4o generated 100 contextual instances per pair, giving 121,000 instances.

  • Each item has three variants:

    • an anchor with the weak term ("Some pets…"),
    • a logical variant with the strong term ("All pets…"),
    • a pragmatic variant that negates the strong term ("Not all pets…").

  • Steering is applied as h′(x) = h(x) + αv. Here h(x) concatenates hidden states from k layers, and one shared vector v is computed once for all inputs. The intervention operates on extracted representations, not inside the forward pass.

  • The models tested are LLaMA3, Qwen2, Gemma2 and OLMo. Both steering regimes give significant Wilcoxon signed-rank shifts from baseline. Only graded steering shows significant Spearman correlations between per-item change and the A–E grades.

  • Limitations stated by the author:

    • only scalar implicature is studied;
    • the direction is estimated from a small subset of instances;
    • interpretive preference is measured through representational similarity, not explicit behavioral judgments.

    Data, code and results are public.

Interesting ideas

  • I1. Three-way minimal variants (weak anchor, strong logical reading, negated-strong pragmatic reading) make an interpretation measurable by asking which variant the anchor is closer to. This turns a vague notion of "reading" into a comparison between explicit candidates.
  • I2. Implicature strength is treated as a graded per-item property, ordered A–E from human data, rather than a yes/no rule.
  • I3. An intervention's strength is varied like a dose, and success is judged by whether per-item effects rank-correlate with an independent hierarchy, not by mean improvement.
  • I4. Significant aggregate shifts can hide a homogeneous blanket push. Plotting per-item change distributions separates indiscriminate change from structured change.
  • I5. Data was augmented by an LLM under explicit theory-derived constraints, keeping the fixed relation between variants while varying context. This sits between rigid templates and free generation.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): The implicature module in prag could generate both the logical and the enriched frame for a scalar utterance and score which one the context supports, keeping both candidates visible for explanation.
  • I2 (world): The compiled lexical knowledge could store scales such as <some, all> or <warm, hot> with a strength grade, so downstream reasoning applies weak enrichments cautiously instead of uniformly.
  • I3 (learn): When testing a new repair rule or teacher tier, check that its effect scales with an independent difficulty or strength ordering, which works as a sharper control than a single aggregate gain.
  • I4 (learn): Evaluation reports could include histograms of per-item deltas, flagging rules that improve everything equally as possible over-generation rather than real competence.
  • I5 (learn): LLM teacher tiers could produce silver test items under linguistic constraints, such as preserved scale forms, giving varied yet controlled material for training and probing.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Sungkyunkwan UniversitySuwon · South Korea9 works · 195 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Implicature · 138 works

Cite

Cite as Ye-eun Cho. 2026. Continuous Interpretive Steering for Scalar Diversity. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.577

@inproceedings{cho2026continuous,
  title = {{Continuous Interpretive Steering for Scalar Diversity}},
  author = {Ye-eun Cho},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.577},
  url = {https://doi.org/10.18653/v1/2026.acl-long.577}
}

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