CheckEmbed: Effective Verification of LLM Solutions to Open-Ended Tasks

TL;DR

CheckEmbed (CE) is a scalable, embedding-based verification method for LLM outputs that effectively detects hallucinations and generalizes across modalities, outperforming prior token-level approaches.

Contribution

Introducing CheckEmbed, a novel embedding-based verification technique that improves accuracy and scalability for evaluating LLM solutions on open-ended tasks.

Findings

CE reliably detects hallucinations in LLM outputs.

CE outperforms token-based methods like BERTScore.

CE generalizes to non-text modalities such as vision.

Abstract

Large Language Models (LLMs) are transforming a wide range of domains, yet verifying their outputs remains a significant challenge, especially for complex open-ended tasks such as consolidation, summarization, and knowledge extraction. To address this, we introduce CheckEmbed (CE): a simple, scalable, and accurate verification method. CE reduces each LLM answer to a single embedding vector using powerful modern embedding LLM models like SFR-Embedding-Mistral. Prior methods such as BERTScore and SelfCheckGPT relied on weaker encoders like BERT, forcing them to operate at token or sentence granularity. In contrast, CE performs fast, semantically rich comparisons directly at the whole-answer level, overcoming key limitations in both accuracy and scalability. We conduct a comprehensive design and time complexity analysis across 13 verification baselines, including classical text scorers (e.g., BLEU), stability-based methods (e.g., SelfCheckGPT), and generative evaluators (e.g., LLM-as-a-Judge), which highlights the effectiveness, efficiency, versatility, and simplicity of CE. Empirical results show that CE reliably detects hallucinations in both closed and open-ended tasks. We further present evidence that CE generalizes beyond text to other modalities such as vision, establishing it as a practical and versatile verification framework.