Search Results

Query Performance Prediction is a key task in IR, focusing on estimating the retrieval quality of a given query without relying on human-labeled relevance judgments. Over the decades, QPP has gained increasing significance, with a surge in research ...

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 or partially relevant images can help explore the design space. Since ideation is an important subclass of ...

Traditional search evaluation metrics, such as MRR and NDCG, focus on absolute measures of effectiveness. While they allow us to compare the absolute performance of one retrieval method to another, we do not know if systems with similar absolute ...

Prior research has demonstrated that reformulation of queries can significantly enhance retrieval effectiveness. Despite notable successes in neural-based query reformulation methods, identifying optimal reformulations that cover the same information ...

The peer review process is a fundamental aspect of academic publishing, ensuring the quality and credibility of scholarly work. In this talk, we will explore the critical challenges associated specifically with the assignment of reviewers to submitted ...

The First Search Futures Workshop, in conjunction with the Fourty-sixth European Conference on Information Retrieval (ECIR) 2024, looked into the future of search to ask questions such as:

• How can we harness the power of generative AI to enhance, ...

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., trim re-ranking candidates). RLT is crucial for re-ranking as it can improve re-ranking efficiency ...

Query performance prediction (QPP) is a core task in information retrieval (IR) that aims at predicting the retrieval quality for a given query without relevance judgments. QPP has been investigated for decades and has witnessed a surge in ...

Entity retrieval is essential in information access domains where people search for specific entities, such as individuals, organizations, and places. While entity retrieval is an active research topic in Information Retrieval, it is necessary to ...

The state-of-the-art query performance prediction methods rely on the fine-tuning of contextual language models to estimate retrieval effectiveness on a per-query basis. Our work in this paper builds on this strong foundation and proposes to learn ...

Recent empirical studies have shown that while neural rankers exhibit increasingly higher retrieval effectiveness on tasks such as ad hoc retrieval, these improved performances are not experienced uniformly across the range of all queries. There ...

Research has already found that many retrieval methods are sensitive to the choice and order of terms that appear in a query, which can significantly impact retrieval effectiveness. We capitalize on this finding in order to predict the performance ...

Query Performance Prediction (QPP) aims to estimate the effectiveness of a query in addressing the underlying information need without any relevance judgments. More recent works in this area have employed the pre-trained neural embedding ...

Large language models can now directly generate answers to many factual questions without referencing external sources. Unfortunately, relatively little attention has been paid to methods for evaluating the quality and correctness of these ...

Entity search plays a crucial role in various information access domains, where users seek information about specific entities. Despite significant research efforts to improve entity search methods, the availability of large-scale resources and ...

Current large language models (LLMs) can exhibit near-human levels of performance on many natural language-based tasks, including open-domain question answering. Unfortunately, at this time, they also convincingly hallucinate incorrect answers, so that ...

Estimating query difficulty, also known as Query Performance Prediction (QPP), is concerned with assessing the retrieval quality of a ranking method for an input query. Most traditional unsupervised frequency-based models and many recent supervised ...

Researchers have shown that the retrieval effectiveness of queries may depend on other factors in addition to the semantics of the query. In other words, several queries expressed with the same intent, and even using overlapping keywords, may exhibit ...

Large neural-based Pre-trained Language Models (PLM) have recently gained much attention due to their noteworthy performance in many downstream Information Retrieval (IR) and Natural Language Processing (NLP) tasks. PLMs can be categorized as ...

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• Exploring General vs. domain-specific languge models in Biomed QA.
• Studying the synergy between general PLMs in Biomed QA.
• proposing the problem of Language Model Selection for Biomed QA.
• proposing novel ML strategy to select ...

Instant search has emerged as the dominant search paradigm in entity-focused search applications, including search on Apple Music, Kayak, LinkedIn, and Spotify. Unlike the traditional search paradigm, in which users fully issue their query and then the ...