# Measuring Reliability of Large Language Models through Semantic Consistency

arXiv: https://arxiv.org/abs/2211.05853 · HTML: https://mlsanity.com/paper/2211.05853/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 28 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 24 of 24)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [How Can We Know What Language Models Know?](https://mlsanity.com/paper/1911.12543/) (2019)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Measuring and Improving Consistency in Pretrained Language Models](https://mlsanity.com/paper/2102.01017/) (2021)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Factuality Enhanced Language Models for Open-Ended Text Generation](https://mlsanity.com/paper/2206.04624/) (2022)
- [Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2102.00894/) (2021)
- [Prompt Consistency for Zero-Shot Task Generalization](https://mlsanity.com/paper/2205.00049/) (2022)
- [Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema](https://mlsanity.com/paper/2104.08161/) (2021)
- [Quality Controlled Paraphrase Generation](https://mlsanity.com/paper/2203.10940/) (2022)
- [Novelty Controlled Paraphrase Generation with Retrieval Augmented Conditional Prompt Tuning](https://mlsanity.com/paper/2202.00535/) (2022)
- [P-Adapters: Robustly Extracting Factual Information from Language Models with Diverse Prompts](https://mlsanity.com/paper/2110.07280/) (2021)
- [Factual Consistency of Multilingual Pretrained Language Models](https://mlsanity.com/paper/2203.11552/) (2022)
- [Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models](https://mlsanity.com/paper/2108.06665/) (2021)

## Built on by (most cited first, 30 of 38)

- [Knowledge Conflicts for LLMs: A Survey](https://mlsanity.com/paper/2403.08319/) (2024)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [Knowledge Mechanisms in Large Language Models: A Survey and Perspective](https://mlsanity.com/paper/2407.15017/) (2024)
- [Consistency Analysis of ChatGPT](https://mlsanity.com/paper/2303.06273/) (2023)
- [Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs](https://mlsanity.com/paper/2305.14279/) (2023)
- [Predicting Question-Answering Performance of Large Language Models through Semantic Consistency](https://mlsanity.com/paper/2311.01152/) (2023)
- [Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks](https://mlsanity.com/paper/2410.11005/) (2024)
- [On the Robustness of Agentic Function Calling](https://mlsanity.com/paper/2504.00914/) (2025)
- [Semantic Consistency for Assuring Reliability of Large Language Models](https://mlsanity.com/paper/2308.09138/) (2023)
- [Enhancing Semantic Consistency of Large Language Models through Model Editing: An Interpretability-Oriented Approach](https://mlsanity.com/paper/2501.11041/) (2025)
- [Consistency in Language Models: Current Landscape, Challenges, and Future Directions](https://mlsanity.com/paper/2505.00268/) (2025)
- [Improving Consistency in Large Language Models through Chain of Guidance](https://mlsanity.com/paper/2502.15924/) (2025)
- [Self-Consistency of Large Language Models under Ambiguity](https://mlsanity.com/paper/2310.13439/) (2023)
- [A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios](https://mlsanity.com/paper/2408.01963/) (2024)
- [LF-Steering: Latent Feature Activation Steering for Enhancing Semantic Consistency in Large Language Models](https://mlsanity.com/paper/2501.11036/) (2025)
- [Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs](https://mlsanity.com/paper/2407.04173/) (2024)
- [When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents](https://mlsanity.com/paper/2605.11928/) (2026)
- [Statistical Knowledge Assessment for Large Language Models](https://mlsanity.com/paper/2305.10519/) (2023)
- [STED and Consistency Scoring: A Framework for Evaluating LLM Structured Output Reliability](https://mlsanity.com/paper/2512.23712/) (2025)
- [Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation](https://mlsanity.com/paper/2305.17819/) (2023)
- [LUNA: A Model-Based Universal Analysis Framework for Large Language Models](https://mlsanity.com/paper/2310.14211/) (2023)
- [EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium](https://mlsanity.com/paper/2605.09278/) (2026)
- [SaGE: Evaluating Moral Consistency in Large Language Models](https://mlsanity.com/paper/2402.13709/) (2024)
- [Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models](https://mlsanity.com/paper/2503.18562/) (2025)
- [Consistency as a Testable Property: Statistical Methods to Evaluate AI Agent Reliability](https://mlsanity.com/paper/2605.10516/) (2026)
- [Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards](https://mlsanity.com/paper/2510.04392/) (2025)
- [GSM-SEM: Benchmark and Framework for Generating Semantically Variant Augmentations](https://mlsanity.com/paper/2605.07053/) (2026)
- [OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories](https://mlsanity.com/paper/2607.17082/) (2026)
- [SimCT: A Simple Consistency Test Protocol in LLMs Development Lifecycle](https://mlsanity.com/paper/2407.17150/) (2024)
- [Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering](https://mlsanity.com/paper/2410.15440/) (2024)

## Abstract (arXiv)

While large pretrained language models (PLMs) demonstrate incredible fluency and performance on many natural language tasks, recent work has shown that well-performing PLMs are very sensitive to what prompts are feed into them. Even when prompts are semantically identical, language models may give very different answers. When considering safe and trustworthy deployments of PLMs we would like their outputs to be consistent under prompts that mean the same thing or convey the same intent. While some work has looked into how state-of-the-art PLMs address this need, they have been limited to only evaluating lexical equality of single- or multi-word answers and do not address consistency of generative text sequences. In order to understand consistency of PLMs under text generation settings, we develop a measure of semantic consistency that allows the comparison of open-ended text outputs. We implement several versions of this consistency metric to evaluate the performance of a number of PLMs on paraphrased versions of questions in the TruthfulQA dataset, we find that our proposed metrics are considerably more consistent than traditional metrics embodying lexical consistency, and also correlate with human evaluation of output consistency to a higher degree.