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Register a new userDiagnosing the First-Order Logical Reasoning Ability Through LogicNLI
تشخيص القدرة المنطقية من الدرجة الأولى من خلال Logicnli
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في الآونة الأخيرة، حققت نماذج اللغة (LMS) أداء كبيرا في العديد من مهام NLU، التي حفزت اهتماما واسع النطاق للتطبيقات المحتملة في المجال العلمي والاجتماعي.ومع ذلك، واجهت LMS الكثير من الانتقاد لما إذا كانت قادرة حقا على التفكير في NLU.في هذا العمل، نقترح طريقة تشخيصية للمنطق المنطقي من الدرجة الأولى (FOL) مع معيار جديد مقترح، Logicnli.Logicnli عبارة عن مجموعة بيانات ذات طراز NLI الذي تم تنشيطه بشكل فعال من مستهدف فولت المستهدف من استنتاج المنطقي ويمكن استخدامه لتشخيص LMS من أربعة وجهات نظر: الدقة والمتانة والتعميم والتفسيرية.كشفت تجارب على بيرت وروبرتا و XLNET، عن نقاط الضعف في هؤلاء LMS بشأن التفكير فول، مما يحفز الاستكشاف المستقبلي لتعزيز قدرة المنطق.
Recently, language models (LMs) have achieved significant performance on many NLU tasks, which has spurred widespread interest for their possible applications in the scientific and social area. However, LMs have faced much criticism of whether they are truly capable of reasoning in NLU. In this work, we propose a diagnostic method for first-order logic (FOL) reasoning with a new proposed benchmark, LogicNLI. LogicNLI is an NLI-style dataset that effectively disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and interpretability. Experiments on BERT, RoBERTa, and XLNet, have uncovered the weaknesses of these LMs on FOL reasoning, which motivates future exploration to enhance the reasoning ability.
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Since language is a natural concrete phenomenon, it became a fact that
language has been a matter of induction by making it go through
experiment in attempt to attain the rules that can take hold of the
language's partial phenomena and organize th
em in general regulations,
and if we observed the linguistic matter which grammarians investigated,
we could find that their work involved both the complete induction and
the incomplete induction according to Aristotle's induction method, but
they disagreed with this method in accordance with the nature of Islamic
method of thinking; therefore they had their own distinguishing method
of induction.
In modern natural language processing pipelines, it is common practice to pretrain'' a generative language model on a large corpus of text, and then to finetune'' the created representations by continuing to train them on a discriminative textual inf
erence task. However, it is not immediately clear whether the logical meaning necessary to model logical entailment is captured by language models in this paradigm. We examine this pretrain-finetune recipe with language models trained on a synthetic propositional language entailment task, and present results on test sets probing models' knowledge of axioms of first order logic.
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