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Laura Dietz

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Associate Professor with Tenure

University of New Hampshire

United States

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About

Laura Dietz is an Assistant Professor at the University of New Hampshire, USA. Her research focuses on automatic large-scale test collections for entity, passage, and entity-passage retrieval. She is dedicated to advancing methodologies in information retrieval and machine learning, as reflected in her recent publications.

Recent Grants

Grant: Close

Forecasting Salinity in Rivers during Storm Events

Open Date: 2020-12-01

Close Date: 2021-05-01

Grant: Close

Utilizing Fine-grained Knowledge Annotations in Text Understanding and Retrieval

Open Date: 2019-01-01

Close Date: 2025-06-01

Articles (17)

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2022

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2020

Collaborators (4)

Damiano Spina

Senior Lecturer

RMIT University

AUSTRALIA

View details

XI WANG

Lecturer

University of Sheffield

UNITED KINGDOM

View details

Maarten de Rijke

Prof.

University of Amsterdam

NETHERLANDS

View details

Mark Sanderson

RMIT University

AUSTRALIA

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Laura Dietz

Research Interests

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About

Recent Grants

Articles (17)

VulGen: Workshop on Vulnerabilities in Generative Systems for Information Retrieval

Beyond Relevance: On the Relationship Between Retrieval and RAG Information Coverage

DoGMaTiQ: Automated Generation of Question-and-Answer Nuggets for Report Evaluation

Auto-ARGUE: LLM-Based Report Generation Evaluation

Auto-Judge: A Cross-Task Benchmark for Comparing LLM Judges for Citation-Grounded RAG Systems

Learning to Rank with Multi-Criteria LLM-Judge Annotations

Too Many Questions: Deriving Concise and Effective Nugget Banks

Supporting Humans in Evaluating AI Summaries of Legal Depositions

Incorporating Q&A Nuggets Into Retrieval-Augmented Generation

Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

Talmud-IR: A Talmud-Inspired Interface for Discussing RAG Response Quality

Criteria-Based LLM Relevance Judgments

Principles and Guidelines for the Use of LLM Judges

Does UMBRELA work on other LLMs?

LLM-based Relevance Assessment Still Can’t Replace Human Relevance Assessment

Topic-Mono-BERT: A Joint Retrieval-Clustering System for Retrieving Overview Passages

Humans Optional? Automatic Large-Scale Test Collections for Entity, Passage, and Entity-Passage Retrieval

Collaborators (4)

Damiano Spina

XI WANG

Maarten de Rijke

Mark Sanderson

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