paper-with-me

Papers

Distilling Text into Circuits

2023-01-25 · Vincent Wang-Mascianica, Jonathon Liu, Bob Coecke

This paper concerns the structure of meanings within natural language. Earlier, a framework named DisCoCirc was sketched that (1) is compositional and distributional (a.k.a. vectorial); (2) applies to general text; (3) captures linguistic connections' between meanings (cf. grammar) (4) updates word meanings as text progresses; (5) structures sentence types; (6) accommodates ambiguity. Here, we realise DisCoCirc for a substantial fragment of English. When passing to DisCoCirc's text circuits, some grammatical bureaucracy' is eliminated, that is, DisCoCirc displays a significant degree of (7) inter- and intra-language independence. That is, e.g., independence from word-order conventions that differ across languages, and independence from choices like many short sentences vs. few long sentences. This inter-language independence means our text circuits should carry over to other languages, unlike the language-specific typings of categorial grammars. Hence, text circuits are a lean structure for the actual substance of text', that is, the inner-workings of meanings within text across several layers of expressiveness (cf. words, sentences, text), and may capture that what is truly universal beneath grammar. The elimination of grammatical bureaucracy also explains why DisCoCirc: (8) applies beyond language, e.g. to spatial, visual and other cognitive modes. While humans could not verbally communicate in terms of text circuits, machines can. We first define a hybrid grammar' for a fragment of English, i.e. a purpose-built, minimal grammatical formalism needed to obtain text circuits. We then detail a translation process such that all text generated by this grammar yields a text circuit. Conversely, for any text circuit obtained by freely composing the generators, there exists a text (with hybrid grammar) that gives rise to it. Hence: (9) text circuits are generative for text.

📄 PDF Abstract BibTeX arXiv:2301.10595

Code (0)

등록된 구현이 없습니다.

Tasks

Sentence

Similar Papers 제목 키워드 기반

CircuitSynth: Reliable Synthetic Data Generation

2026-04-11 · Zehua Cheng, Wei Dai, Jiahao Sun, Thomas Lukasiewicz arxiv

The generation of high-fidelity synthetic data is a cornerstone of modern machine learning, yet Large Language Models (LLMs) frequently suffer from hallucinations, logical inconsistencies, and mode collapse when tasked w…

Synthetic Data Generation

Moonshine: Distilling Game Content Generators into Steerable Generative Models

2024-08-18 · Yuhe Nie, Michael Middleton, Tim Merino, Nidhushan Kanagaraja 외

Procedural Content Generation via Machine Learning (PCGML) has enhanced game content creation, yet challenges in controllability and limited training data persist. This study addresses these issues by distilling a constr…

Language ModelingLanguage ModellingLarge Language Model

Distilling Wikipedia mathematical knowledge into neural network models

2021-04-13 · Joanne T. Kim, Mikel Landajuela Larma, Brenden K. Petersen

Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as training data. In contrast, the field of natu…

BIG-bench Machine LearningPhilosophyregressionSymbolic Regression

PrefixLLM: LLM-aided Prefix Circuit Design

2024-12-03 · Weihua Xiao, Venkata Sai Charan Putrevu, Raghu Vamshi Hemadri, Siddharth Garg 외

Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for…

Text Generationvalid

TempLM: Distilling Language Models into Template-Based Generators

2022-05-23 · Tianyi Zhang, Mina Lee, Lisa Li, Ende Shen 외

While pretrained language models (PLMs) have greatly improved text generation, they have also been known to produce unfaithful or inappropriate content. In contrast, classic template-based systems provide strong guarante…

Text Generation