Search Results for author:
Found 13 papers, 7 papers with code
Scaling Laws For Dense Retrieval
In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.
DRAGIN: Dynamic Retrieval Augmented Generation based on the Real-time Information Needs of Large Language Models
Our framework is specifically designed to make decisions on when and what to retrieve based on the LLM's real-time information needs during the text generation process.
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.
Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval
With the development of deep learning and natural language processing techniques, pre-trained language models have been widely used to solve information retrieval (IR) problems.
Relevance Feedback with Brain Signals
To explore the effectiveness of brain signals in the context of RF, we propose a novel RF framework that combines BCI-based relevance feedback with pseudo-relevance signals and implicit signals to improve the performance of document re-ranking.
Caseformer: Pre-training for Legal Case Retrieval Based on Inter-Case Distinctions
Legal case retrieval aims to help legal workers find relevant cases related to their cases at hand, which is important for the guarantee of fairness and justice in legal judgments.
THUIR2 at NTCIR-16 Session Search (SS) Task
Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task.
THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment
This paper describes the approach of the THUIR team at the COLIEE 2023 Legal Case Entailment task.
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval
Legal case retrieval techniques play an essential role in modern intelligent legal systems.
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding
In the data sampling phase, we enhance the quality of the training data by utilizing fine-grained law article information to guide the selection of positive and negative examples.
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank.
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank
This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task.
Web Search via an Efficient and Effective Brain-Machine Interface
While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades.
