Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition

Core Concepts

The author proposes SlotSum, a framework for entity abstract summarization that reduces hallucinations by disentangling facts and introducing external knowledge sources.

Abstract

Entity abstract summarization aims to generate concise descriptions of entities from internet documents. Pre-trained language models often suffer from hallucinations, generating non-factual information. SlotSum disentangles facts and templates to reduce hallucinations and improve factual correctness.
Key points:

Entity abstract summarization generates concise descriptions of entities.
Pre-trained language models may produce hallucinations.
SlotSum disentangles facts and templates to reduce hallucinations.
External knowledge is introduced to improve factual correctness.
SlotSum outperforms baseline models in reducing hallucinations and improving factual correctness in entity abstract summarization.

Stats

Slot Frequency: name - 21743 (79.44%)
Slot Frequency: birth_date - 21595 (78.89%)
Slot Frequency: position - 5041 (18.42%)
Slot Frequency: currentclub - 4817 (17.6%)
Slot Frequency: occupation - 4595 (16.79%)
Slot Frequency: fullname - 3712 (13.56%)
Slot Frequency: death_date - 3286 (12.0%)
Slot Frequency: birth_place - 3089 (11.29%)
Slot Frequency: nationality - 2696 (9.85%)
Slot Frequency: genre - 2338 (8.54%)

Quotes

"SlotSum views the summary of an entity as a combination of facts and fact-agnostic template."
"Introducing external knowledge drastically improves the quality of generated summaries."

Key Insights Distilled From

Reducing Hallucinations in Entity Abstract Summarization with Facts-Template Decomposition

by Fangwei Zhu,... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18873.pdf

Deeper Inquiries

How can the concept of disentangling facts be applied to other text generation tasks

The concept of disentangling facts can be applied to other text generation tasks by incorporating external knowledge sources to improve the factual correctness of generated content. In tasks like data-to-text generation or dialogue systems, where accuracy and reliability are crucial, disentangling facts can help reduce hallucinations and ensure that the generated text aligns with the provided information. By separating factual details from generic content in a structured manner, models can focus on generating accurate information while maintaining coherence and fluency in the output.

What are the potential ethical considerations when using external knowledge sources in text generation

When using external knowledge sources in text generation, several ethical considerations need to be taken into account. Firstly, there is a risk of propagating misinformation if the external knowledge is not verified or comes from unreliable sources. Models may inadvertently generate false or biased information based on incorrect external data. Secondly, privacy concerns arise when utilizing external databases or human experts for fact-checking purposes. Ensuring that sensitive information is handled securely and anonymized appropriately is essential to protect individuals' privacy rights. Additionally, transparency about the use of external knowledge sources should be maintained to uphold ethical standards in text generation practices.

How can the findings of this study be extended to improve the performance of large language models in entity abstract summarization

The findings of this study can be extended to improve the performance of large language models (LLMs) in entity abstract summarization by integrating a similar framework that disentangles facts from templates and incorporates external knowledge for fact correction. By training LLMs on datasets like WikiFactSum with trustworthy knowledge alongside input documents, these models can learn to prioritize factual correctness while generating summaries without relying solely on memorized data points. Implementing mechanisms for slot-filling strategies guided by reliable external sources could enhance LLMs' ability to produce accurate and informative entity abstract summaries while minimizing hallucinations commonly observed due to pretraining biases.