Introduction-to-LLM: A Multi-Label Intelligent Routing System Based on DistilBERT

Introduces the Introduction-to-LLM project, which aims to solve the intelligent routing problem in multi-AI agent scenarios. It implements multi-label text classification by fine-tuning the lightweight DistilBERT model, routes user prompts to the appropriate subset of AI agents, achieves a Micro F1 score of 0.87, and provides a Gradio interactive interface for easy demonstration and use.

With the development of LLM and AI agent technologies, multi-agent integrated applications have increased. However, traditional keyword matching or rule engine routing methods perform poorly when handling complex and ambiguous inputs. This project proposes an intelligent routing solution based on deep learning for multi-label classification to solve the problem of assigning user prompts to the appropriate subset of AI agents.

DistilBERT is selected as the base model (high efficiency, strong expressive ability, easy to fine-tune); adopts a multi-label classification design and uses the BCEWithLogitsLoss function; builds a dataset with 502 annotated prompts (covering 20 agent categories, manually annotated, and handles class imbalance).

Adopts a transfer learning process (loading pre-trained weights, adding a classification head, layered learning rate, early stopping mechanism); uses Micro F1 as the main evaluation metric, achieving a score of 0.87; auxiliary metrics such as Macro F1, Hamming Loss, and Subset Accuracy can also be used.

Applicable to scenarios such as intelligent customer service systems (improving response efficiency), multi-agent collaboration platforms (activating multi-agent collaboration), resource optimization (saving computing resources), A/B testing and agent evaluation (providing data support), etc.

For class imbalance (class weights, data augmentation, sampling strategies); label correlation modeling (co-occurrence analysis, GNN, post-processing rules); real-time performance optimization (model quantization, batch inference, caching mechanism, model serviceization).

Try more powerful base models (RoBERTa, DeBERTa, etc.); explore few-shot/zero-shot learning to adapt to new agent categories; add context awareness (dialogue history, user portraits); optimize routing strategies using reinforcement learning.

This project demonstrates a practical multi-label classification application, achieves efficient intelligent routing through DistilBERT, has good performance and a Gradio interface for easy use, and provides a reference implementation for developers of intelligent routing systems.