Formal Logic
11 papers with code • 1 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Formal Logic
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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BIG-bench
Mindgames
Most implemented papers
Learning Symbolic Rules for Reasoning in Quasi-Natural Language
In this work, we ask how we can build a rule-based system that can reason with natural language input but without the manual construction of rules.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Lectures on Jacques Herbrand as a Logician
We give some lectures on the work on formal logic of Jacques Herbrand, and sketch his life and his influence on automated theorem proving.
Bits and Pieces: Understanding Information Decomposition from Part-whole Relationships and Formal Logic
In this paper we show, first, that the entire theory of partial information decomposition can be derived from considerations of elementary parthood relationships between information contributions.
Negation in Cognitive Reasoning
In this paper, we describe an effective procedure to determine the negated event or property in order to replace it by its inverse.
Design of quantum optical experiments with logic artificial intelligence
In this work, we propose the use of logic AI for the design of optical quantum experiments.
Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers
With the rapidly increasing amount of scientific literature, it is getting continuously more difficult for researchers in different disciplines to be updated with the recent findings in their field of study. Processing scientific articles in an automated fashion has been proposed as a solution to this problem, but the accuracy of such processing remains very poor for extraction tasks beyond the basic ones. Few approaches have tried to change how we publish scientific results in the first place, by making articles machine-interpretable by expressing them with formal semantics from the start. In the work presented here, we set out to demonstrate that we can formally publish high-level scientific claims in formal logic, and publish the results in a special issue of an existing journal. We use the concept and technology of nanopublications for this endeavor, and represent not just the submissions and final papers in this RDF-based format, but also the whole process in between, including reviews, responses, and decisions. We do this by performing a field study with what we call formalization papers, which contribute a novel formalization of a previously published claim. We received 15 submissions from 18 authors, who then went through the whole publication process leading to the publication of their contributions in the special issue. Our evaluation shows the technical and practical feasibility of our approach. The participating authors mostly showed high levels of interest and confidence, and mostly experienced the process as not very difficult, despite the technical nature of the current user interfaces. We believe that these results indicate that it is possible to publish scientific results from different fields with machine-interpretable semantics from the start, which in turn opens countless possibilities to radically improve in the future the effectiveness and efficiency of the scientific endeavor as a whole.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
Leveraging Large Language Models to Generate Answer Set Programs
Specifically, we employ an LLM to transform natural language descriptions of logic puzzles into answer set programs.
RealCQA: Scientific Chart Question Answering as a Test-bed for First-Order Logic
We present a comprehensive study of chart visual question-answering(QA) task, to address the challenges faced in comprehending and extracting data from chart visualizations within documents.
