The Palladian grammar

Notes on the Synthesis of Form

Notes on Notes on the Synthesis of Form.

Categorial grammar

Categorial grammar is a family of formalisms in natural language syntax that share the central assumption that syntactic constituents combine as functions and arguments. Categorial grammar posits a close relationship between the syntax and semantic composition, since it typically treats syntactic categories as corresponding to semantic types. Categorial grammars were developed in the 1930s by Kazimierz Ajdukiewicz and in the 1950s by Yehoshua Bar-Hillel and Joachim Lambek. It saw a surge of interest in the 1970s following the work of Richard Montague, whose Montague grammar assumed a similar view of syntax. It continues to be a major paradigm, particularly within formal semantics.

Exploring Rectangle Subdivisions

Last week, I saw a talk on Vuntra City, a procedurally generated city with a fully explorable city. Developer Larissa Davidova explained that she settled on using Recursive Subdivision for the city…

The Lambek Calculus

There is a noticeable revival of categorial grammar these days, as a vehicle for linguistic description. The systems used differ somewhat from the original calculus of Ajdukiewicz and Bar-Hillel, however. In particular, there is a component of rules for ‘type change’ of expressions, making for greater flexibility and elegance. One fundamental system of this kind is the so-called ‘Lambek Calculus’, whose type-change rules show a close analogy with the inference rules of constructive propositional logic. In this paper, we present one calculus of this kind, and survey its theoretical properties as a device in linguistic semantics. Our two main new contributions are a new and complete semantics for this calculus, as well as a modest study of its language-accepting capacity. In this way, we hope to provide a better understanding of the background theory of flexible categorial grammar, in tandem with its descriptive uses.

How well do models follow their constitutions? — LessWrong

This work was conducted during the MATS 9.0 program under Neel Nanda and Senthooran Rajamanoharan. …

La Singularidad Reflexiva: Derivas Identitarias en la Topología Probabilística de Modelos Generativos

Introspección asistida por entropía

How to Make a PDS - Part 2 - Zicklag's Leaflets

So you want to make a PDS? Or maybe you want to watch some poor fool try? In this series I might make a PDS, or not.

Structured CoT: Shorter Reasoning with a Grammar File

Constrain only the think block with a tiny grammar. On Qwen3.6 coding evals, explicit reasoning gets 22x-43x shorter without losing pass@1 in these runs.

How to Make a PDS - Part 1 - Zicklag's Leaflets

So you want to make a PDS? Or maybe you want to watch some poor fool try? In this series I might make a PDS, or not.

Language Machines

How generative AI systems capture a core function of language Looking at the emergence of generative AI, Language Machines presents a new theory of meaning i...

Rauno Freiberg

Make it fast. Make it beautiful. Make it consistent. Make it carefully. Make it timeless. Make it soulful. Make it.

Do LLMs write like humans? Variation in grammatical and rhetorical styles

As large language models (LLMs) have grown in power and become more widely available, research has focused on their ability to complete various tasks and the biases they exhibit when doing so. In this study, we instead examine their writing style in detail. We show that instruction-tuned models, which are trained to answer questions and solve problems, have a distinct noun-heavy, informationally dense writing style, even when prompted to match the style of informal speech and writing. These findings suggest that instruction-tuned models generate text that does not align with genre conventions familiar to human audiences, and demonstrate the value of linguistic variables in evaluating the output of LLMs., Large language models (LLMs) are capable of writing grammatical text that follows instructions, answers questions, and solves problems. As they have advanced, it has become difficult to distinguish their output from human-written text. While past research has found some differences in features such as word choice and punctuation and developed classifiers to detect LLM output, none has studied the rhetorical styles of LLMs. Using several variants of Llama 3 and GPT-4o, we construct two parallel corpora of human- and LLM-written texts from common prompts. Using Douglas Biber’s set of lexical, grammatical, and rhetorical features, we identify systematic differences between LLMs and humans and between different LLMs. These differences persist when moving from smaller models to larger ones and are larger for instruction-tuned models than base models. This observation of differences demonstrates that despite their advanced abilities, LLMs struggle to match human stylistic variation. Attention to more advanced linguistic features can hence detect patterns in their behavior not previously recognized.

letta-ai/context-constitution

Viva la machina.