{"id":"26dc3e9b-bedb-4955-8fa2-023a04ddb0a5","arxiv_id":"2606.02953","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Larger LLMs reproduce constructional productivity via entrenchment in coercion cases with nonce words but fail to use statistical preemption to avoid overgeneralizing semantically plausible but unobserved patterns.","lead":"The paper tests large language models on whether they show two opposing forces from usage-based grammar: entrenchment that supports creative language use and preemption that prevents overgeneralization from never-seen patterns. A smart generalist might read it to understand current limits in how AI handles novel but plausible language versus human-like caution from absence of evidence.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Nonce-word coercion tasks may not isolate preemption from model-specific generalization limits","rationale":"The reader's weakest assumption directly identifies the load-bearing point; without full-text experimental details the concern remains the mapping from task behavior to the claimed statistical forces, so the unverdicted status is appropriate.","tokens_in":1734,"tokens_out":267,"duration_ms":14657,"concrete_test":"Replicate the preemption condition using the exact nonce-word frames from the paper but with an additional control set of attested low-frequency items that are semantically infelicitous; if models still overgeneralize the infelicitous items at the same rate as the unattested ones, the negative-evidence claim is unsupported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim requires that failure to avoid overgeneralization on semantically felicitous but unattested patterns demonstrates absence of preemption, rather than insufficient negative evidence in training or inability of the architecture to represent 'never observed' as a distinct signal. The abstract and reader's weakest assumption both flag that the specific constructional frames and nonce-word prompts must be shown to be valid measures; if the models' responses instead track surface co-occurrence statistics or prompt artifacts, the dissociation between entrenchment and preemption does not follow.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims that LLMs exhibit entrenchment-driven constructional productivity (via coercion with nonce words) that scales with model size, but lack preemption effects from negative evidence, failing to block overgeneralization on semantically felicitous but unattested patterns; this dissociation is presented as holding across architectures and as evidence that statistical preemption does not constrain LLM productivity in the manner predicted by usage-based theories.","tokens_in":1836,"tokens_out":315,"duration_ms":22298,"significance":"If the dissociation is robustly demonstrated, the result would bear on whether LLMs implement the two distinct frequency signals posited in usage-based grammar, with potential implications for cognitive modeling of productivity. The nonce-word design is a standard tool for testing generalization and is a positive feature when properly controlled.","major_comments":[{"comment":"Abstract: results are asserted across architectures with no accompanying details on test constructions, statistical controls, sample sizes, or the operationalization of overgeneralization; without these elements the central empirical claim cannot be evaluated.","section":"Abstract"},{"comment":"The reported failure to avoid overgeneralization on unattested but felicitous patterns is taken to demonstrate absence of preemption; however, this inference requires showing that the nonce-word frames and prompting regime provide sufficient negative evidence and isolate preemption from architecture-specific limits on representing absence, which is not addressed.","section":"Experimental tasks"}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their detailed and constructive comments. We address each major comment point by point below, indicating planned revisions where appropriate.","responses":[{"response":"We agree that the abstract is highly condensed. The full manuscript specifies the constructions (coercion frames with nonce words), controls (model size and architecture comparisons), sample sizes (multiple LLMs and prompt variants), and operationalization (preference rates for attested vs. unattested patterns). We will revise the abstract to include a concise reference to these elements.","revision_made":"partial","referee_comment":"[Abstract] Abstract: results are asserted across architectures with no accompanying details on test constructions, statistical controls, sample sizes, or the operationalization of overgeneralization; without these elements the central empirical claim cannot be evaluated."},{"response":"The nonce-word coercion design follows established usage-based methods to supply contexts where preemption from negative evidence would be expected if utilized. Testing across architectures and sizes helps separate general statistical effects from model-specific constraints. We will add explicit discussion in the Methods and Discussion sections on the prompting regime's provision of negative evidence and note limitations in fully isolating preemption from representational factors.","revision_made":"yes","referee_comment":"[Experimental tasks] The reported failure to avoid overgeneralization on unattested but felicitous patterns is taken to demonstrate absence of preemption; however, this inference requires showing that the nonce-word frames and prompting regime provide sufficient negative evidence and isolate preemption from architecture-specific limits on representing absence, which is not addressed."}],"tokens_in":1273,"tokens_out":345,"duration_ms":24716,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main takeaway is that larger LLMs reproduce constructional productivity through entrenchment in coercion tasks with nonce words, but they do not use statistical preemption to block overgeneralization of unattested yet semantically fine patterns.\n\nThe paper does a straightforward job of taking usage-based ideas about frequency signals and running them on LLMs across scales. The reported split between the two effects is a concrete empirical claim that goes beyond restating prior linguistic work.\n\nThe soft spot is the lack of concrete experimental detail. The abstract gives no information on the actual test constructions, how responses were scored for overgeneralization, sample sizes, or controls for prompt artifacts. Until those are shown, it is hard to rule out that the missing preemption effect comes from the models treating the tasks as surface statistics rather than learning a distinct negative-evidence signal. The stress-test concern about whether nonce coercion isolates preemption looks like it needs direct checking in the full text.\n\nThis is aimed at people working on how LLMs acquire linguistic constraints and how that compares to human usage-based learning. The question is grounded enough and the result specific enough that it deserves a serious referee, even with likely requests for clearer methods and more controls.","headline":"LLMs show entrenchment effects on nonce coercion but no preemption on overgeneralization; the dissociation is interesting but the methods need more detail to hold up.","tokens_in":2319,"tokens_out":323,"would_cite":false,"duration_ms":18033,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Large language models capture entrenchment through coercion with nonce words but show no preemption from absent patterns.","keywords":["linguistic productivity","entrenchment","preemption","constructional coercion","nonce words","large language models","usage-based theories","overgeneralization"],"falsifier":"A controlled test in which models trained or prompted with explicit negative evidence for a semantically acceptable but unattested construction subsequently stop producing that construction at rates significantly above baseline.","tokens_in":2647,"feed_emoji":"","tokens_out":630,"duration_ms":18089,"temperature":0.7,"pith_summary":"Usage-based theories hold that language productivity is supported by frequent exposure to structures and limited by their consistent absence where expected. The paper tests whether these same frequency signals shape how LLMs generate and interpret language. Experiments show larger models can extend constructions to made-up words when context forces an atypical meaning, reproducing the entrenchment side of the theory. The same models nevertheless produce overgeneralizations of semantically acceptable patterns that never occurred in their training data, indicating they do not register or apply negative evidence in the way preemption requires.","feed_headline":"LLMs handle coercion but ignore preemption","feed_subtitle":"Larger models extend constructions to new words under context pressure yet still produce unattested but acceptable patterns.","key_machinery":"The contrast between entrenchment, driven by high-frequency usage of a construction, and preemption, driven by consistent non-occurrence in contexts where the construction might otherwise appear, tested through nonce-word substitution in coercion frames.","core_discovery":"Across model sizes and architectures, LLMs reproduce constructional productivity via entrenchment when a broader frame coerces an atypical reading of a nonce word, yet they continue to overgeneralize patterns that are semantically acceptable but unattested, showing that statistical preemption does not constrain their output.","pith_inferences":["This pattern suggests LLMs may need explicit mechanisms for registering negative evidence if they are to match human-like avoidance of certain generalizations.","The result points to a possible test: whether targeted exposure to unattested constructions paired with corrective signals reduces overgeneralization in subsequent generations.","It raises the question of whether other statistical or architectural features, beyond raw frequency counts, could supply the missing preemption effect."],"forward_implications":["Larger models increasingly exhibit entrenchment effects that allow coerced interpretations with novel lexical items.","Models of any size fail to block overgeneralization of unattested but semantically coherent patterns.","Statistical absence alone does not function as a learning signal for LLMs in the manner predicted by preemption accounts.","The dissociation between coercion success and preemption failure holds across different model architectures."],"fun_headline_variants":["LLMs coerce via entrenchment but ignore preemption","Larger models extend coerced constructions but overgeneralize","LLMs show productivity from coercion lacking preemption","Entrenchment succeeds in LLMs but preemption fails"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The specific nonce-word tasks and construction frames used here validly isolate the same entrenchment and preemption mechanisms that usage-based theories attribute to human speakers.","fun_headline_variants_meta":{"raw":{"variants":["LLMs coerce via entrenchment but ignore preemption","Larger models extend coerced constructions but overgeneralize","LLMs show productivity from coercion lacking preemption","Entrenchment succeeds in LLMs but preemption fails"]},"model":"grok-4.3","cost_usd":0.006624,"raw_usage":{"total_tokens":3062,"prompt_tokens":610,"num_sources_used":0,"completion_tokens":62,"cost_in_usd_ticks":66237000,"prompt_tokens_details":{"text_tokens":610,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2390,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":610,"tokens_out":62,"duration_ms":17573,"temperature":1.0,"reasoning_tokens":2390,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T14:11:06.475662+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled test in which models trained or prompted with explicit negative evidence for a semantically acceptable but unattested construction subsequently stop producing that construction at rates significantly above baseline.","supporting_citations":[],"review_version":1}