Wed May 10 2023
Tue May 09 2023

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

computer science
cost reduction
model optimization
natural language processing
language model

FrugalGPT proposes LLM cascade strategy to reduce cost and improve accuracy of using large language models

FrugalGPT can reduce the cost of using large language models by up to 98% while maintaining its performance or improve the accuracy over GPT-4 by 4% with the same cost

NerfAcc: Efficient Sampling Accelerates NeRFs

computer science
training time reduction
model optimization
computer graphics
3D modeling

NerfAcc proposes advanced efficient sampling techniques to accelerate NeRFs training

NerfAcc can reduce the training time of several recent NeRF methods by 1.5x to 20x with minimal modifications to the existing codebase

Recommender Systems with Generative Retrieval

computer science
model optimization
data processing
recommender systems
information retrieval

The paper proposes a new single-stage paradigm for training recommender systems using generative retrieval models

The proposed single-stage paradigm improves the results achieved by current SOTA models on the Amazon dataset and offers better generalization for cold-start items

Large Language Model Programs

AI programming
Natural Language Processing
Evidence-supported question-answering

Methodology of embedding LLMs in a classic program to perform more complex tasks

Can expand the capabilities of LLMs at a much lower cost than finetuning, providing a 6.4% improvement over the baseline for question-answering tasks.

SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models

Large Language Models
Natural Language Processing
Computer Vision
Text-to-image generation

Proposes a parameter-efficient fine-tuning approach for pre-trained diffusion models to improve the capacity for narrative prompts in text-to-image generation

Enables diffusion models to understand and reason concise natural language without image quality degradation, making text-to-image diffusion models easier to use with better user experience. The approach has the potential to further advance the development of user-friendly text-to-image generation models.

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