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Found 11 papers, 3 papers with code
Expanding Chatbot Knowledge in Customer Service: Context-Aware Similar Question Generation Using Large Language Models
To accommodate potential variations in how a customer's query may be expressed, it emerges as the favored solution to augment these QA pairs with similar questions that are possibly diverse while remaining semantic consistency.
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing
With the growing importance of customer service in contemporary business, recognizing the intents behind service dialogues has become essential for the strategic success of enterprises.
Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting
Batch prompting is a common technique in large language models (LLMs) used to process multiple inputs simultaneously, aiming to improve computational efficiency.
InfantCryNet: A Data-driven Framework for Intelligent Analysis of Infant Cries
Additionally, knowledge distillation and model quantization are applied to enhance model efficiency and reduce the model size, better supporting industrial deployment in mobile devices.
Cost-Aware Uncertainty Reduction in Schema Matching with GPT-4: The Prompt-Matcher Framework
We introduce a novel framework, Prompt-Matcher, to reduce the uncertainty in the process of integration of multiple automatic schema matching algorithms and the selection of complex parameterization.
DataVisT5: A Pre-trained Language Model for Jointly Understanding Text and Data Visualization
Data visualization (DV) is the fundamental and premise tool to improve the efficiency in conveying the insights behind the big data, which has been widely accepted in existing data-driven world.
Pairwise Judgment Formulation for Semantic Embedding Model in Web Search
Semantic Embedding Model (SEM), a neural network-based Siamese architecture, is gaining momentum in information retrieval and natural language processing.
Dissecting Multiplication in Transformers: Insights into LLMs
This work underscores the importance of explainable AI, helping to build trust in large language models and promoting their adoption in critical applications.
XPose: eXplainable Human Pose Estimation
In this paper, we propose XPose, a novel framework that incorporates Explainable AI (XAI) principles into pose estimation.
On Leveraging Large Language Models for Enhancing Entity Resolution: A Cost-efficient Approach
Entity resolution, the task of identifying and merging records that refer to the same real-world entity, is crucial in sectors like e-commerce, healthcare, and law enforcement.
Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspection
While large language models (LLMs) such as ChatGPT and PaLM have demonstrated remarkable performance in various language understanding and generation tasks, their capabilities in complex reasoning and intricate knowledge utilization still fall short of human-level proficiency.
