47 citations · 64 across the 6 of their papers we have counts for
6 papers
Rule or Story, Which is a Better Commonsense Expression for Talking with Large Language Models?
Ning Bian, Xianpei Han, Hongyu Lin +3
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
Ning Bian, Hongyu Lin, Peilin Liu +5
Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs),…
ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models
Ning Bian, Xianpei Han, Le Sun +5
Large language models (LLMs) have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain poi…
Bridging the Gap between Language Model and Reading Comprehension: Unsupervised MRC via Self-Supervision
Ning Bian, Xianpei Han, Bo Chen +3
Despite recent success in machine reading comprehension (MRC), learning high-quality MRC models still requires large-scale labeled training data, even using strong pre-trained lang…
Benchmarking Knowledge-Enhanced Commonsense Question Answering via Knowledge-to-Text Transformation
Ning Bian, Xianpei Han, Bo Chen +1
A fundamental ability of humans is to utilize commonsense knowledge in language understanding and question answering. In recent years, many knowledge-enhanced Commonsense Question…
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading Comprehension
Lingyong Yan, Xianpei Han, Le Sun +2
Distant supervision (DS) is a promising approach for relation extraction but often suffers from the noisy label problem. Traditional DS methods usually represent an entity pair as…