ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models

TL;DR

ERBench leverages entity-relationship databases with integrity constraints to create a dynamic, complex, and verifiable benchmark for evaluating large language models' reasoning and answer accuracy.

Contribution

This paper introduces ERBench, a novel benchmark that uses relational databases with integrity constraints to evaluate LLMs' reasoning and answer verification capabilities.

Findings

ERBench effectively evaluates LLMs across multiple domains.

It verifies answers by checking for correct keywords and reasoning.

ERBench supports continuous, multimodal, and complex question evaluation.

Abstract

Large language models (LLMs) have achieved unprecedented performances in various applications, yet evaluating them is still challenging. Existing benchmarks are either manually constructed or are automatic, but lack the ability to evaluate the thought process of LLMs with arbitrary complexity. We contend that utilizing existing relational databases based on the entity-relationship (ER) model is a promising approach for constructing benchmarks as they contain structured knowledge that can be used to question LLMs. Unlike knowledge graphs, which are also used to evaluate LLMs, relational databases have integrity constraints that can be used to better construct complex in-depth questions and verify answers: (1) functional dependencies can be used to pinpoint critical keywords that an LLM must know to properly answer a given question containing certain attribute values; and (2) foreign key constraints can be used to join relations and construct multi-hop questions, which can be arbitrarily long and used to debug intermediate answers. We thus propose ERBench, which uses these integrity constraints to convert any database into an LLM benchmark. ERBench supports continuous evaluation as databases change, multimodal questions, and various prompt engineering techniques. In our experiments, we construct LLM benchmarks using databases of multiple domains and make an extensive comparison of contemporary LLMs. We show how ERBench can properly evaluate any LLM by not only checking for answer correctness, but also effectively verifying the rationales by looking for the right keywords.