"Don't Say It!": Constraints, Compliance, and Communication when Language Models Play Taboo

Daniel Scalena; Francesca Padovani; Malvina Nissim; Sara Candussio

arxiv: 2607.00601 · v1 · pith:O6YKEDMMnew · submitted 2026-07-01 · 💻 cs.CL

"Don't Say It!": Constraints, Compliance, and Communication when Language Models Play Taboo

Sara Candussio , Francesca Padovani , Daniel Scalena , Malvina Nissim This is my paper

Pith reviewed 2026-07-02 13:10 UTC · model grok-4.3

classification 💻 cs.CL

keywords Taboo gamelanguage modelslexical constraintscommunicative effectivenesscomplianceconstrained generationLLM evaluation

0 comments

The pith

Language models balance Taboo rule compliance and description effectiveness differently by intervention type but remain weaker than humans at guessing.

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

The paper tests how language models handle the Taboo game, where players must describe a target word without using forbidden terms. It applies interventions at prompting, generation constraints, and internal representations, then measures both rule violations and how well descriptions help evoke the target for guessers. Results show that stricter controls shift the balance between following rules and producing useful descriptions, with models underperforming humans as guessers. This highlights that current models still face difficulties with precise lexical choices under limits.

Core claim

When language models play Taboo, compliance with forbidden-word rules and communicative effectiveness trade off differently across prompting, generation-time constraints, and internal representation manipulations, while models remain substantially weaker than humans as guessers, indicating that lexical grounding under constraint is an open challenge.

What carries the argument

The Taboo game evaluated through forbidden-word violation detection and LLM-as-a-judge scoring of how well descriptions evoke the target concept for human and machine guessers, with interventions applied at prompting, generation, and representation levels.

If this is right

Compliance and communicative effectiveness trade off differently depending on whether constraints are applied via prompting, generation-time rules, or internal representation changes.
Models perform substantially worse than humans when trying to guess the target word from constrained descriptions.
Lexical grounding under constraint remains an open challenge for current language models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Similar constraint-handling difficulties may appear in other tasks that require avoiding specific vocabulary while conveying meaning.
Training approaches focused on explicit constraint satisfaction during generation could narrow the gap with human performance.
The observed trade-offs suggest that internal model adjustments might offer different control levers than surface-level prompting.

Load-bearing premise

The LLM-as-a-judge metric and human comparisons accurately measure genuine communicative success rather than artifacts of the evaluation setup or prompt design.

What would settle it

A controlled experiment in which humans directly guess target words from the model's generated descriptions and their success rates are compared to both the LLM-as-a-judge scores and human player baselines.

Figures

Figures reproduced from arXiv: 2607.00601 by Daniel Scalena, Francesca Padovani, Malvina Nissim, Sara Candussio.

**Figure 2.** Figure 2: Compliance breakdown by method and model. Bars are partitioned into mutually exclusive categories summing to 100%; outputs with multiple error types are grouped into Mixed. The Baseline frequently violates taboo rules, while prompting substantially reduces errors. Constrained decoding achieves near-perfect compliance by construction, with residual failures almost exclusively due to target-word leaks. For g… view at source ↗

**Figure 3.** Figure 3: Compliance vs. pass@10 across models, methods, and evaluators. All three proposed methods (Prompt, Constrained, SAE) substantially increase compliance over the Baseline, confirming their effectiveness at enforcing the Taboo constraint. Pass@10 remains broadly stable across methods, suggesting compliance gains do not come at the cost of description quality. Gemma, in the guesser role, consistently achieves … view at source ↗

**Figure 4.** Figure 4: Pass@𝑘 guessing accuracy on 10 shared Taboo cards, for all clue-givers evaluated by openai/gpt-oss-20b (left) and google/gemma-3-4b-it (right). Each cell reports the fraction of cards for which the judge ranked the target word within its top 𝑘 guesses. is 0.0% while pass@10 reaches 30.0%. The same asymmetry holds when models guess from model-written descriptions, with Claude Sonnet-4.6 and Gemini-3.1-Pro a… view at source ↗

read the original abstract

The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple task combines strict lexical constraints with the need for communicatively effective descriptions, making it a compelling playground for examining how LLMs navigate competing demands at inference time. We evaluate two open-weight models under conditions that intervene at progressively deeper levels of the generative process, from prompting to generation-time constraints to internal representations manipulations. We assess their outputs through forbidden word violation detection, LLM-as-a-judge measuring the degree to which generated descriptions successfully evoke the target concept for both human and machine guessers, and examining whether the strategies models adopt under constraint align with those of human players. Our results show that compliance with the rules of the game and communicative effectiveness trade off differently across conditions, and that models remain substantially weaker than humans as guessers, suggesting that lexical grounding under constraint is an open challenge for current language models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper shows LLMs trade off compliance and effectiveness differently across intervention depths in Taboo but lag humans, with the main open question being whether the LLM judge is calibrated.

read the letter

The core finding is that intervening at prompting, generation constraints, or representation level changes how models balance rule-following with useful descriptions in Taboo, and that models still guess worse than people. The setup itself is the main new piece: taking an established game and layering interventions at different stages of generation, then measuring both violations and communicative success.

The work does a clean job of comparing those conditions side by side and including a human baseline for guessing ability. That gives a concrete picture of where current models sit on lexical grounding under hard constraints.

The soft spot is the reliance on an LLM-as-a-judge for communicative success. The abstract gives no sign that this judge was checked against held-out human guessers or tested for sensitivity to prompt wording or forbidden-word lists. If the judge favors unconstrained outputs or underrates constrained ones, the reported trade-offs and human gap become harder to interpret. Sample sizes, exact word lists, and any statistical tests are also not mentioned, so the strength of the differences is difficult to assess from what is shown.

This is useful for people working on constrained generation, safety filters, or evaluation of communicative ability in LLMs. Readers who care about how models handle simultaneous lexical and functional demands will find the testbed worth looking at. The question is relevant and the experimental framing is honest, so it deserves a serious referee even though the metric validation needs to be tightened.

Recommendation: send it to review.

Referee Report

1 major / 1 minor

Summary. The manuscript evaluates two open-weight language models on the Taboo game, which requires generating descriptions of a target word while avoiding a set of forbidden words. Interventions are applied at prompting, generation-time constraints, and internal representation levels. Outputs are assessed via forbidden-word violation detection and an LLM-as-a-judge metric that scores how successfully descriptions evoke the target for human and machine guessers; model strategies are also compared to those of human players. The central claims are that compliance and communicative effectiveness trade off differently across the tested conditions and that models remain substantially weaker than humans as guessers, indicating that lexical grounding under constraint is an open challenge.

Significance. If the results hold after addressing evaluation concerns, the work offers a structured empirical probe of how LLMs navigate competing lexical constraints and communicative goals at inference time. The progressive intervention design (prompting through representation manipulation) is a constructive way to isolate effects, and the direct human comparison provides a clear benchmark. No machine-checked proofs or parameter-free derivations are present, but the multi-condition empirical setup is a strength for testing claims about trade-offs.

major comments (1)

[Abstract and Evaluation sections] The strongest claims—that compliance-effectiveness trade-offs differ across conditions and that models are substantially weaker than humans as guessers—rest entirely on the LLM-as-a-judge scores for communicative success. The manuscript reports no calibration of this judge against held-out human guessing data, no inter-annotator agreement with human raters, and no sensitivity analyses to prompt wording or forbidden-word list variations. Without such validation, it is impossible to determine whether the reported trade-offs and human-model gap reflect genuine differences in lexical grounding or artifacts of the automated metric.

minor comments (1)

[Abstract] The abstract omits sample sizes, statistical tests used for the trade-off comparisons, the exact composition of forbidden-word lists, and the number of human participants, all of which are needed to assess the reliability of the quantitative claims.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on our evaluation approach. We address the major comment point by point below.

read point-by-point responses

Referee: [Abstract and Evaluation sections] The strongest claims—that compliance-effectiveness trade-offs differ across conditions and that models are substantially weaker than humans as guessers—rest entirely on the LLM-as-a-judge scores for communicative success. The manuscript reports no calibration of this judge against held-out human guessing data, no inter-annotator agreement with human raters, and no sensitivity analyses to prompt wording or forbidden-word list variations. Without such validation, it is impossible to determine whether the reported trade-offs and human-model gap reflect genuine differences in lexical grounding or artifacts of the automated metric.

Authors: We concur that validating the LLM-as-a-judge metric against human data is essential for robust claims. Although our metric is used uniformly to compare conditions and the human-model gap is corroborated by direct human guessing performance and strategy alignment analyses, we did not include calibration, IAA, or sensitivity checks in the submitted manuscript. We will revise the manuscript to incorporate a calibration study on held-out human guessing data, report inter-annotator agreement, and perform sensitivity analyses on prompt wording and forbidden-word variations to ensure the results reflect true differences rather than metric artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation with no derivations or self-referential fitting

full rationale

The paper is an empirical study comparing LLMs on the Taboo game under various interventions. It reports results from violation detection, LLM-as-a-judge scoring, and human comparisons, with no equations, parameter fitting, derivations, or self-citation chains that reduce claims to inputs by construction. The reader's assessment of score 0.0 is consistent with the absence of any load-bearing mathematical or definitional steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical evaluation study; the central claim rests on no free parameters, mathematical axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5705 in / 965 out tokens · 19565 ms · 2026-07-02T13:10:07.051497+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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