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Found 24 papers, 12 papers with code
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Cross-encoders are effective passage re-rankers.
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
In this paper, we investigate various aspects of embedding models that could influence the recoverability of text using Vec2Text.
ReSLLM: Large Language Models are Strong Resource Selectors for Federated Search
Our ReSLLM method exploits LLMs to drive the selection of resources in federated search in a zero-shot setting.
TPRF: A Transformer-based Pseudo-Relevance Feedback Model for Efficient and Effective Retrieval
This paper considers Pseudo-Relevance Feedback (PRF) methods for dense retrievers in a resource constrained environment such as that of cheap cloud instances or embedded systems (e. g., smartphones and smartwatches), where memory and CPU are limited and GPUs are not present.
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TAR
In this context, we show that there is no need for further pre-training if a domain-specific BERT backbone is used within the active learning pipeline.
Zero-shot Generative Large Language Models for Systematic Review Screening Automation
Systematic reviews are crucial for evidence-based medicine as they comprehensively analyse published research findings on specific questions.
Team IELAB at TREC Clinical Trial Track 2023: Enhancing Clinical Trial Retrieval with Neural Rankers and Large Language Models
We describe team ielab from CSIRO and The University of Queensland's approach to the 2023 TREC Clinical Trials Track.
Open-source Large Language Models are Strong Zero-shot Query Likelihood Models for Document Ranking
In the field of information retrieval, Query Likelihood Models (QLMs) rank documents based on the probability of generating the query given the content of a document.
A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Our approach reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, significantly improving the efficiency of LLM-based zero-shot ranking.
ChatGPT Hallucinates when Attributing Answers
We find that ChatGPT provides correct or partially correct answers in about half of the cases (50. 6% of the times), but its suggested references only exist 14% of the times.
Generating Natural Language Queries for More Effective Systematic Review Screening Prioritisation
Our best approach is not only viable based on the information available at the time of screening, but also has similar effectiveness to the final title.
Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation
To improve diagnostic accuracy, we propose a CXR report generator that integrates aspects of the radiologist workflow and is trained with our proposed reward for reinforcement learning.
Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness
Aside from measuring the effectiveness of ChatGPT in this context, we show that the knowledge passed in the prompt can overturn the knowledge encoded in the model and this is, in our experiments, to the detriment of answer correctness.
Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?
The ability of ChatGPT to follow complex instructions and generate queries with high precision makes it a valuable tool for researchers conducting systematic reviews, particularly for rapid reviews where time is a constraint and often trading-off higher precision for lower recall is acceptable.
AgAsk: An Agent to Help Answer Farmer's Questions From Scientific Documents
On the basis of these needs we release an information retrieval test collection comprising real questions, a large collection of scientific documents split in passages, and ground truth relevance assessments indicating which passages are relevant to each question.
Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search
An empirical analysis compares how effective neural methods compare to traditional methods for this task.
Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search
However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query.
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval.
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
However, we show pseudo seed studies are not representative of real seed studies used by information specialists.
Improving Chest X-Ray Report Generation by Leveraging Warm Starting
Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively.
Semantic Search for Large Scale Clinical Ontologies
Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets.
