On A Scale From 1 to 5: Quantifying Hallucination in Faithfulness Evaluation

TL;DR

This paper explores automated methods for evaluating the faithfulness of natural language generation, focusing on quantifying hallucinations using large language models and natural language inference, with practical insights on deployment costs.

Contribution

It introduces a rubric-based scoring method using LLMs for faithfulness evaluation, compares different models, and proposes techniques for synthetic unfaithful data generation.

Findings

GPT-4 accurately judges factual consistency.

Tuning NLI models on synthetic data improves performance.

Insights on latency and cost of deployment.

Abstract

Hallucination has been a popular topic in natural language generation (NLG). In real-world applications, unfaithful content can result in poor data quality or loss of trust from end users. Thus, it is crucial to fact-check before adopting NLG for production usage, which can be expensive if done manually. In this paper, we investigate automated faithfulness evaluation in guided NLG. We developed a rubric template and used large language models (LLMs) to score the generation on quantifiable scales. We compared popular LLMs as well as widely adopted natural language inference (NLI) models in scoring quality and sensitivity. In addition, we developed methods for the generation of synthetic unfaithful data, as well as heuristics to quantify the percentage of hallucination. Our results on 4 travel-domain industry dataset show that GPT-4 can provide accurate judgement and explanation of whether a source and a generation are factually consistent. Furthermore, we found that tuning NLI models on synthetic data can improve performance. Lastly, we present insights on the latency and cost of deploying such a system.