The paper investigates how restricting a language model’s view to isolated subtask contexts—via recursive language models—affects learning compared to standard chain‑of‑thought (CoT) approaches. While CoT can simulate recursive reasoning and offers similar in‑distribution performance, it tends to exploit contextual shortcuts that fail out of distribution; recursive models avoid this by isolating context, thereby preventing such shortcuts. The study shows that merely covering the correct rule is insufficient for robust reasoning beyond the training distribution.
By Chenxiao Yang, Zhiyuan Li, David McAllester, Nathan Srebro
arXiv:2609.37635v1 Announce Type: new
Abstract: LLMs have been studied in recent linguistics as potential models of humans' linguistic abilities. Here we discuss an entirely different use of AI, name...
By Emmanuel Chemla, Benjamin Spector, Alexandros Kalomoiros, Philippe Schlenker
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.
By Akihiro Maeda, Thomas Seiller, Yohei Oseki
RAG‑RST is a retrieval‑augmented framework that supplies a formal discourse schema as in‑context demonstrations for Rhetorical Structure Theory parsing. By retrieving exemplar EDU pairs, it improves nuclearity F1 from 0.794 to 0.846 and relation F1 from 0.738 to 0.788 without any training. Two configurations—precomputed and dynamic—both outperform existing RST parsers, with the dynamic variant consistently strongest, and the approach shows that a formal linguistic schema can be provided to a foundation model as a demonstration rather than encoded in its parameters.
By Mohammadreza Sediqin, Shlomo Engelson Argamon
arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.
By Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Emmanuel Malherbe, C\'eline Hudelot, Pierre Colombo
Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universals models either lexicons with fixed signal structures or holistic composition without interpretable lexical parts. We introduce a framework that integrates this fundamental insight of formal semantics in evolutionary modeling, by allowing lexical meanings and a composition function to co-evolve under pressures for conceptual simplicity and communicative accuracy.
arXiv:2510.12306v4 Announce Type: replace
Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic...
By Cameron Morin, Matti Marttinen Larsson
arXiv:2610.03551v1 Announce Type: new
Abstract: Large language models (LLMs) have reached expert-level performance on competition mathematics largely through the volume of search placed around them:...
By Yanli Wang, Suijin Wang, Xiaopeng Yuan, Haohan Wang
arXiv:2606.27228v2 Announce Type: replace
Abstract: Formal semantics has shown that sentence meanings arise by recursively composing lexical meanings, yet much of the literature on semantic universal...
By Fausto Carcassi
arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.
By Nasim Shirvani-Mahdavi, Chengkai Li
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.
By Iskar Deng, Shane Steinert-Threlkeld
The paper introduces the "Prompt Lottery" phenomenon, where paraphrastic rephrasings of judge prompts in LLM-as-a-Judge (LLMaJ) lead to inconsistent judgments and can even reverse model rankings. A large-scale study across various tasks, judge families, and model backbones demonstrates that these prompt variations significantly alter evaluation outcomes. A user study shows that LLMaJ judges are 6–9 times more sensitive to prompt rephrasings than humans, and that aggregating multiple prompts improves robustness by focusing on consistent judgments.
By Ilija Subasic, Brett Levert, Andrew Rabinovich, Sajjad Pakdamansavoji, Zhao Chen