Search Results for author:
Found 6 papers, 2 papers with code
ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science
We train a graph neural network on the curated document graph to act as a structural encoder for the corresponding passages retrieved during the model pretraining.
Empirical evaluation of Uncertainty Quantification in Retrieval-Augmented Language Models for Science
This research investigates how uncertainty scores vary when scientific knowledge is incorporated as pretraining and retrieval data and explores the relationship between uncertainty scores and the accuracy of model-generated outputs.
Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning
Despite the dramatic progress in Large Language Model (LLM) development, LLMs often provide seemingly plausible but not factual information, often referred to as hallucinations.
NuclearQA: A Human-Made Benchmark for Language Models for the Nuclear Domain
As LLMs have become increasingly popular, they have been used in almost every field.
Finding Trolls Under Bridges: Preliminary Work on a Motif Detector
We briefly describe an annotation effort to produce data for training motif detection, which is on-going.
Towards an Atlas of Cultural Commonsense for Machine Reasoning
Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences.
