HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
Despite their impressive ability to generate high-quality and fluent text,
generative large language models (LLMs) also produce hallucinations: statements
that are misaligned with established world knowledge or provided input context.
However, measuring hallucination can be challenging, as having humans verify
model generations on-the-fly is both expensive and time-consuming. In this
work, we release HALoGEN, a comprehensive hallucination benchmark consisting
of: (1) 10,923 prompts for generative models spanning nine domains including
programming, scientific attribution, and summarization, and (2) automatic
high-precision verifiers for each use case that decompose LLM generations into
atomic units, and verify each unit against a high-quality knowledge source. We
use this framework to evaluate ~150,000 generations from 14 language models,
finding that even the best-performing models are riddled with hallucinations
(sometimes up to 86% of generated atomic facts depending on the domain). We
further define a novel error classification for LLM hallucinations based on
whether they likely stem from incorrect recollection of training data (Type A
errors), or incorrect knowledge in training data (Type B errors), or are
fabrication (Type C errors). We hope our framework provides a foundation to
enable the principled study of why generative models hallucinate, and advances
the development of trustworthy large language models.
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Abhilasha Ravichander
claimed_verified
2025-01-15T17:13:38.972Z
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Abhilasha Ravichander
user
lasha-nlp
678731bbe24feaaa753768b6
Shrusti Ghela
admin_assigned
2025-01-15T17:06:55.911Z
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Shrusti Ghela
user
shrustighela
678731bbe24feaaa753768b7
David Wadden
admin_assigned
2025-01-15T17:06:50.718Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1609884433338-5ff4e2a1463be69ae4bd42bd.jpeg
David Wadden
user
dwadden
678731bbe24feaaa753768b8
Yejin Choi
admin_assigned
2025-01-15T17:06:44.995Z
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/avatars/52e54bdd6a1fb6c774a40cd70f3d7925.svg
Yejin Choi
user
yejinchoinka
HALoGEN: Fantastic LLM Hallucinations and Where to Find Them
Despite their impressive ability to generate high-quality and fluent text,
generative large language models (LLMs) also produce hallucinations: statements
that are misaligned with established world knowledge or provided input context.
However, measuring hallucination can be challenging, as having humans verify
model generations on-the-fly is both expensive and time-consuming. In this
work, we release HALoGEN, a comprehensive hallucination benchmark consisting
of: (1) 10,923 prompts for generative models spanning nine domains including
programming, scientific attribution, and summarization, and (2) automatic
high-precision verifiers for each use case that decompose LLM generations into
atomic units, and verify each unit against a high-quality knowledge source. We
use this framework to evaluate ~150,000 generations from 14 language models,
finding that even the best-performing models are riddled with hallucinations
(sometimes up to 86% of generated atomic facts depending on the domain). We
further define a novel error classification for LLM hallucinations based on
whether they likely stem from incorrect recollection of training data (Type A
errors), or incorrect knowledge in training data (Type B errors), or are
fabrication (Type C errors). We hope our framework provides a foundation to
enable the principled study of why generative models hallucinate, and advances
the development of trustworthy large language models.
HALoGEN is a benchmark for identifying and categorizing hallucinations in generative language models by verifying atomic units against knowledge sources.
generative large language models (LLMs) hallucinations HALoGEN automatic verifiers atomic units knowledge source Type A errors Type B errors Type C errors