We're sunsetting PodQuest on 2025-07-28. Thank you for your support!
Export Podcast Subscriptions

Papers Read on AI

Keeping you up to date with the latest trends and best performing architectures in this fast evolvin

Episodes

Total: 205

Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code

In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-q

We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achie

The conventional recipe for maximizing model accuracy is to (1) train multiple models with various h

There are two common ways in which developers are incorporating proprietary and domain-specific data

Software engineers are increasingly adding semantic search capabilities to applications using a stra

Language agents perform complex tasks by using tools to execute each step precisely. However, most e

To extend the context length of Transformer-based large language models (LLMs) and improve comprehen

Retrieval Augmented Generation (RAG) enhances the abilities of Large Language Models (LLMs) by enabl

Simultaneous speech-to-speech translation (Simul-S2ST, a.k.a streaming speech translation) outputs t

We introduce VASA, a framework for generating lifelike talking faces with appealing visual affective

The misuse of large language models (LLMs) has drawn significant attention from the general public a

Large Language Models (LLMs) are often described as being instances of foundation models - that is,

We introduce Buffer of Thoughts (BoT), a novel and versatile thought-augmented reasoning approach fo

Knowledge Graphs (KGs) represent human-crafted factual knowledge in the form of triplets (head, rela

We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo (April 2024) and GPT-4

With impressive achievements made, artificial intelligence is on the path forward to artificial gene

Generative pre-trained large language models (LLMs) have demonstrated impressive performance over a

Large language models (LLMs) often generate content that contains factual errors when responding to

End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set