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Knowledge Graph-Enhanced Large Language Models via Path Selection
Large Language Models (LLMs) have shown unprecedented performance in various real-
world applications. However, they are known to generate factually inaccurate outputs, aka …
world applications. However, they are known to generate factually inaccurate outputs, aka …
From instance to metric calibration: A unified framework for open-world few-shot learning
Robust few-shot learning (RFSL), which aims to address noisy labels in few-shot learning,
has recently gained considerable attention. Existing RFSL methods are based on the …
has recently gained considerable attention. Existing RFSL methods are based on the …
Noise-robust fine-tuning of pretrained language models via external guidance
Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language
Models (PLMs) have achieved substantial advancements in the field of natural language …
Models (PLMs) have achieved substantial advancements in the field of natural language …
Understanding user intent modeling for conversational recommender systems: a systematic literature review
S Farshidi, K Rezaee, S Mazaheri, AH Rahimi… - User Modeling and User …, 2024 - Springer
User intent modeling in natural language processing deciphers user requests to allow for
personalized responses. The substantial volume of research (exceeding 13,000 …
personalized responses. The substantial volume of research (exceeding 13,000 …
Joint agricultural intent detection and slot filling based on enhanced heterogeneous attention mechanism
X Hao, L Wang, H Zhu, X Guo - Computers and Electronics in Agriculture, 2023 - Elsevier
Agricultural diseases and pests are important factors restricting global food production. The
knowledge-based question-answering (Q&A) system opens a new avenue for pest and …
knowledge-based question-answering (Q&A) system opens a new avenue for pest and …
An intent taxonomy of legal case retrieval
Legal case retrieval is a special Information Retrieval (IR) task focusing on legal case
documents. Depending on the downstream tasks of the retrieved case documents, users' …
documents. Depending on the downstream tasks of the retrieved case documents, users' …
APPN: An Attention-based Pseudo-label Propagation Network for few-shot learning with noisy labels
Few-shot learning has garnered significant attention in deep learning as an effective
approach for addressing the issue of data scarcity. Conventionally, training datasets in few …
approach for addressing the issue of data scarcity. Conventionally, training datasets in few …
Warming Up Cold-Start CTR Prediction by Learning Item-Specific Feature Interactions
In recommendation systems, new items are continuously introduced, initially lacking
interaction records but gradually accumulating them over time. Accurately predicting the …
interaction records but gradually accumulating them over time. Accurately predicting the …
Meta-learning in healthcare: A survey
As a subset of machine learning, meta-learning, or learning to learn, aims at improving the
model's capabilities by employing prior knowledge and experience. A meta-learning …
model's capabilities by employing prior knowledge and experience. A meta-learning …
Optimizing question answering systems in education: addressing domain-specific challenges
BP Swathi, M Geetha, G Attigeri, MV Suhas… - IEEE …, 2024 - ieeexplore.ieee.org
Question Answering (QA) systems are increasingly essential in educational institutions,
enhancing both learning and administrative processes by providing quick and accurate …
enhancing both learning and administrative processes by providing quick and accurate …