Search Results for author:
Found 20 papers, 8 papers with code
Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
Legal retrieval techniques play an important role in preserving the fairness and equality of the judicial system.
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc.
DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment
Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity.
Evaluation Ethics of LLMs in Legal Domain
In recent years, the utilization of large language models for natural language dialogue has gained momentum, leading to their widespread adoption across various domains.
PRE: A Peer Review Based Large Language Model Evaluator
Existing paradigms rely on either human annotators or model-based evaluators to evaluate the performance of LLMs on different tasks.
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.
LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset
However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling.
Mask wearing object detection algorithm based on improved YOLOv5
Our proposed method significantly enhances the detection capability of mask-wearing.
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).
An Intent Taxonomy of Legal Case Retrieval
Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.
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.
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.
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.
SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements.
T2Ranking: A large-scale Chinese Benchmark for Passage Ranking
T2Ranking comprises more than 300K queries and over 2M unique passages from real-world search engines.
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.
Reachability of Dimension-Bounded Linear Systems
In this paper, the reachability of dimension-bounded linear systems is investigated. Since state dimensions of dimension-bounded linear systems vary with time, the expression of state dimension at each time is provided. A method for judging the reachability of a given vector space is proposed.
Forecasting Popularity of Videos using Social Media
This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media.
