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Found 7 papers, 4 papers with code
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language(e. g., chemical molecular formula).
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
This shift encompasses a move from discriminative AI approaches to generative AI approaches, as well as a shift from model-centered methodologies to datacentered methodologies.
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings.
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
(2) How to enhance the perception of reasoning types for the models?
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively.
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MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams.
Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning
Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and entities, which is limited to a transductive setting and has restriction on processing unseen entities in an inductive situation.
