Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-OASIS
After the introduction of Large Language Models (LLMs), there have been
substantial improvements in the performance of Natural Language Generation
(NLG) tasks, including Text Summarization and Machine Translation. However,
LLMs still produce outputs containing hallucinations, that is, content not
grounded in factual information. Therefore, developing methods to assess the
factuality of LLMs has become urgent.
  Indeed, resources for factuality evaluation have recently emerged. Although
challenging, these resources face one or more of the following limitations: (i)
they are tailored to a specific task or domain; (ii) they are limited in size,
thereby preventing the training of new factuality evaluators; (iii) they are
designed for simpler verification tasks, such as claim verification.
  To address these issues, we introduce LLM-Oasis, to the best of our knowledge
the largest resource for training end-to-end factuality evaluators. LLM-Oasis
is constructed by extracting claims from Wikipedia, falsifying a subset of
these claims, and generating pairs of factual and unfactual texts. We then rely
on human annotators to both validate the quality of our dataset and to create a
gold standard test set for benchmarking factuality evaluation systems.
  Our experiments demonstrate that LLM-Oasis presents a significant challenge
for state-of-the-art LLMs, with GPT-4o achieving up to 60% accuracy in our
proposed end-to-end factuality evaluation task, highlighting its potential to
drive future research in the field.
674db37adefe7edb9f5e0028
Alessandro Scirè
claimed_verified
2024-12-05T09:28:58.122Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/65ef2286d08d92badac66e34/-FtE-wVpqlbZtNLhEOCc_.jpeg
Alessandro Scirè
user
alescire94
674db37adefe7edb9f5e0029
Andrei Stefan Bejgu
claimed_verified
2024-12-05T09:28:59.956Z
633ee9704be90e06da1eb774
https://cdn-avatars.huggingface.co/v1/production/uploads/633ee9704be90e06da1eb774/xU6gXNHkBub0hAQb9RRsR.jpeg
Andrei Stefan Bejgu
user
andreim14
674db37adefe7edb9f5e002a
Simone Tedeschi
claimed_verified
2024-12-23T11:11:53.665Z
61b85aa99ba538c73a7dc78b
https://cdn-avatars.huggingface.co/v1/production/uploads/61b85aa99ba538c73a7dc78b/gWxtQAvOYn7cXgE_nAy0p.jpeg
Simone Tedeschi
user
sted97
674db37adefe7edb9f5e002b
Karim Ghonim
674db37adefe7edb9f5e002c
Federico Martelli
674db37adefe7edb9f5e002d
Roberto Navigli
Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-OASIS
After the introduction of Large Language Models (LLMs), there have been
substantial improvements in the performance of Natural Language Generation
(NLG) tasks, including Text Summarization and Machine Translation. However,
LLMs still produce outputs containing hallucinations, that is, content not
grounded in factual information. Therefore, developing methods to assess the
factuality of LLMs has become urgent.
  Indeed, resources for factuality evaluation have recently emerged. Although
challenging, these resources face one or more of the following limitations: (i)
they are tailored to a specific task or domain; (ii) they are limited in size,
thereby preventing the training of new factuality evaluators; (iii) they are
designed for simpler verification tasks, such as claim verification.
  To address these issues, we introduce LLM-Oasis, to the best of our knowledge
the largest resource for training end-to-end factuality evaluators. LLM-Oasis
is constructed by extracting claims from Wikipedia, falsifying a subset of
these claims, and generating pairs of factual and unfactual texts. We then rely
on human annotators to both validate the quality of our dataset and to create a
gold standard test set for benchmarking factuality evaluation systems.
  Our experiments demonstrate that LLM-Oasis presents a significant challenge
for state-of-the-art LLMs, with GPT-4o achieving up to 60% accuracy in our
proposed end-to-end factuality evaluation task, highlighting its potential to
drive future research in the field.
LLM-Oasis, a comprehensive dataset for factuality evaluation, challenges state-of-the-art LLMs by requiring high accuracy in distinguishing factual from unfactual texts.
LLMs Natural Language Generation Text Summarization Machine Translation hallucinations factuality evaluation LLM-Oasis claims falsification human annotators benchmarking