ICLR 2025

Published as a conference paper at ICLR 2025
SCOPE: A SELF-SUPERVISED FRAMEWORK FOR IM-
PROVING FAITHFULNESS IN CONDITIONAL TEXT GEN-
ERATION
Song Duong∗,1,4
Florian Le Bronnec∗,1,2
Alexandre Allauzen2
Vincent Guigue3
Alberto Lumbreras4
Laure Soulier1
Patrick Gallinari1,4
1Sorbonne Université, CNRS, ISIR, F-75005 Paris, France
2Miles Team, LAMSADE, Université Paris-Dauphine, Université PSL, CNRS, 75016 Paris, France
3AgroParisTech, UMR MIA-PS, Palaiseau, France
4Criteo AI Lab, Paris, France
ABSTRACT
Large Language Models (LLMs), when used for conditional text generation, often
produce hallucinations, i.e., information that is unfaithful or not grounded in the
input context. This issue arises in typical conditional text generation tasks, such
as text summarization and data-to-text generation, where the goal is to produce
fluent text based on contextual input. When fine-tuned on specific domains, LLMs
struggle to provide faithful answers to a given context, often adding information
or generating errors. One underlying cause of this issue is that LLMs rely on
statistical patterns learned from their training data. This reliance can interfere with
the model’s ability to stay faithful to a provided context, leading to the generation
of ungrounded information. We build upon this observation and introduce a novel
self-supervised method for generating a training set of unfaithful samples. We
then refine the model using a training process that encourages the generation of
grounded outputs over unfaithful ones, drawing on preference-based training. Our
approach leads to significantly more grounded text generation, outperforming
existing self-supervised techniques in faithfulness, as evaluated through automatic
metrics, LLM-based assessments, and human evaluations. Code is available at
https://github.com/sngdng/scope-faithfulness.
1
INTRODUCTION
Large Language Models (LLMs) are widely used for generating fluent and coherent text completions
based on input contexts (Brown et al....