In recent times, large language models have attracted a lot of attention due to their superior capabilities. LLMs are capable of everything from question answering and content creation to language translation and text summarization. Recent developments in automated summarization have largely been attributed to a shift in strategy from supervised fine-tuning on labeled datasets to the use of large language models such as GPT-4 developed with zero-shot prompting. This change enables careful prompt customization of various summary properties, including length, theme, and style, without additional training.
In automated summarizing, deciding how much information to include in the summary is a difficult task. A great summary should carefully balance being comprehensive and entity-focused while avoiding overly dense language that confuses the reader. In recent research, a team of researchers have studied using the well-known GPT-4 to generate summaries with Chain of Density (CoD) prompts to better understand the trade-offs.
The main objective of this study was to find a limit by combining human preferences for the collection of summaries produced by GPT-4 that are progressively denser. The CoD prompt consisted of several steps, and GPT-4 initially produced a summary with a limited number of listed elements. The summary was then expanded to include missing salient items. Compared to summaries generated by conventional GPT-4 prompts, these CoD-generated summaries were distinguished by enhanced abstraction, higher levels of fusion, i.e. information integration, and less bias towards the beginning of the source text.
One hundred items from the CNN DailyMail were used to assess the effectiveness of COD-derived summaries in human preference research. The results of the study showed that GPT-4 summaries generated with COD prompts, which were denser than those generated with vanilla prompts but approached the density of human-written summaries, were preferred by human raters. This suggests that it is important to strike an ideal balance between informativeness and readability in summaries. In addition to the human preference study, the researchers also released 5,000 annotated COD summaries, all of which are available to the public on the HuggingFace website.
The team summarized their major contributions as follows –
- The Chain of Density (CoD) method is presented, which is an iterative prompt-based strategy that gradually improves the component density of summaries produced by GPT-4.
- Comprehensive Evaluation: Research thoroughly evaluates COD summaries of ever density with manual and automated evaluations. This assessment seeks to understand the delicate balance between the two, favoring clarity and informativeness, with fewer elements in summaries.
- Open Source Resources: This study provides open-source access to 5,000 unannotated CoD summaries, annotations, and summaries produced by GPT-4. These tools are made available for analysis, evaluation, or instruction, promoting continuous development in the field of automated summarization.
Finally, this research highlights the ideal balance between brevity and informativeness in automated summaries as determined by human preferences, and argues that it is desirable for automated summarization processes to achieve density levels close to human-generated summaries.
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Tanya Malhotra is an undergrad from University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with specialization in Artificial Intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills, along with a keen interest in learning new skills, leading teams and managing work in an organized manner.