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Found 6 papers, 1 papers with code
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Building machines with commonsense has been a longstanding challenge in NLP due to the reporting bias of commonsense rules and the exposure bias of rule-based commonsense reasoning.
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
(4) Can ChatGPT effectively leverage commonsense for answering questions?
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
In this paper, we propose a new framework for unsupervised MRC.
Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation
Experiments show that: (1) Our knowledge-to-text framework is effective and achieves state-of-the-art performance on CommonsenseQA dataset, providing a simple and strong knowledge-enhanced baseline for CQA; (2) The potential of knowledge is still far from being fully exploited in CQA -- there is a significant performance gap from current models to our models with golden knowledge; and (3) Context-sensitive knowledge selection, heterogeneous knowledge exploitation, and commonsense-rich language models are promising CQA directions.
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
By re-organizing all sentences about an entity as a document and extracting relations via querying the document with relation-specific questions, the document-based DS paradigm can simultaneously encode and exploit all sentence-level, inter-sentence-level, and entity-level evidence.
Ranked #1 on
Relationship Extraction (Distant Supervised)
on NYT
