# Challenges and Responses in the Practice of Large Language Models

arXiv: https://arxiv.org/abs/2408.09416 · HTML: https://mlsanity.com/paper/2408.09416/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation](https://mlsanity.com/paper/2109.01048/) (2021)
- [MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models](https://mlsanity.com/paper/2312.14480/) (2023)

## Built on by (most cited first, 2 of 2)

- [Generative AI in Transportation Planning: A Survey](https://mlsanity.com/paper/2503.07158/) (2025)
- [TTF: Temporal Token Fusion for Efficient Video-Language Model](https://mlsanity.com/paper/2605.07355/) (2026)

## Abstract (arXiv)

This paper carefully summarizes extensive and profound questions from all walks of life, focusing on the current high-profile AI field, covering multiple dimensions such as industry trends, academic research, technological innovation and business applications. This paper meticulously curates questions that are both thought-provoking and practically relevant, providing nuanced and insightful answers to each. To facilitate readers' understanding and reference, this paper specifically classifies and organizes these questions systematically and meticulously from the five core dimensions of computing power infrastructure, software architecture, data resources, application scenarios, and brain science. This work aims to provide readers with a comprehensive, in-depth and cutting-edge AI knowledge framework to help people from all walks of life grasp the pulse of AI development, stimulate innovative thinking, and promote industrial progress.