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Found 10 papers, 5 papers with code
Self-Evaluation of Large Language Model based on Glass-box Features
The proliferation of open-source Large Language Models (LLMs) underscores the pressing need for evaluation methods.
An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Models are Task-specific Classifiers
Recently, there has been a growing trend of utilizing Large Language Model (LLM) to evaluate the quality of other LLMs.
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.
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.
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).
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval.
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
Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
To preserve more original information, we propose an attentive recurrent neural network with similarity matrix based convolutional neural network (AR-SMCNN) model, which is able to capture comprehensive hierarchical information utilizing the advantages of both RNN and CNN.
