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Found 20 papers, 9 papers with code
From Matching to Generation: A Survey on Generative Information Retrieval
We will summarize the advancements in GR regarding model training, document identifier, incremental learning, downstream tasks adaptation, multi-modal GR and generative recommendation, as well as progress in reliable response generation in aspects of internal knowledge memorization, external knowledge augmentation, generating response with citations and personal information assistant.
Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models
Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate.
BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence
Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.
Metacognitive Retrieval-Augmented Large Language Models
Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes.
Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs.
Grounding Language Model with Chunking-Free In-Context Retrieval
These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.
CorpusLM: Towards a Unified Language Model on Corpus for Knowledge-Intensive Tasks
We design the following mechanisms to facilitate effective retrieval and generation, and improve the end-to-end effectiveness of KI tasks: (1) We develop a ranking-oriented DocID list generation strategy, which refines GR by directly learning from a DocID ranking list, to improve retrieval quality.
UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models
Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing.
Generative Retrieval via Term Set Generation
On top of the term-set DocID, we propose a permutation-invariant decoding algorithm, with which the term set can be generated in any permutation yet will always lead to the corresponding document.
Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering
Results: Using baseline prompts, GPT-3. 5 and GPT-4 achieved relaxed F1 scores of 0. 634, 0. 804 for MTSamples, and 0. 301, 0. 593 for VAERS.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
In order to unify these two stages, we explore a model-based indexer for document retrieval.
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Web search provides a promising way for people to obtain information and has been extensively studied.
Socialformer: Social Network Inspired Long Document Modeling for Document Ranking
In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking.
PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling
Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors.
Group based Personalized Search by Integrating Search Behaviour and Friend Network
Specifically, we propose a friend network enhanced personalized search model, which groups the user into multiple friend circles based on search behaviours and friend relations respectively.
Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data
As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models.
Numerical Simulation of Bubbly Flow Using Partially Averaged Navier−Stokes Simulation and a Path Oscillation Model in the Euler−Lagrange Approach
Large bubbles always undergo asymmetric paths due to the wake instability, which was rarely considered in the simulation of bubbly flow in complex flow systems such as bubble columns.
SpineParseNet: Spine Parsing for Volumetric MR Image by a Two-Stage Segmentation Framework with Semantic Image Representation
The SpineParseNet consists of a 3D graph convolutional segmentation network (GCSN) for 3D coarse segmentation and a 2D residual U-Net (ResUNet) for 2D segmentation refinement.
COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.
Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation
In fact, due to the limitation of the receptive field, the 3 x 3 kernel has difficulty in covering the corresponding features at high/original resolution.
