Search Results for author:
Found 9 papers, 5 papers with code
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.
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.
Boosting legal case retrieval by query content selection with large language models
Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention.
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.
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).
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.
A Multi-turn Machine Reading Comprehension Framework with Rethink Mechanism for Emotion-Cause Pair Extraction
Our framework can model complicated relations between emotions and causes while avoiding generating the pairing matrix (the leading cause of the label sparsity problem).
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge.
