Principle:Lm sys FastChat LLM Prompt Classification
| Field | Value |
|---|---|
| Page Type | Principle |
| Title | LLM Prompt Classification |
| Repository | lm-sys/FastChat |
| Workflow | Arena Data Analysis |
| Domains | NLP, Classification |
| Knowledge Sources | fastchat/serve/monitor/classify/category.py, fastchat/serve/monitor/classify/label.py, OpenAI API documentation |
| Last Updated | 2026-02-07 14:00 GMT |
Overview
This principle describes the approach of using large language models themselves as classifiers to categorize conversation prompts into semantic categories and difficulty levels. Rather than training a dedicated classification model, the system formulates classification as a prompt-completion task, leveraging the in-context learning capabilities of frontier LLMs to produce structured labels from raw conversation data. This technique is central to analyzing arena battle data by topic, complexity, and domain.
Description
LLM-as-Classifier Prompting
The core idea is to present an LLM with a conversation prompt and a carefully engineered system instruction that directs it to output a structured classification response. The system prompt defines the taxonomy of categories, provides definitions for each category, and specifies the expected output format (typically JSON). By treating classification as a generation task, the system avoids the overhead of training, maintaining, and deploying a separate classifier model. The LLM's broad world knowledge enables it to handle diverse prompt topics without domain-specific training data.
Multi-Label Categorization Taxonomies
Arena prompts span a wide range of topics -- coding, creative writing, mathematics, reasoning, roleplay, and more. The classification system employs a multi-label taxonomy where a single prompt may belong to multiple categories simultaneously. For example, a prompt asking for a Python solution to a mathematical problem would receive both "Coding" and "Math" labels. The taxonomy is defined declaratively and can be updated without retraining, simply by modifying the system prompt.
Batch API Processing
Classifying thousands of arena conversations requires efficient API utilization. The system employs batch API processing, submitting classification requests in bulk rather than one at a time. This takes advantage of provider-side batch endpoints that offer higher throughput and lower cost per request. Results are collected asynchronously and matched back to their source conversations by unique identifiers.
Confidence Thresholding
Not all LLM-generated classifications are equally reliable. The system may apply confidence thresholding by examining the model's self-reported confidence scores or by requiring agreement across multiple classification passes. Prompts that fall below the confidence threshold are flagged for manual review or excluded from category-specific analyses, ensuring that downstream statistics are not corrupted by uncertain labels.
Criteria-Based Difficulty Assessment
Beyond topical categorization, the system also assesses prompt difficulty along multiple criteria such as domain specificity, reasoning depth, required context length, and ambiguity. Difficulty labels enable stratified analysis of model performance -- revealing, for example, that a model excels on simple factual queries but underperforms on complex multi-step reasoning tasks. The criteria are encoded in the classification prompt and the LLM produces a structured difficulty rating alongside the category labels.
Theoretical Basis
LLM-based classification leverages in-context learning (ICL), the ability of large language models to perform new tasks from natural language descriptions and optional examples without parameter updates. Brown et al. (2020) demonstrated that sufficiently large language models can achieve competitive classification accuracy in zero-shot and few-shot settings. The approach is theoretically grounded in the observation that pre-training on diverse text corpora implicitly teaches the model to recognize and categorize textual patterns. Prompt engineering controls the output taxonomy by constraining the model's generation to a predefined label set, effectively converting an open-ended generative model into a structured classifier. The key trade-off is between the flexibility and convenience of LLM-based classification versus the higher per-sample cost compared to lightweight specialized classifiers.
Related Pages
- Implementation:Lm_sys_FastChat_Category_Classifier
- Implemented by: Implementation:Lm_sys_FastChat_Category_Classifier
- Implementation:Lm_sys_FastChat_Category_Label_Pipeline
- Implemented by: Implementation:Lm_sys_FastChat_Category_Label_Pipeline
- Implementation:Lm_sys_FastChat_Criteria_Labeling
- Implemented by: Implementation:Lm_sys_FastChat_Criteria_Labeling