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Found 8 papers, 4 papers with code
Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits.
A Survey on Semantic Processing Techniques
We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks.
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations.
Finding the Pillars of Strength for Multi-Head Attention
Specifically, the heads of MHA were originally designed to attend to information from different representation subspaces, whereas prior studies found that some attention heads likely learn similar features and can be pruned without harming performance.
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
This paper provides a comprehensive overview on a new paradigm of logical reasoning, which uses natural language as knowledge representation and pretrained language models as reasoners, including philosophical definition and categorization of logical reasoning, advantages of the new paradigm, benchmarks and methods, challenges of the new paradigm, possible future directions, and relation to related NLP fields.
Language Models as Inductive Reasoners
To this end, we propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts, and create a dataset termed DEER containing 1. 2k rule-fact pairs for the task, where rules and facts are written in natural language.
Improving Event Duration Prediction via Time-aware Pre-training
End-to-end models in NLP rarely encode external world knowledge about length of time.
Reducing the dilution: An analysis of the information sensitiveness of capsule network with a practical improvement method
Capsule network has shown various advantages over convolutional neural network (CNN).
