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Found 11 papers, 4 papers with code
LLMs are Meaning-Typed Code Constructs
This is because it is currently unclear as to the right abstractions through which we should marry GenAI models with the nature of traditional programming code constructs.
One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI
Conversational agents have been gaining increasing popularity in recent years.
Scaling Down to Scale Up: A Cost-Benefit Analysis of Replacing OpenAI's LLM with Open Source SLMs in Production
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences.
Label Agnostic Pre-training for Zero-shot Text Classification
In addition, depending on the aspect (sentiment, topic, etc.)
The Jaseci Programming Paradigm and Runtime Stack: Building Scale-out Production Applications Easy and Fast
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models.
One Agent To Rule Them All: Towards Multi-agent Conversational AI
To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale.
Ranked #1 on
Multi-agent Integration
on BBAI Dataset
Conversational Response Selection
Multi-agent Integration
+1
Towards Personalized Intelligence at Scale
Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user.
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.
Outlier Detection for Improved Data Quality and Diversity in Dialog Systems
We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples.
Rethinking Numerical Representations for Deep Neural Networks
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency.
Data Collection for Dialogue System: A Startup Perspective
In this paper, we present a study of crowdsourcing methods for a user intent classification task in our deployed dialogue system.
