Improving Factual Consistency of Text Summarization by Adversarially Decoupling Comprehension and Embellishment Abilities of LLMs
CoRR(2023)
摘要
Despite the recent progress in text summarization made by large language
models (LLMs), they often generate summaries that are factually inconsistent
with original articles, known as "hallucinations" in text generation. Unlike
previous small models (e.g., BART, T5), current LLMs make fewer silly mistakes
but more sophisticated ones, such as imposing cause and effect, adding false
details, and overgeneralizing, etc. These hallucinations are challenging to
detect through traditional methods, which poses great challenges for improving
the factual consistency of text summarization. In this paper, we propose an
adversarially DEcoupling method to disentangle the Comprehension and
EmbellishmeNT abilities of LLMs (DECENT). Furthermore, we adopt a probing-based
parameter-efficient technique to cover the shortage of sensitivity for true and
false in the training process of LLMs. In this way, LLMs are less confused
about embellishing and understanding, thus can execute the instructions more
accurately and have enhanced abilities to distinguish hallucinations.
Experimental results show that DECENT significantly improves the reliability of
text summarization based on LLMs.
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