# Diagnosing Robotics Systems Issues with Large Language Models

arXiv: https://arxiv.org/abs/2410.09084 · HTML: https://mlsanity.com/paper/2410.09084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers](https://mlsanity.com/paper/2210.17323/) (2022)
- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [ExpertPrompting: Instructing Large Language Models to be Distinguished Experts](https://mlsanity.com/paper/2305.14688/) (2023)
- [Value-aware Quantization for Training and Inference of Neural Networks](https://mlsanity.com/paper/1804.07802/) (2018)
- [LoRA Land: 310 Fine-tuned LLMs that Rival GPT-4, A Technical Report](https://mlsanity.com/paper/2405.00732/) (2024)
- [LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model](https://mlsanity.com/paper/2111.09564/) (2021)
- [Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models](https://mlsanity.com/paper/2102.11570/) (2021)
- [Learning Representations on Logs for AIOps](https://mlsanity.com/paper/2308.11526/) (2023)

## Abstract (arXiv)

Quickly resolving issues reported in industrial applications is crucial to minimize economic impact. However, the required data analysis makes diagnosing the underlying root causes a challenging and time-consuming task, even for experts. In contrast, large language models (LLMs) excel at analyzing large amounts of data. Indeed, prior work in AI-Ops demonstrates their effectiveness in analyzing IT systems. Here, we extend this work to the challenging and largely unexplored domain of robotics systems. To this end, we create SYSDIAGBENCH, a proprietary system diagnostics benchmark for robotics, containing over 2500 reported issues. We leverage SYSDIAGBENCH to investigate the performance of LLMs for root cause analysis, considering a range of model sizes and adaptation techniques. Our results show that QLoRA finetuning can be sufficient to let a 7B-parameter model outperform GPT-4 in terms of diagnostic accuracy while being significantly more cost-effective. We validate our LLM-as-a-judge results with a human expert study and find that our best model achieves similar approval ratings as our reference labels.