LLM Benchmarks Dashboard: A One-Stop Evaluation Platform for RCA Capabilities of 4500+ Models

This article introduces the LLM Benchmarks Dashboard, an open-source evaluation platform for Root Cause Analysis (RCA) scenarios. Covering over 4500 models, the platform assesses LLMs' engineering practical capabilities from 8 dimensions including code understanding and log analysis, providing engineers and researchers with intuitive references for model selection and bridging the gap between general evaluations and engineering practices.

As LLMs are deployed across industries, enterprises rely on AI to assist in troubleshooting. However, general evaluations (such as MMLU and HumanEval) cannot reflect performance in real engineering scenarios. RCA tasks require models to have multiple capabilities like code understanding and log parsing, and to work collaboratively under incomplete context and time pressure. Therefore, a specialized RCA evaluation tool is crucial.

The LLM Benchmarks Dashboard, developed by bhanvimenghani, is an open-source web platform that includes evaluation data for over 4500 models. Its technical architecture adopts a front-end and back-end separation approach: the front-end is based on React + TypeScript, providing task leaderboards, model comparisons, and score visualization; the back-end uses Python FastAPI to provide API services and score calculation; the data layer uses JSON to store model scores, task definitions, etc., which is convenient for updates and maintenance.

The platform is designed around RCA needs with 8 core dimensions (including weights): 1. Code Understanding (15%): Evaluate the ability to read and understand codebases; 2. Log Analysis (20%): Extract key log information and identify anomalies; 3. Metric Interpretation (15%): Understand the meaning of monitoring metrics and abnormal trends; 4. Causal Reasoning (20%): Identify true causal relationships in the system; 5. Pattern Recognition (10%): Match historical failure patterns; 6. Context Synthesis (10%): Integrate multi-source information to form a failure picture; 7. Root Cause Identification (5%): Locate the root cause; 8. Solution Recommendation (5%): Propose repair suggestions.

Typical users and value of the platform: SRE/operation and maintenance teams can evaluate candidate models to avoid selection risks; AI product managers can formulate scientific selection strategies; researchers can analyze the impact of different architectures/training strategies on RCA capabilities; model developers can optimize model shortcomings through fine-grained feedback.

Current limitations: Data is stored using static JSON, which may require migrating to a database as the number of models grows; the evaluation dataset and scoring standards need to be improved. Future directions: Support real-time evaluation APIs; add multi-modal capability evaluation; introduce time-series analysis dimensions; establish an RCA capability certification system.

The LLM Benchmarks Dashboard bridges the gap between general evaluations and engineering practices, providing a scientific assessment framework for RCA scenarios. In today's rapid development of AIOps, this platform will help the industry establish clear capability standards and promote the real implementation of LLMs in the field of reliability engineering.