Search Results for author:
Found 12 papers, 9 papers with code
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering
By combining the improvements in both architecture and training, our proposed REAR can better utilize external knowledge by effectively perceiving the relevance of retrieved documents.
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation.
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
To tackle the LLM hallucination, three key questions should be well studied: how to detect hallucinations (detection), why do LLMs hallucinate (source), and what can be done to mitigate them (mitigation).
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA.
TOME: A Two-stage Approach for Model-based Retrieval
Recently, model-based retrieval has emerged as a new paradigm in text retrieval that discards the index in the traditional retrieval model and instead memorizes the candidate corpora using model parameters.
A Survey of Large Language Models
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
Dense Text Retrieval based on Pretrained Language Models: A Survey
With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling.
A Thorough Examination on Zero-shot Dense Retrieval
Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM).
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking.
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks.
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers.
Ranked #4 on
Passage Retrieval
on Natural Questions
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of sequential recommendation.
