Large Language Models (LLMs) have been shown to effectively perform zero-shot document retrieval, a process that typically consists of two steps: i) retrieving relevant documents, and ii) re-ranking them based on their relevance to the query.This paper presents GENRA, a new approach to zero-shot document retrieval that incorporates rank aggregation to improve retrieval effectiveness.Given a query, GENRA first utilizes LLMs to generate informative passages that capture the query's intent.These passages are then employed to guide the retrieval process, selecting similar documents from the corpus.Next, we use LLMs again for a second refinement step.This step can be configured for either direct relevance assessment of each retrieved document or for re-ranking the retrieved documents.Ultimately, both approaches ensure that only the most relevant documents are kept.Upon this filtered set of documents, we perform multi-document retrieval, generating individual rankings for each document.As a final step, GENRA leverages rank aggregation, combining the individual rankings to produce a single refined ranking.Extensive experiments on benchmark datasets demonstrate that GENRA improves existing approaches, highlighting the effectiveness of the proposed methodology in zero-shot retrieval.

Abstract In this paper, we propose a novel framework, called STAL, which makes use of unlabeled graph data, through a combination of Active Learning and Self-Training, in order to improve node labeling by Graph Neural Networks (GNNs). GNNs have been shown to perform well on many tasks, when sufficient labeled data are available. Such data, however, is often scarce, leading to the need for methods that leverage unlabeled data that are abundant. Active Learning and Self-training are two common approaches towards this goal and we investigate here their combination, in the context of GNN training. Specifically, we propose a new framework that first uses active learning to select highly uncertain unlabeled nodes to be labeled and be included in the training set. In each iteration of active labeling, the proposed method expands also the label set through self-training. In particular, highly certain pseudo-labels are obtained and added automatically to the training set. This process is repeated, leading to good classifiers, with a limited amount of labeled data. Our experimental results on various datasets confirm the efficiency of the proposed approach.

OBJECTIVE: This article presents the novel BioASQ Synergy research process which aims to facilitate the interaction between biomedical experts and automated question-answering systems. MATERIALS AND METHODS: The proposed research allows systems to provide answers to emerging questions, which in turn are assessed by experts. The assessment of the experts is fed back to the systems, together with new questions. With this iteration, we aim to facilitate the incremental understanding of a developing problem and contribute to solution discovery. RESULTS: The results suggest that the proposed approach can assist researchers to navigate available resources. The experts seem to be very satisfied with the quality of the ideal answers provided by the systems, suggesting that such systems are already useful in answering open research questions. DISCUSSION: BioASQ Synergy aspires to provide a tool that gives the experts easy and personalized access to the latest findings in a fast-growing corpus of material. CONCLUSION: In this article, we envisioned BioASQ Synergy as a continuous dialogue between experts and systems to issue open questions. We ran an initial proof-of-concept of the approach, in order to evaluate its usefulness, both from the side of the experts, as well as from the side of the participating systems.

The performance of Graph Neural Networks (GNNs) deteriorates as the depth of the network increases. That performance drop is mainly attributed to oversmoothing, which leads to similar node representations through repeated graph convolutions. We show that in deep GNNs the activation function plays a crucial role in oversmoothing. We explain theoretically why this is the case and propose a simple modification to the slope of ReLU to reduce oversmoothing. The proposed approach enables deep networks without the need to change the network architecture or to add residual connections. We verify the theoretical results experimentally and further show that deep networks, which do not suffer from oversmoothing, are beneficial in the presence of the “cold start” problem, i.e. when there is no feature information about unlabeled nodes.

Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge.However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks.In this study, we propose a new method to predict the effectiveness of an intervention in a clinical trial.Our method relies on generating an informative summary from multiple documents available in the literature about the intervention under study.Specifically, our method first gathers all the abstracts of PubMed articles related to the intervention.Then, an evidence sentence, which conveys information about the effectiveness of the intervention, is extracted automatically from each abstract.Based on the set of evidence sentences extracted from the abstracts, a short summary about the intervention is constructed.Finally, the produced summaries are used to train a BERT-based classifier, in order to infer the effectiveness of an intervention.To evaluate our proposed method, we introduce a new dataset which is a collection of clinical trials together with their associated PubMed articles.Our experiments demonstrate the effectiveness of producing short informative summaries and using them to predict the effectiveness of an intervention.

Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.

The discovery of web documents about certain topics is an important task for web-based applications including web document retrieval, opinion mining and knowledge extraction. In this paper, we propose an agent-based focused crawling framework able to retrieve topic- and genre-related web documents. Starting from a simple topic query, a set of focused crawler agents explore in parallel topic-specific web paths using dynamic seed URLs that belong to certain web genres and are collected from web search engines. The agents make use of an internal mechanism that weighs topic and genre relevance scores of unvisited web pages. They are able to adapt to the properties of a given topic by modifying their internal knowledge during search, handle ambiguous queries, ignore irrelevant pages with respect to the topic and retrieve collaboratively topic-relevant web pages. We performed an experimental study to evaluate the behavior of the agents for a variety of topic queries demonstrating the benefits and the capabilities of our framework.