Search Results - "Arabzadeh, Negar"

A self-supervised language model selection strategy for biomedical question answering by Arabzadeh, Negar, Bagheri, Ebrahim

“…Large neural-based Pre-trained Language Models (PLM) have recently gained much attention due to their noteworthy performance in many downstream Information…”
Get full text

Shallow pooling for sparse labels by Arabzadeh, Negar, Vtyurina, Alexandra, Yan, Xinyi, Clarke, Charles L. A.

“…Recent years have seen enormous gains in core information retrieval tasks, including document and passage ranking. Datasets and leaderboards, and in particular…”
Get full text

Neural embedding-based specificity metrics for pre-retrieval query performance prediction by Arabzadeh, Negar, Zarrinkalam, Fattane, Jovanovic, Jelena, Al-Obeidat, Feras, Bagheri, Ebrahim

“…•We show how specificity can be measured in the context of neural embeddings.•We employ neural embedding-based specificity for performing query performance…”
Get full text

Offline Evaluation of Set-Based Text-to-Image Generation by Arabzadeh, Negar, Diaz, Fernando, He, Junfeng

“…Text-to-Image (TTI) systems often support people during ideation, the early stages of a creative process when exposure to a broad set of relevant images can…”
Get full text

A Comparison of Methods for Evaluating Generative IR by Arabzadeh, Negar, Clarke, Charles L. A

“…Information retrieval systems increasingly incorporate generative components. For example, in a retrieval augmented generation (RAG) system, a retrieval…”
Get full text

Fr\'echet Distance for Offline Evaluation of Information Retrieval Systems with Sparse Labels by Arabzadeh, Negar, Clarke, Charles L. A

“…The rapid advancement of natural language processing, information retrieval (IR), computer vision, and other technologies has presented significant challenges…”
Get full text

Generative Information Retrieval Evaluation by Alaofi, Marwah, Arabzadeh, Negar, Clarke, Charles L. A, Sanderson, Mark

“…This paper is a draft of a chapter intended to appear in a forthcoming book on generative information retrieval, co-edited by Chirag Shah and Ryen White. In…”
Get full text

Adapting Standard Retrieval Benchmarks to Evaluate Generated Answers by Arabzadeh, Negar, Bigdeli, Amin, Clarke, Charles L. A

“…Large language models can now directly generate answers to many factual questions without referencing external sources. Unfortunately, relatively little…”
Get full text

Retrieving Supporting Evidence for LLMs Generated Answers by Huo, Siqing, Arabzadeh, Negar, Clarke, Charles L. A

“…Current large language models (LLMs) can exhibit near-human levels of performance on many natural language tasks, including open-domain question answering…”
Get full text

Query Performance Prediction: From Ad-hoc to Conversational Search by Meng, Chuan, Arabzadeh, Negar, Aliannejadi, Mohammad, de Rijke, Maarten

“…Query performance prediction (QPP) is a core task in information retrieval. The QPP task is to predict the retrieval quality of a search system for a query…”
Get full text

Ranked List Truncation for Large Language Model-based Re-Ranking by Meng, Chuan, Arabzadeh, Negar, Askari, Arian, Aliannejadi, Mohammad, de Rijke, Maarten

“…We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e.,…”
Get full text

Early Stage Sparse Retrieval with Entity Linking by Shehata, Dahlia, Arabzadeh, Negar, Clarke, Charles L. A

“…Despite the advantages of their low-resource settings, traditional sparse retrievers depend on exact matching approaches between high-dimensional bag-of-words…”
Get full text

Unsupervised Question Clarity Prediction Through Retrieved Item Coherency by Arabzadeh, Negar, Seifikar, Mahsa, Clarke, Charles L. A

“…Despite recent progress on conversational systems, they still do not perform smoothly and coherently when faced with ambiguous requests. When questions are…”
Get full text

Query Performance Prediction using Relevance Judgments Generated by Large Language Models by Meng, Chuan, Arabzadeh, Negar, Askari, Arian, Aliannejadi, Mohammad, de Rijke, Maarten

“…Query performance prediction (QPP) aims to estimate the retrieval quality of a search system for a query without human relevance judgments. Previous QPP…”
Get full text

Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications by Arabzadeh, Negar, Kiseleva, Julia, Wu, Qingyun, Wang, Chi, Awadallah, Ahmed, Dibia, Victor, Fourney, Adam, Clarke, Charles

“…The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to…”
Get full text

Retrieving Supporting Evidence for Generative Question Answering by Huo, Siqing, Arabzadeh, Negar, Clarke, Charles L. A

“…Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP '23), November 26--28,…”
Get full text

IDAT: A Multi-Modal Dataset and Toolkit for Building and Evaluating Interactive Task-Solving Agents by Mohanty, Shrestha, Arabzadeh, Negar, Tupini, Andrea, Sun, Yuxuan, Skrynnik, Alexey, Zholus, Artem, Côté, Marc-Alexandre, Kiseleva, Julia

“…Seamless interaction between AI agents and humans using natural language remains a key goal in AI research. This paper addresses the challenges of developing…”
Get full text

Assessing and Verifying Task Utility in LLM-Powered Applications by Arabzadeh, Negar, Huo, Siqing, Mehta, Nikhil, Wu, Qinqyun, Wang, Chi, Awadallah, Ahmed, Clarke, Charles L. A, Kiseleva, Julia

“…The rapid development of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents, assisting humans…”
Get full text

Predicting Efficiency/Effectiveness Trade-offs for Dense vs. Sparse Retrieval Strategy Selection by Arabzadeh, Negar, Yan, Xinyi, Clarke, Charles L. A

“…Over the last few years, contextualized pre-trained transformer models such as BERT have provided substantial improvements on information retrieval tasks…”
Get full text

Shallow pooling for sparse labels by Arabzadeh, Negar, Vtyurina, Alexandra, Yan, Xinyi, Clarke, Charles L. A

“…Recent years have seen enormous gains in core IR tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO…”
Get full text