Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Zhuang, Shengyao"'
Autor:
Khramtsova, Ekaterina, Leelanupab, Teerapong, Zhuang, Shengyao, Baktashmotlagh, Mahsa, Zuccon, Guido
In this demo we present a web-based application for selecting an effective pre-trained dense retriever to use on a private collection. Our system, DenseQuest, provides unsupervised selection and ranking capabilities to predict the best dense retrieve
Externí odkaz:
http://arxiv.org/abs/2407.06685
The goal of screening prioritisation in systematic reviews is to identify relevant documents with high recall and rank them in early positions for review. This saves reviewing effort if paired with a stopping criterion, and speeds up review completio
Externí odkaz:
http://arxiv.org/abs/2407.00635
We provide a systematic understanding of the impact of specific components and wordings used in prompts on the effectiveness of rankers based on zero-shot Large Language Models (LLMs). Several zero-shot ranking methods based on LLMs have recently bee
Externí odkaz:
http://arxiv.org/abs/2406.14117
This study investigates the integration of diverse patient data sources into multimodal language models for automated chest X-ray (CXR) report generation. Traditionally, CXR report generation relies solely on CXR images and limited radiology data, ov
Externí odkaz:
http://arxiv.org/abs/2406.13181
Autor:
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin, Hagen, Matthias
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, the distilled models usually do not reach their teacher LLM's effectiveness. To investiga
Externí odkaz:
http://arxiv.org/abs/2405.07920
Utilizing large language models (LLMs) for zero-shot document ranking is done in one of two ways: 1) prompt-based re-ranking methods, which require no further training but are only feasible for re-ranking a handful of candidate documents due to compu
Externí odkaz:
http://arxiv.org/abs/2404.18424
Autor:
Schlatt, Ferdinand, Fröbe, Maik, Scells, Harrisen, Zhuang, Shengyao, Koopman, Bevan, Zuccon, Guido, Stein, Benno, Potthast, Martin, Hagen, Matthias
Existing cross-encoder re-rankers can be categorized as pointwise, pairwise, or listwise models. Pair- and listwise models allow passage interactions, which usually makes them more effective than pointwise models but also less efficient and less robu
Externí odkaz:
http://arxiv.org/abs/2404.06912
The emergence of Vec2Text -- a method for text embedding inversion -- has raised serious privacy concerns for dense retrieval systems which use text embeddings, such as those offered by OpenAI and Cohere. This threat comes from the ability for a mali
Externí odkaz:
http://arxiv.org/abs/2402.12784
Federated search systems aggregate results from multiple search engines, selecting appropriate sources to enhance result quality and align with user intent. With the increasing uptake of Retrieval-Augmented Generation (RAG) pipelines, federated searc
Externí odkaz:
http://arxiv.org/abs/2402.11891
Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The use of stemming in IR has been shown to often improve the effectiveness of keyword-matching models such as BM25
Externí odkaz:
http://arxiv.org/abs/2402.11757