# REFLEX: Reference-Free Evaluation of Log Summarization via Large Language Model Judgment

arXiv: https://arxiv.org/abs/2511.07458 · HTML: https://mlsanity.com/paper/2511.07458/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics](https://mlsanity.com/paper/2008.06448/) (2020)
- [A Survey on Large Language Models for Software Engineering](https://mlsanity.com/paper/2312.15223/) (2023)
- [LILAC: Log Parsing using LLMs with Adaptive Parsing Cache](https://mlsanity.com/paper/2310.01796/) (2023)
- [A Large-Scale Evaluation for Log Parsing Techniques: How Far Are We?](https://mlsanity.com/paper/2308.10828/) (2023)
- [LogParser-LLM: Advancing Efficient Log Parsing with Large Language Models](https://mlsanity.com/paper/2408.13727/) (2024)
- [RAGLog: Log Anomaly Detection using Retrieval Augmented Generation](https://mlsanity.com/paper/2311.05261/) (2023)
- [A Survey of AIOps for Failure Management in the Era of Large Language Models](https://mlsanity.com/paper/2406.11213/) (2024)
- [An Assessment of ChatGPT on Log Data](https://mlsanity.com/paper/2309.07938/) (2023)
- [A Comparative Study on Large Language Models for Log Parsing](https://mlsanity.com/paper/2409.02474/) (2024)
- [CrashEventLLM: Predicting System Crashes with Large Language Models](https://mlsanity.com/paper/2407.15716/) (2024)
- [HELP: Hierarchical Embeddings-based Log Parsing](https://mlsanity.com/paper/2408.08300/) (2024)
- [Enhancing Reasoning Capacity of SLM using Cognitive Enhancement](https://mlsanity.com/paper/2404.01135/) (2024)

## Abstract (arXiv)

Evaluating log summarization systems is challenging due to the lack of high-quality reference summaries and the limitations of existing metrics like ROUGE and BLEU, which depend on surface-level lexical overlap. We introduce REFLEX, a reference-free evaluation metric for log summarization based on large language model (LLM) judgment. REFLEX uses LLMs as zero-shot evaluators to assess summary quality along dimensions such as relevance, informativeness, and coherence, without requiring gold-standard references or human annotations. We show that REFLEX produces stable, interpretable, and fine-grained evaluations across multiple log summarization dataset, and more effectively distinguishes model outputs than traditional metrics. REFLEX provides a scalable alternative for evaluating log summaries in real-world settings where reference data is scarce or unavailable.