Elliott Ash

The Choice of Knowledge Base in Automated Claim Checking

2021 · arXiv (Cornell University)

Automated claim checking is the task of determining the veracity of a claim given evidence found in a knowledge base of trustworthy facts. While previous work has taken the knowledge base as given and optimized …

Revisiting Automated Topic Model Evaluation with Large Language Models

2023

Topic models help us make sense of large text collections. Automatically evaluating their output and determining the optimal number of topics are both longstanding challenges, with no effective automated solutions to date. This paper proposes …

Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning

2025 · arXiv (Cornell University)

Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness. This trade-off arises because IFT steers LLMs to generate responses containing long-tail knowledge that was not well covered …

DIRAS: Efficient LLM Annotation of Document Relevance for Retrieval Augmented Generation

2025

Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when answering queries that need an integrated analysis of information (e.g., Tell …