Language and cultural evolution

Últimos artículos

• Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages

  Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe · 2 de octubre de 2026

  Some properties of languages, e.g., subject-object-verb (SOV) word order, are more prevalent than others among the thousands of attested natural languages (NLs). Such typological commonality is often attributed to learning biases. Computational simulations, recently with language models (LMs), have …

• Invariant Atoms: Sparse Coordinates of Local Semantic Geometry in Language Model Representations

  Muhammad Ahtesham, Xin Zhong · 1 de octubre de 2026

  Large language models often preserve meaning despite substantial changes in wording, style, and syntax, while small semantic edits can systematically alter their hidden representations. This suggests that semantic variation may be organized along recurring local directions. We propose the Invariant …

• Co-Linguistics: AI-augmented Theory Construction in Linguistics

  Emmanuel Chemla, Benjamin Spector, Alexandros Kalomoiros, Philippe Schlenker · 1 de octubre de 2026

  LLMs have been studied in recent linguistics as potential models of humans' linguistic abilities. Here we discuss an entirely different use of AI, namely as a co-scientist, to help construct and assess linguistic theories (we refer to the result as "Co-Linguistics"). Since the 1960s, linguistics has…

• Geometric Representations of African Languages: A Regional Semantic Hub and Cultural Steering

  Muhammad Abdullahi Said, Jonathan Shock · 1 de octubre de 2026

  We study how Gemma 4 31B represents African languages and responds to cultural steering. The first study compares nine African languages and three controls using probes, contrast directions, and measures of representation similarity. Transfer from English varies across languages and layers. Directio…

• ETHER: Aligning Emergent Communication for Hindsight Experience Replay

  Kevin Yandoka Denamgana\"i, Daniel Hernandez, Ozan Vardal, Sondess Missaoui, James Alfred Walker · 1 de octubre de 2026

  Hindsight Experience Replay (HER) enhances sample efficiency in goal-conditioned reinforcement learning (RL) by relabelling failed trajectories with goals that were actually achieved. However, HER assumes access to a goal relabelling function and a predicate function that determines whether a goal h…

• Semantic Projection for Continual Self-Evolution of Language Agents

  Ziyu Liu, Jun Chen, Lixu Wang · 30 de septiembre de 2026

  Language-model agents increasingly rely on persistent natural-language skills to adapt beyond their frozen model parameters. When a shared skill is repeatedly revised from a non-stationary, heterogeneous task stream, however, improvements for new tasks can overwrite procedures needed for earlier one…

• LLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks

  Julia Witte Zimmerman, Calla G. Beauregard, Tabia Tanzin Prama, Parisa Suchdev, Kathryn Cramer, Elisabeth Kollrack · 29 de septiembre de 2026

  The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they cannot move beyond statistical pattern matching into abstraction or reasoning, remaining ontologically near the lower bound of pattern reuse despite producing a…

• Coupled Usage-Sense Processes: Temporal and Attributable Lexical Semantic Change

  Haruka Ezoe, Ryohei Hisano · 28 de septiembre de 2026

  Lexical semantic change is usually summarized by a scalar distance between independently sampled period distributions. This measures how much a word changed, but does not reveal when it changed, which mechanisms and component movements carried the change, or which usages support the attribution. We …

• What a Cross-Model Fixed-Point Census Can and Cannot Arbitrate About Repetition

  Nicol\'as Vera Z\'u\~niga · 25 de septiembre de 2026

  Two accounts of neural text degeneration coexist. One locates the cause in the training data -- repetition in the corpus produces repetition in the output, established by training on repetition-sorted data -- the other in the trained network, in copying circuits and repetition features. Neither has …

• HiPACE: Hierarchical Phase-Boundary Analysis and Controlled Evaluation of Feature Absorption in Sparse Autoencoders

  Jinyuan Zhang, Peng He, Yin Yuan, He Hu, ShengShuo Jiao · 25 de septiembre de 2026

  Sparse autoencoders (SAEs) decompose LLM activations into sparse dictionary atoms, so that each distinct concept gets its own feature. One recurring behavior complicates this premise: feature absorption, in which a parent concept and its children--fruit and {apple, banana, pear}, say--collapse into …

• Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment

  Suqin Yuan, Runqi Lin, Muyang Li, Guanzhe Hong, Jindong Gu, Lei Feng, Chris Russell, Tongliang Liu · 23 de septiembre de 2026

  Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses people prefer from an AI need not be the responses they themselves would give. We distinguish alignment with human preferences from alignment with human …

• From Utterances to Networks: Modelling Slang Adoption and Diffusion Across Subreddits

  Xiaoning Wang, Ted Underwood, Zhewei Sun · 23 de septiembre de 2026

  Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phrases such as Canon Event meaning an embarrassing but pivotal event, go viral online, it becomes increasingly important to…

• A Computational Approach to Measuring Semantic Change in Sanskrit Literature

  Tanay Agrawal · 23 de septiembre de 2026

  Diachronic word embeddings have become the modern standard for tracking semantic change, yet they have been largely validated on modern, high-resource, and well-segmented languages. This paper tests whether the paradigm transfers to Sanskrit, an ancient, low-resource language whose phonological fusi…

• AhaBench: Do Agents Turn Experience into Reusable Insights? A Long-Horizon Benchmark for Continual Learning

  Zerui Cheng, Jiawei Xu, Huacan Chai, Jiayang Sun, Pramod Viswanath, Maxm Pan · 22 de septiembre de 2026

  Can language agents continually learn from experience, turning earlier interactions into reusable capabilities? AhaBench evaluates this ability through exploration after solved hidden-state puzzles, computational transfer after mathematical teaching, and sustained business operation under delayed fe…

• Contributions to the hierarchy of probabilistic languages

  Lothar Sebastian Krapp, Remo Nitschke · 22 de septiembre de 2026

  We reconsider the theory of probabilistic formal languages generated by n-gram models and by probabilistic context-free grammars (PCFGs). The expected hierarchy of probabilistic grammars is established by proving that every probabilistic language generated by an n-gram model is also generated by som…

• A Ticket from Marginals to Joints: Coupled-Noise Distillation for One-Step Block Generation in Diffusion Language Models

  Lin Yao · 21 de septiembre de 2026

  Diffusion language models (dLLMs) predict all tokens of a block in parallel, but a single forward pass samples each position from its own marginal distribution, so the tokens need not form a coherent block. We ask whether a discrete masked model can commit an entire block in one pass when its mask e…

• Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations

  Yuqing Zhang, Tessa Verhoef, Gertjan van Noord, Arianna Bisazza · 18 de septiembre de 2026

  Given various grammatical options, language users prefer the word order choice that reduces the overall length of syntactic dependencies, a principle known as dependency length minimization (DLM). The origins of this preference remain an open question, particularly whether it originates from constra…

• Embedding Models Measure in Peculiar Ways

  Juri Opitz, Andrianos Michail · 18 de septiembre de 2026

  Embedding spaces define notions of semantic similarity and distance. We study whether those embeddings reflect physical measurements of mass, distance, time and volume, which admit a unique, objective notion of semantic equivalence and distance. We find that physical measurement is only weakly model…

• Foundations of Stochastic Lexical Calculus: Semantic Descent and Random Dynamics on Probability Simplices

  Matthew F Dixon · 18 de septiembre de 2026

  Large language models produce prompt-dependent probabilities over words, whereas scientific systems require uncertainty over meaningful states that can be updated as evidence arrives. We develop an observable framework for determining when language-derived probabilities support such a sequential sta…

• Reduplicative constructions in Mandarin: Socio-emotional profiling through distributional semantics

  Chaoyi Wu, Yu-Hsiang Tseng, R. Harald Baayen · 16 de septiembre de 2026

  Mandarin Chinese has two productive reduplicative constructions that repeat either two-character base words or their constituents (e.g., `in good health', `discuss a bit'). Their varied meanings have been described as realizing plurality, valence coloring, sound symbolism and pragmatic functions. Th…

• A Data-free Universal Prior over Syntactic Structures

  Ferm\'{\i}n Moscoso del Prado Mart\'{\i}n · 16 de septiembre de 2026

  Probability is fundamental to theories of language comprehension, production, acquisition, and evolution, as well as to large language models. Existing theories estimate the probability of syntactic structures from language-specific data. Whether part of this probability structure can arise independ…

• Convergent Emergence of In-Context Learning Across Modalities

  Nathan Breslow, Seungwook Han, Daniel Hyunsoo Lee, Aayush Mishra, Anqi Liu, Daniel Khashabi · 15 de septiembre de 2026

  Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them to new inputs, has been extensively studied in large language models trained for next-token prediction on human text. Recently, few-shot ICL has bee…

• Type Diversity Enables Transformers to Generalise Compositionally

  Anssi Moisio, Mathias Creutz, Mikko Kurimo · 14 de septiembre de 2026

  Compositional generalisation has been divided into lexical and structural generalisation. Previous work has found that structural generalisation is harder than lexical for Transformers. We propose that this difference is not inherent to Transformers, but due to the high diversity of lexical types an…

• CHRONOBERG: Capturing Language Evolution and Temporal Awareness in Foundation Models

  Niharika Hegde, Subarnaduti Paul, Lars Joel-Frey, Manuel Brack, Kristian Kersting, Martin Mundt, Patrick Schramowski · 11 de septiembre de 2026

  Large language models (LLMs) excel at operating at scale by leveraging social media and various data crawled from the web. Whereas existing corpora are diverse, their frequent lack of long-term temporal structure may however limit an LLM's ability to contextualize semantic and normative evolution of…

• A Fragility Spectrum for Recursive Language-Model Training

  Yangze Liu, Zhongyi Han · 11 de septiembre de 2026

  Model-generated text is finding its way back into training corpora, and there is plenty of evidence that training on such data over and over collapses output diversity. Prior work has studied the phenomenon itself: which protocols and which data mixtures cause collapse. But different models behave v…