Prompting large language models with chain-of-thought for few-shot knowledge base question generation
The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical
form into a natural language question. For the sake of expensive cost of large-scale question …
form into a natural language question. For the sake of expensive cost of large-scale question …
Systematic assessment of factual knowledge in large language models
Previous studies have relied on existing question-answering benchmarks to evaluate the
knowledge stored in large language models (LLMs). However, this approach has limitations …
knowledge stored in large language models (LLMs). However, this approach has limitations …
A survey on neural question generation: Methods, applications, and prospects
In this survey, we present a detailed examination of the advancements in Neural Question
Generation (NQG), a field leveraging neural network techniques to generate relevant …
Generation (NQG), a field leveraging neural network techniques to generate relevant …
SGSH: Stimulate Large Language Models with Skeleton Heuristics for Knowledge Base Question Generation
Knowledge base question generation (KBQG) aims to generate natural language questions
from a set of triplet facts extracted from KB. Existing methods have significantly boosted the …
from a set of triplet facts extracted from KB. Existing methods have significantly boosted the …
Actively Learn from LLMs with Uncertainty Propagation for Generalized Category Discovery
Generalized category discovery faces a key issue: the lack of supervision for new and
unseen data categories. Traditional methods typically combine supervised pretraining with …
unseen data categories. Traditional methods typically combine supervised pretraining with …
Zero-shot knowledge graph question generation via multi-agent llms and small models synthesis
Knowledge Graph Question Generation (KGQG) is the task of generating natural language
questions based on the given knowledge graph (KG). Although extensively explored in …
questions based on the given knowledge graph (KG). Although extensively explored in …
Clusterprompt: Cluster semantic enhanced prompt learning for new intent discovery
The discovery of new intent categories from user utterances is a crucial task in expanding
agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and …
agent skills. The key lies in how to efficiently solicit semantic evidence from utterances and …
MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedback
Automatic question generation (QG) is essential for AI and NLP, particularly in intelligent
tutoring, dialogue systems, and fact verification. Generating multiple-choice questions …
tutoring, dialogue systems, and fact verification. Generating multiple-choice questions …
Towards human-like questioning: Knowledge base question generation with bias-corrected reinforcement learning from human feedback
Abstract Knowledge Base Question Generation (KBQG) aims to output natural language
questions based on a Knowledge Base (KB) and the target answers. However, existing …
questions based on a Knowledge Base (KB) and the target answers. However, existing …
Distribution Shifts Are Bottlenecks: Extensive Evaluation for Grounding Language Models to Knowledge Bases
Grounding language models (LMs) to knowledge bases (KBs) helps to obtain rich and
accurate facts. However, it remains challenging because of the enormous size, complex …
accurate facts. However, it remains challenging because of the enormous size, complex …