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Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper argues that instruction-tuned LLMs, although able to separate broad uses, fail to reliably distinguish the fine-grained discourse-particle senses of the English word 'just' in naturalistic text.

desk verdict A solid, honest study of LLM sensitivity to the senses of 'just' whose main conclusion is plausible but currently over-claimed because the gold labels are not shown to be reliable. read the letter →

arxiv 2506.04534 v1 pith:O5K4FYWI submitted 2025-06-05 cs.CL cs.AI

William Sheffield , Kanishka Misra , Valentina Pyatkin , Ashwini Deo , Kyle Mahowald , Junyi Jessy Li This is my paper · ORCID
classification cs.CLcs.AI
keywords discourseparticlesjust(English)LLMsensedisambiguationformalsemanticspragmaticspolyfunctionalitymetalinguisticpromptingOpenSubtitles
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper asks whether instruction-tuned large language models know the fine-grained senses of the English discourse particle 'just'—the exclusive, unelaboratory, unexplanatory, emphatic, temporal, and adjective uses that formal semantics distinguishes. Using expert-constructed and expert-annotated sentences, it shows that models can tell apart the broad categories (adjective and temporal) but do not reliably identify the four nuanced discourse-particle senses, even when sense definitions and examples are provided in the prompt. On naturalistic movie-subtitle sentences, accuracy falls close to chance for most models, and adding conversational context does not help. A pairwise judgment task confirms the pattern: models separate clearly distinct senses like the controls 'bat' and 'bank', but only weakly separate the target senses. If right, this means current LLMs lack an internalized, fine-grained semantics for a frequent and discourse-critical word.

What carries the argument

Two probing tasks carry the argument. The first is a metalinguistic sense-labeling probe: the model assigns one of six sense labels by taking the label with the highest conditional probability after a prompt that defines each sense with examples. The second is a pairwise same-use probe: for each pair of sentences the model rates whether the two occurrences of 'just' are used the same way, and the normalized log-probability difference between 'Yes' and 'No' forms a heatmap whose block structure measures sense separation. The load-bearing resources are the two expert-labeled datasets—90 hand-constructed unambiguous sentences and 149 OpenSubtitles sentences—and the control items ('bat', 'bank', adjective and temporal senses) that show the probes can detect clear distinctions when they exist.

What would settle it

Have independent semanticists label the 90 hand-constructed and 149 subtitle sentences and calculate inter-annotator agreement; then score models against each annotator's label rather than the majority. If model accuracy equals the human annotation ceiling on ambiguous items, or if the expert labels themselves diverge on a large share of sentences, then the near-chance performance is a labeling artifact rather than a semantic deficit in the models.

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Extended reading notes

Core claim

The central discovery is a measured gap between the coarse and fine senses of 'just' in LLMs. In the labeling task, models are scored by the conditional probability of each sense label; all models except the 1B one beat chance on 90 hand-built unambiguous sentences, but accuracy drops by about 0.24 on 149 subtitle sentences and does not recover when two previous utterances are added as context. In the pairwise task, the model compares sentences and gives the difference in log-probability of 'Yes' and 'No'; same-sense pairs are rated higher than different-sense pairs for every model except the smallest, but the effect sizes for the four target senses are small, while adjective, temporal, and the control words 'bat' and 'bank' separate cleanly. The authors read this as evidence that LLMs have a coarse, partially correct grasp of 'just' but have not internalized the discourse-particle distinctions that formal semantics identifies.

Load-bearing premise

The central claim rests on the assumption that every sentence in the two datasets has one correct intended sense of 'just'; if many sentences are genuinely ambiguous or the six-way taxonomy splits natural uses too finely, then low model accuracy could reflect disagreement in the labels rather than a real gap in the models' understanding.

Editorial extensions

If this is right

  • Scaling model size alone will not close the gap: 70B models perform near chance on naturalistic sentences and show only weak separation of the four target discourse senses.
  • Providing conversational context is not a cure; model accuracy did not improve and often fell when two prior utterances were added.
  • The pairwise same-use probe is sensitive enough to detect clearly separated senses (adjective, temporal, and the control words bat and bank), so the weak signals for 'just' point to a property of the particle's semantics rather than a broken measurement.
  • Systems that rely on LLMs for discourse interpretation—dialogue, summarization, reasoning about speaker intent—should expect errors where a polyfunctional particle like 'just' carries the meaning.
  • Because the task uses labels from formal semantic theory, the result also constrains what 'understanding' means for function words: coarse category knowledge can coexist with missing fine-grained internalized senses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The result likely extends beyond 'just' to other polyfunctional discourse particles such as 'actually', 'even', 'like', and 'though'; a direct test would be to rerun the same two probes on a small inventory of such words.
  • Because the paper notes that intonation often disambiguates 'just' in speech, text-only models may be operating with genuinely less information; an extension would be to test speech or prosody-aware models on the same sentences.
  • The absence of a context benefit may reflect the instruction-following setup rather than the models' internal representations; a direct sentence-surprisal measure could reveal sensitivity that metalinguistic prompting misses, a caveat the paper itself flags.
  • A human-model alignment analysis—scoring models by agreement with individual annotators instead of majority labels—would separate genuine semantic deficits from ambiguity in the gold standard.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper investigates whether instruction-tuned LLMs correctly distinguish the fine-grained senses of the English discourse particle "just" (exclusive, unelaboratory, unexplanatory, emphatic, plus temporal and adjective controls). Using 90 hand-constructed sentences and 149 OpenSubtitles sentences annotated by expert semanticists, the authors run two experiments: (1) prompted sense labeling with definitions and examples, and (2) pairwise judgments of whether two occurrences of "just" are used the same way. They report that most models exceed uniform chance on the hand-constructed data but perform near the majority-class baseline on naturalistic subtitles, that context does not help, and that pairwise separation is clear only for coarser senses such as adjective and temporal. The authors conclude that LLMs have basic but incomplete knowledge of discourse-particle senses.

Significance. If the results hold, this is a valuable behavioral case study with expert-constructed and expert-annotated data, two complementary experimental paradigms, and a cross-scale model comparison that includes both open and commercial-style instruction-tuned models. The explicit release of code and data is a strength, as are the bat/bank control experiments that validate the pairwise methodology. The paper extends prior work on discourse relations to a class of understudied function words, and its negative result is falsifiable. However, the central negative claim is load-bearing on the reliability of the gold labels and on the statistical treatment of small, imbalanced samples, both of which need strengthening before the conclusions can be considered airtight.

major comments (4)
  1. [Section 3 and §4.2] The subtitle-label ground truth is the load-bearing premise for the central negative result, but no inter-annotator agreement statistic is reported and the adjudication procedure is underspecified: the text states that a variable subset of eight annotators labeled each sentence and that when there was disagreement, "two additional senior annotators, whose labels were both considered regardless of agreement" were used, but it never explains how a single gold label was derived from this process. With only 149 items and a skewed distribution (60 Exclusive, 33 Temporal, 22 Unexplanatory, 21 Emphatic, 12 Unelaboratory, 1 Adjective), even a modest fraction of mislabeled items materially lowers the achievable accuracy ceiling: if 20% of gold labels are wrong, the ceiling is roughly 0.8, and a model scoring 0.45 could be near that ceiling. The authors should report agreement statistics (e.g., Fleiss' kappa), per-item confidence ratings, and a clear gold-label adjudication rule, and they should make the annotator-by-item label matrix available so that the ceiling is estimable.
  2. [Section 5.2, Table 5] The claim that all models except Llama-3.2-1B show "significant separation" (p < .005) rests on Welch t-tests computed on 1260 same-sense pairs and 6750 different-sense pairs, but these pairs are not independent: they share common sentences, so the effective sample size is far smaller than the pairwise counts and the p-values are anti-conservative. The same issue affects the reported Cohen's d effect sizes, which are not accompanied by confidence intervals. The authors should either use a cluster-robust test or a mixed-effects model with sentence- or pair-level random effects, or a permutation test that preserves the dependence structure, and they should report confidence intervals for the effect sizes. The qualitative heatmap patterns are suggestive, but the quantitative significance claim needs a sounder statistical basis.
  3. [Section 4.2, Figure 2] Accuracy is reported as point estimates without confidence intervals or explicit tests against the chance baselines. For the 149-item subtitle set, the difference between a model at 0.45 and the majority-class baseline of 0.403 may not be meaningful, and the claim that models are "at chance" for several conditions should be supported by binomial or bootstrap confidence intervals per model and per condition. Without such intervals, it is impossible to judge which model-by-condition differences are reliable, and the pattern of "near chance" on subtitles versus above-chance on hand-constructed data is not quantified in a way that supports the paper's central conclusion.
  4. [Section 3 and §5.2] The hand-constructed sentences are described as having been "carefully created by an expert" who is a graduate linguist and native speaker, but no second annotation, adjudication, or agreement measure is reported for these 90 items. Since the pairwise experiment in §5.2 relies entirely on these labels as ground truth, any ambiguous or mislabeled items would attenuate the measured same-sense versus different-sense separation, particularly for the fine-grained target senses that are the focus of the paper. A validation pass by a second annotator, or at least a public release of the items with confidence ratings, would strengthen the interpretation of the effect sizes in Table 5.
minor comments (4)
  1. [Appendix F, Figure 6] The sense-labeling prompt uses the label "Exclusionary" for what the rest of the paper calls "Exclusive"; the authors should harmonize this terminology to avoid confusion in reproducing the exact prompt.
  2. [Section 3] The sentence "strong speaker consensus on the reading of an occurrence of just does remove more ambiguous sentences from out data" contains a typo: "out data" should be "our data."
  3. [Section 5.2 and Table 5] The notation is inconsistent across model names (e.g., "Llama3.2 1B" in Table 5 versus "Llama-3.2-1b" in the text and figures); the authors should standardize model identifiers.
  4. [Figure 3] The "ideal" heatmap is embedded in the same row as the model heatmaps, which makes it easy to mistake it for a model output; placing it separately or adding a clear panel label would improve readability.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper's negative result is an empirical evaluation against external ground-truth labels; no prediction reduces to a fitted input or to a self-citation chain.

full rationale

The paper's central claim—that instruction-tuned LLMs only partially separate fine-grained senses of 'just'—is an empirical measurement, not a derivation. The sense taxonomy is taken from published formal-semantics literature (Lee, Warstadt, Beltrama, Coppock & Beaver), and the models' outputs are compared against expert-created ground-truth labels; the outcome (near-chance accuracy on subtitle items) is not built into the experimental setup, since the models could in principle have matched the labels. There are no fitted parameters, and no 'prediction' is a renamed input. The bat/bank controls provide an external check that the pairwise method can detect sense distinctions, which strengthens the conclusion rather than presupposing it. The only author-overlap citation that touches the design is Deo and Thomas (2025), a unified account of 'just' whose taxonomy is cited as background; the four target senses are additionally sourced to independent prior work (Coppock & Beaver 2014; Warstadt 2020; Wiegand 2018; Windhearn 2021; Lee 1987; Beltrama 2018, 2022), so the self-citation is not load-bearing. The reported absence of inter-annotator agreement on the subtitle labels is a validity concern about ground-truth reliability, not a circularity: labeling noise would depress accuracy for all models but is not a logical reduction of the paper's claim to its inputs. Score 1 reflects the minor background self-citation; there is no circular step.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

This study is empirical evaluation; it introduces no new theoretical entities and fits no numerical parameters. The central result rests on assumptions about the validity of the sense taxonomy, the uniqueness of the annotated readings, and the interpretability of the prompting protocol; these are listed as axioms.

assumptions (5)
  • domain assumption The six-way taxonomy of just senses (exclusive, unelaboratory, unexplanatory, emphatic, temporal, adjective) is a valid and applicable scheme for classifying occurrences of just.
    Section 2 defines the senses from formal semantics literature (Lee 1987; Warstadt 2020; Deo and Thomas 2025); the evaluation assumes these categories are the right ones to test.
  • domain assumption Hand-constructed sentences have a single strong primary reading without context.
    Section 3 states sentences were created "to have only one sense available" and that all have a strong primary reading; if this fails, the labeling task is ambiguous.
  • domain assumption Log-probability of label continuations measures the model's metalinguistic sense knowledge.
    Section 4.1 uses argmax over PM(l|S); Limitations acknowledges prompting may underestimate LLM ability (Hu and Levy 2023).
  • domain assumption Subtitle annotations by volunteer semanticists reflect consensus ground truth.
    Section 3: labels from a variable subset of 8 annotators, with senior annotators resolving disagreements; no inter-annotator agreement reported.
  • domain assumption In the pairwise test, 'Yes/No' judgments measure sense identity rather than general sentence similarity.
    Section 5.1; controls with bat and bank support this, but only for clearly separated senses.

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Cite this review

Pith. "Pith review of Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs." pith.science (2026). https://pith.science/paper/O5K4FYWI

@misc{pith2026250604534,
  author       = {Pith},
  title        = {Pith review of: Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O5K4FYWI}},
  note         = {Machine review of arXiv:2506.04534}
}
read the original abstract

Discourse particles are crucial elements that subtly shape the meaning of text. These words, often polyfunctional, give rise to nuanced and often quite disparate semantic/discourse effects, as exemplified by the diverse uses of the particle "just" (e.g., exclusive, temporal, emphatic). This work investigates the capacity of LLMs to distinguish the fine-grained senses of English "just", a well-studied example in formal semantics, using data meticulously created and labeled by expert linguists. Our findings reveal that while LLMs exhibit some ability to differentiate between broader categories, they struggle to fully capture more subtle nuances, highlighting a gap in their understanding of discourse particles.

Figures

Figures reproduced from arXiv: 2506.04534 by the authors.

Figure 1
Figure 1. List of just senses in declarative sentences (with target senses in blue). Note that all senses other than the Adjective just are discourse particle senses and function as adverbs. All examples save the Adjective ones come from Warstadt (2020). English just is a good candidate to study as it (1) has been thoroughly analyzed and (2) has many senses. Deo and Thomas (2025) present a uni￾fied account for all senses of t… view at source ↗
Figure 2
Figure 2. Model accuracies for the sense labeling task [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Heatmap of language model pairwise comparisons of the use of [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Accuracy of models for the sense labeling task [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Heatmap of language model pairwise comparisons of the use of "bat" (top) and "bank" (bottom). The [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Prompt for the "just" sense labeling task. When context is included for the subtitle data, the prompt is [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Prompt for the pairwise sense comparisons. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Annotation interface for labeling subtitle data. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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