# Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs

arXiv: https://arxiv.org/abs/2311.00681 · HTML: https://mlsanity.com/paper/2311.00681/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 1 Nov 2023.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://mlsanity.com/paper/2111.09525/) (2021)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization](https://mlsanity.com/paper/2112.08542/) (2021)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [Human-like Summarization Evaluation with ChatGPT](https://mlsanity.com/paper/2304.02554/) (2023)
- [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://mlsanity.com/paper/2305.18486/) (2023)
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://mlsanity.com/paper/2205.12854/) (2022)
- [The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey](https://mlsanity.com/paper/2104.14839/) (2021)
- [LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond](https://mlsanity.com/paper/2305.14540/) (2023)
- [Building Real-World Meeting Summarization Systems using Large Language Models: A Practical Perspective](https://mlsanity.com/paper/2310.19233/) (2023)
- [BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations](https://mlsanity.com/paper/2205.04438/) (2022)
- [Improving Factual Consistency in Summarization with Compression-Based Post-Editing](https://mlsanity.com/paper/2211.06196/) (2022)
- [AI Coach Assist: An Automated Approach for Call Recommendation in Contact Centers for Agent Coaching](https://mlsanity.com/paper/2305.17619/) (2023)
- [Developing a Production System for Purpose of Call Detection in Business Phone Conversations](https://mlsanity.com/paper/2205.06904/) (2022)

## Built on by (most cited first, 24 of 24)

- [A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://mlsanity.com/paper/2407.04069/) (2024)
- [LLM-based NLG Evaluation: Current Status and Challenges](https://mlsanity.com/paper/2402.01383/) (2024)
- [LLMs Can Generate a Better Answer by Aggregating Their Own Responses](https://mlsanity.com/paper/2503.04104/) (2025)
- [xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation](https://mlsanity.com/paper/2405.11874/) (2024)
- [DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2403.00896/) (2024)
- [Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study](https://mlsanity.com/paper/2406.11629/) (2024)
- [Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Tools](https://mlsanity.com/paper/2408.04650/) (2024)
- [A review of faithfulness metrics for hallucination assessment in Large Language Models](https://mlsanity.com/paper/2501.00269/) (2024)
- [Measuring AI "Slop" in Text](https://mlsanity.com/paper/2509.19163/) (2025)
- [Adaptive and Robust Cost-Aware Proof of Quality for Decentralized LLM Inference Networks](https://mlsanity.com/paper/2601.21189/) (2026)
- [UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models](https://mlsanity.com/paper/2402.14690/) (2024)
- [Less is More for Improving Automatic Evaluation of Factual Consistency](https://mlsanity.com/paper/2404.06579/) (2024)
- [KnowMT-Bench: Benchmarking Knowledge-Intensive Long-Form Question Answering in Multi-Turn Dialogues](https://mlsanity.com/paper/2509.21856/) (2025)
- [A Finite-Calibration Regime Map for LLM Judge Panels](https://mlsanity.com/paper/2606.01034/) (2026)
- [Better To Ask in English? Evaluating Factual Accuracy of Multilingual LLMs in English and Low-Resource Languages](https://mlsanity.com/paper/2504.20022/) (2025)
- [Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain](https://mlsanity.com/paper/2507.16974/) (2025)
- [GRP: Goal-Reversed Prompting for Zero-Shot Evaluation with LLMs](https://mlsanity.com/paper/2503.06139/) (2025)
- [Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games](https://mlsanity.com/paper/2504.12333/) (2025)
- [Faithfulness metric fusion: Improving the evaluation of LLM trustworthiness across domains](https://mlsanity.com/paper/2512.05700/) (2025)
- [Making Bias Non-Predictive: Training Robust LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2602.01528/) (2026)
- [Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation](https://mlsanity.com/paper/2602.07673/) (2026)
- [LLM-Based Code Documentation Generation and Multi-Judge Evaluation](https://mlsanity.com/paper/2606.09852/) (2026)
- [Four Ledgers, Not One Score: Responsible Communication of LLM-Judge Calibration in Biomedical ML](https://mlsanity.com/paper/2609.15015/) (2026)
- [How Much Do LLM-as-a-Judge Design Choices Matter? A Systematic Comparison of Prompt Designs, Rating Scales, and Models](https://mlsanity.com/paper/2610.05094/) (2026)

## Abstract (arXiv)

In recent years, Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities, surpassing those seen in earlier language models. A particularly intriguing application of LLMs is their role as evaluators for texts produced by various generative models. In this study, we delve into the potential of LLMs as reliable assessors of factual consistency in summaries generated by text-generation models. Initially, we introduce an innovative approach for factuality assessment using LLMs. This entails employing a singular LLM for the entirety of the question-answering-based factuality scoring process. Following this, we examine the efficacy of various LLMs in direct factuality scoring, benchmarking them against traditional measures and human annotations. Contrary to initial expectations, our results indicate a lack of significant correlations between factuality metrics and human evaluations, specifically for GPT-4 and PaLM-2. Notable correlations were only observed with GPT-3.5 across two factuality subcategories. These consistent findings across various factual error categories suggest a fundamental limitation in the current LLMs' capability to accurately gauge factuality. This version presents the information more concisely while maintaining the main points and findings of the original text.