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Found 15 papers, 9 papers with code
Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from Large Language Models
The process of scale calibration in ranking systems involves adjusting the outputs of rankers to correspond with significant qualities like click-through rates or relevance, crucial for mirroring real-world value and thereby boosting the system's effectiveness and reliability.
In-Context Example Ordering Guided by Label Distributions
Inspired by the idea of learning from label proportions, we propose two principles for in-context example ordering guided by model's probability predictions.
Predictive Uncertainty-based Bias Mitigation in Ranking
Prior work on bias mitigation often assumes that ranking scores, which correspond to the utility that a document holds for a user, can be accurately determined.
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstractive snippet generation.
CODER: An efficient framework for improving retrieval through COntextual Document Embedding Reranking
Contrastive learning has been the dominant approach to training dense retrieval models.
Machine Learning for Mechanical Ventilation Control (Extended Abstract)
Mechanical ventilation is one of the most widely used therapies in the ICU.
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
In contrast to the matching paradigm, the probabilistic nature of generative rankers readily offers a fine-grained measure of uncertainty.
Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models
In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query.
Machine Learning for Mechanical Ventilation Control
We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient's lungs according to a trajectory of airway pressures specified by a clinician.
Evaluating the Performance of Reinforcement Learning Algorithms
Performance evaluations are critical for quantifying algorithmic advances in reinforcement learning.
The Challenges of Optimizing Machine Translation for Low Resource Cross-Language Information Retrieval
When performing cross-language information retrieval (CLIR) for lower-resourced languages, a common approach is to retrieve over the output of machine translation (MT).
MODELLING BIOLOGICAL ASSAYS WITH ADAPTIVE DEEP KERNEL LEARNING
Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.
Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning
We use an adversarial discriminator and train our neural ranking model on a small set of domains.
Information Retrieval
Adaptability of Neural Networks on Varying Granularity IR Tasks
Recent work in Information Retrieval (IR) using Deep Learning models has yielded state of the art results on a variety of IR tasks.
