ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language
  Models
Large language models (LLMs) exhibit hallucinations in long-form
question-answering tasks across various domains and wide applications. Current
hallucination detection and mitigation datasets are limited in domains and
sizes, which struggle to scale due to prohibitive labor costs and insufficient
reliability of existing hallucination annotators. To facilitate the scalable
oversight of LLM hallucinations, this paper introduces an iterative
self-training framework that simultaneously and progressively scales up the
hallucination annotation dataset and improves the accuracy of the hallucination
annotator. Based on the Expectation Maximization (EM) algorithm, in each
iteration, the framework first applies a hallucination annotation pipeline to
annotate a scaled dataset and then trains a more accurate hallucination
annotator on the dataset. This new hallucination annotator is adopted in the
hallucination annotation pipeline used for the next iteration. Extensive
experimental results demonstrate that the finally obtained hallucination
annotator with only 7B parameters surpasses the performance of GPT-4 and
obtains new state-of-the-art hallucination detection results on HaluEval and
HalluQA by zero-shot inference. Such an annotator can not only evaluate the
hallucination levels of various LLMs on the large-scale dataset but also help
to mitigate the hallucination of LLMs generations, with the Natural Language
Inference (NLI) metric increasing from 25% to 37% on HaluEval.
668cb372e7cc32ad4d3a6837
Yuzhe Gu
claimed_verified
2024-07-09T07:42:02.824Z
6601196cc91ba4c08ad6e270
https://cdn-avatars.huggingface.co/v1/production/uploads/6601196cc91ba4c08ad6e270/X2YPNzUOQXBz5Gv-xR9LW.jpeg
yuzhe gu
user
vanilla1116
668cb372e7cc32ad4d3a6838
Ziwei Ji
admin_assigned
2024-07-09T10:37:27.463Z
6218d7c868ec944467c5f607
/avatars/3d2ceb17e02967aea5ef9118d8514136.svg
Ziwei Ji
user
ruguodayu
668cb372e7cc32ad4d3a6839
Wenwei Zhang
admin_assigned
2024-07-09T10:37:33.406Z
64e8505321540e1da3226b54
/avatars/18958b8406d1ce492b54c1c839f18c54.svg
Wenwei Zhang
user
ZwwWayne
668cb372e7cc32ad4d3a683a
Chengqi Lyu
668cb372e7cc32ad4d3a683b
Dahua Lin
admin_assigned
2024-07-09T10:37:50.609Z
636317ed80c1a705a6eff396
/avatars/3db090e101b916d9256d0d3e043db71d.svg
Dahua Lin
user
lindahua
668cb372e7cc32ad4d3a683c
Kai Chen
ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language
  Models
Large language models (LLMs) exhibit hallucinations in long-form
question-answering tasks across various domains and wide applications. Current
hallucination detection and mitigation datasets are limited in domains and
sizes, which struggle to scale due to prohibitive labor costs and insufficient
reliability of existing hallucination annotators. To facilitate the scalable
oversight of LLM hallucinations, this paper introduces an iterative
self-training framework that simultaneously and progressively scales up the
hallucination annotation dataset and improves the accuracy of the hallucination
annotator. Based on the Expectation Maximization (EM) algorithm, in each
iteration, the framework first applies a hallucination annotation pipeline to
annotate a scaled dataset and then trains a more accurate hallucination
annotator on the dataset. This new hallucination annotator is adopted in the
hallucination annotation pipeline used for the next iteration. Extensive
experimental results demonstrate that the finally obtained hallucination
annotator with only 7B parameters surpasses the performance of GPT-4 and
obtains new state-of-the-art hallucination detection results on HaluEval and
HalluQA by zero-shot inference. Such an annotator can not only evaluate the
hallucination levels of various LLMs on the large-scale dataset but also help
to mitigate the hallucination of LLMs generations, with the Natural Language
Inference (NLI) metric increasing from 25% to 37% on HaluEval.
An iterative self-training framework enhances the detection and mitigation of hallucinations in large language models by scaling the annotation dataset and improving the annotator's accuracy.
large language models hallucinations self-training framework Expectation Maximization hallucination annotation pipeline GPT-4 HaluEval HalluQA Natural Language Inference