Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars
- URL: http://arxiv.org/abs/2406.11035v1
- Date: Sun, 16 Jun 2024 18:10:49 GMT
- Title: Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars
- Authors: Damien Sileo,
- Abstract summary: We present a declarative framework with flexible context-sensitive rules binding multiple languages.
We construct first-order logic problems by selecting up to 32 premises and one hypothesis.
We demonstrate that using semantic constraints during generation and careful English verbalization of predicates enhances logical reasoning without hurting natural English tasks.
- Score: 0.6537995248511139
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Logical reasoning remains a challenge for natural language processing, but it can be improved by training language models to mimic theorem provers on procedurally generated problems. Previous work used domain-specific proof generation algorithms, which biases reasoning toward specific proof traces and limits auditability and extensibility. We present a simpler and more general declarative framework with flexible context-sensitive rules binding multiple languages (specifically, simplified English and the TPTP theorem-proving language). We construct first-order logic problems by selecting up to 32 premises and one hypothesis. We demonstrate that using semantic constraints during generation and careful English verbalization of predicates enhances logical reasoning without hurting natural English tasks. We use relatively small DeBERTa-v3 models to achieve state-of-the-art accuracy on the FOLIO human-authored logic dataset, surpassing GPT-4 in accuracy with or without an external solver by 12%.
Related papers
- LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models [52.03659714625452]
Recently developed large language models (LLMs) have been shown to perform remarkably well on a wide range of language understanding tasks.
But, can they really "reason" over the natural language?
This question has been receiving significant research attention and many reasoning skills such as commonsense, numerical, and qualitative have been studied.
arXiv Detail & Related papers (2024-04-23T21:08:49Z) - Language Models can be Logical Solvers [99.40649402395725]
We introduce LoGiPT, a novel language model that directly emulates the reasoning processes of logical solvers.
LoGiPT is fine-tuned on a newly constructed instruction-tuning dataset derived from revealing and refining the invisible reasoning process of deductive solvers.
arXiv Detail & Related papers (2023-11-10T16:23:50Z) - LINC: A Neurosymbolic Approach for Logical Reasoning by Combining
Language Models with First-Order Logic Provers [60.009969929857704]
Logical reasoning is an important task for artificial intelligence with potential impacts on science, mathematics, and society.
In this work, we reformulating such tasks as modular neurosymbolic programming, which we call LINC.
We observe significant performance gains on FOLIO and a balanced subset of ProofWriter for three different models in nearly all experimental conditions we evaluate.
arXiv Detail & Related papers (2023-10-23T17:58:40Z) - QA-NatVer: Question Answering for Natural Logic-based Fact Verification [11.002475880349452]
We propose to use question answering to predict natural logic operators.
In a few-shot setting on FEVER, our approach outperforms the best baseline by $4.3$ accuracy points.
A human evaluation indicates that our approach produces more plausible with fewer erroneous natural logic operators than previous natural logic-based systems.
arXiv Detail & Related papers (2023-10-22T06:27:31Z) - Empower Nested Boolean Logic via Self-Supervised Curriculum Learning [67.46052028752327]
We find that any pre-trained language models even including large language models only behave like a random selector in the face of multi-nested logic.
To empower language models with this fundamental capability, this paper proposes a new self-supervised learning method textitCurriculum Logical Reasoning (textscClr)
arXiv Detail & Related papers (2023-10-09T06:54:02Z) - Language Models as Inductive Reasoners [125.99461874008703]
We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts.
We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language.
We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
arXiv Detail & Related papers (2022-12-21T11:12:14Z) - A Relational Tsetlin Machine with Applications to Natural Language
Understanding [6.375447757249894]
We increase the computing power of TMs by proposing a first-order logic-based framework with Herbrand semantics.
The resulting TM is relational and can take advantage of logical structures appearing in natural language.
In closed-domain question-answering, the first-order representation produces 10x more compact KBs, along with an increase in answering accuracy from 94.83% to 99.48%.
arXiv Detail & Related papers (2021-02-22T12:40:37Z) - Learning as Abduction: Trainable Natural Logic Theorem Prover for
Natural Language Inference [0.4962199635155534]
We implement a learning method in a theorem prover for natural language.
We show that it improves the performance of the theorem prover on the SICK dataset by 1.4% while still maintaining high precision.
The obtained results are competitive with the state of the art among logic-based systems.
arXiv Detail & Related papers (2020-10-29T19:49:17Z) - Logical Natural Language Generation from Open-Domain Tables [107.04385677577862]
We propose a new task where a model is tasked with generating natural language statements that can be emphlogically entailed by the facts.
To facilitate the study of the proposed logical NLG problem, we use the existing TabFact dataset citechen 2019tabfact featured with a wide range of logical/symbolic inferences.
The new task poses challenges to the existing monotonic generation frameworks due to the mismatch between sequence order and logical order.
arXiv Detail & Related papers (2020-04-22T06:03:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.