The paper proposes a new way to evaluate compositional generalization by examining which structural or lexical identifications allow held‑out COGS examples to be considered admissible based on training data. Sentences are modeled as functors from syntactic addresses to lexical tokens, and selective collapses induce Kan extensions that propagate observed associations. Across 21 COGS generalization types, admissibility follows distinct identification profiles, while residual failures highlight unsupported structural templates, providing data‑side diagnoses of what the training corpus licenses without training a predictive model.
arXiv:2604. 26157v4 Announce Type: replace-cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations.
The paper investigates why Transformers struggle more with structural than lexical compositional generalisation. It argues that this disparity stems from low structural type diversity rather than an inherent limitation of Transformers. By creating linguistically diverse variants of the COGS and SLOG datasets, the authors show that type diversity correlates equally with generalisation in both lexical and structural cases, challenging previous explanations of the difficulty.
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that only same-category constituents can be conjoined, which has been challenged by the many grammatical unlike coordinations found in natural language.
The study investigates how part‑of‑speech (PoS) categories are represented in the latent space of Sparse AutoEncoders (SAEs) applied to language models. Results show that PoS distinctions can be reliably recovered from SAE activations, but the mapping is not one‑to‑one; instead, PoS categories are supported by compact, distributed groups of sparse latents that vary across tags and remain stable on held‑out data. The findings suggest that SAEs encode morpho‑syntactic information in a distributed, category‑dependent manner rather than through isolated grammatical features.
The paper investigates how verb–noun decomposition, a common strategy for recognizing assembly actions, generalizes to novel combinations of familiar components. Through a systematic study on three datasets (MECCANO, HAViD, and IMPACT), the authors find that while decomposition avoids the zero‑probability ceiling of atomic classifiers, its performance still heavily depends on the co‑occurrence patterns seen during training. The analysis reveals that errors concentrate on the larger‑vocabulary component, that shared‑encoder training can entangle components and worsen generalization, and that these issues stem from primitive support, vocabulary asymmetry, and component entanglement.
arXiv:2606. 00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs.
arXiv:2610.35111v1 Announce Type: new Abstract: Large language models operationalize an intuitive, view of meaning as use. We argue that it faces a rule-following challenge. Using Kripke's interpreta...
The paper examines how language models learn action nominal constructions (ANCs) across 96 synthetic target languages with varied grammatical features. Using GPT‑2‑small trained from scratch, the study evaluates models on minimal pairs covering 34 grammatical phenomena, revealing that models mirror typological patterns in basic clause configurations but diverge in complement systems and ANC strategies. The findings suggest that natural language distributions may arise from both general learning pressures and functional demands not fully captured by current model experiments.
arXiv:2607. 18961v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix.
arXiv:2610.16837v1 Announce Type: cross Abstract: Syntactic language models (SLMs) that generate sentences along with their syntactic tree structures have shown promise in both syntactic generalizati...
arXiv:2610.01428v1 Announce Type: cross Abstract: Generalization in large language models (LLMs) is the ability to produce consistent and semantically stable outputs when the same input is expressed...
The study investigates whether pretrained transformer models encode functional words—such as pronouns and adverbs—in a way that mirrors human usage. By comparing embeddings of nouns with those of their functional counterparts in both isolated and parallel sentences, the authors find that functional words occupy a central yet distinct position in embedding space and that parallel lexicalized and functional sentences reside in different subspaces. Experiments show that only a mixed training set of functional and lexicalized sentences reveals shared syntactic and semantic structure, whereas training on either type alone fails to capture this parallelism.