24 citations · 48 across the 7 of their papers we have counts for
7 papers
Boolean queries are all you need?
Charles L. A. Clarke, Mark D. Smucker
We equipped an LLM-based search agent with access to a Boolean retrieval engine to search the MS MARCO V2.1 deduped segment collection used by the TREC 2024 RAG track. Over a stand…
Resources for Automated Evaluation of Assistive RAG Systems that Help Readers with News Trustworthiness Assessment
Dake Zhang, Mark D. Smucker, Charles L. A. Clarke
Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (Detection, Retrieval, and Au…
Extending MovieLens-32M to Provide New Evaluation Objectives
Mark D. Smucker, Houmaan Chamani
Offline evaluation of recommender systems has traditionally treated the problem as a machine learning problem. In the classic case of recommending movies, where the user has provid…
Assessing top- preferences
Charles L. A. Clarke, Alexandra Vtyurina, Mark D. Smucker
Assessors make preference judgments faster and more consistently than graded judgments. Preference judgments can also recognize distinctions between items that appear equivalent un…
Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval
Haotian Zhang, Gordon V. Cormack, Maura R. Grossman +1
This study uses a novel simulation framework to evaluate whether the time and effort necessary to achieve high recall using active learning is reduced by presenting the reviewer wi…
Efficient and Effective Spam Filtering and Re-ranking for Large Web Datasets
Gordon V. Cormack, Mark D. Smucker, Charles L. A. Clarke
The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general Web in early 2009. This dataset cont…
Analyzing Social Network Structures in the Iterated Prisoner's Dilemma with Choice and Refusal
Mark D. Smucker, E. Ann Stanley, Dan Ashlock
The Iterated Prisoner's Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoner's Dilemma with evolution that allows players to choose and to refuse their…