Results of the summarizing process for the arXiv paper: 2311.09184v2

In their study titled "Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization," authors Yixin Liu, Alexander R. Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, and Arman Cohan explore the performance of large language models (LLMs) in instruction controllable text summarization. The researchers curated an evaluation-only dataset for this specific task setting where the model input includes both a source article and a natural language requirement for desired summary characteristics. <br> The study involved human evaluations of five LLM-based systems to assess their instruction-following capabilities in controllable summarization. Additionally, the researchers benchmarked LLM-based automatic evaluation using four different evaluation protocols and 11 LLMs, resulting in 40 evaluation methods. <br> The findings revealed that instruction controllable text summarization remains a challenging task for LLMs as all evaluated models still made factual errors and other types of mistakes in their summaries. Furthermore, none of the LLM-based evaluation methods were able to achieve strong alignment with human annotators when assessing the quality of candidate summaries. The study also highlighted significant performance gaps among different LLMs in both summary generation and evaluation capabilities.<br> To support future research in this area,. This research was presented at and additional resources including a GitHub repository (https://github.com/yale-nlp/InstruSum) and an LLM-evaluators Leaderboard (https://huggingface.co/spaces/yale-nlp/InstruSumEval) were provided for further exploration by interested parties.

• Study title: "Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization"
• Researchers: Yixin Liu, Alexander R. Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman Cohan
• Purpose: Explore performance of large language models (LLMs) in instruction controllable text summarization
• Methodology:
• Curated an evaluation-only dataset with source article and natural language requirement for desired summary characteristics
• Human evaluations of five LLM-based systems for instruction-following capabilities
• Benchmarking LLM-based automatic evaluation using four protocols and 11 LLMs (40 evaluation methods)
• Findings:
• Instruction controllable text summarization remains challenging for LLMs with factual errors and other mistakes in summaries
• None of the LLM-based evaluation methods achieved strong alignment with human annotators
• Significant performance gaps among different LLMs in both summary generation and evaluation capabilities
• Resources provided for further exploration:
• GitHub repository: https://github.com/yale-nlp/InstruSum
• LLM-evaluators Leaderboard: https://huggingface.co/spaces/yale-nlp/InstruSumEval

SummaryResearchers studied how well large language models can follow instructions to create summaries. They tested different models and found that they struggled with errors and mistakes in their summaries. None of the models performed as well as humans in following instructions. There were big differences in performance among the models. Definitions- Large Language Models (LLMs): Advanced computer programs that can understand and generate human-like text. - Summarization: The process of creating a shorter version of a piece of text while retaining its main points. - Evaluation: Assessing the quality or performance of something based on specific criteria. - Protocols: A set of rules or guidelines for conducting an experiment or evaluation. - GitHub repository: An online platform where developers share and collaborate on code projects. - Leaderboard: A list ranking participants based on their performance, often used in competitions or evaluations.

Introduction

In recent years, large language models (LLMs) have gained significant attention and popularity in the field of natural language processing (NLP). These models are trained on massive amounts of text data and can generate human-like text with impressive fluency and coherence. However, their performance in specific NLP tasks such as text summarization has been a topic of debate.

Text summarization is the process of condensing a longer piece of text into a shorter version while preserving its key information. With the rise of LLMs, there has been an increasing interest in exploring their capabilities for this task. In particular, researchers have focused on instruction controllable summarization, where the model is given specific instructions or requirements for generating a summary with desired characteristics.

In their study titled "Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization," authors Yixin Liu et al. delve into this area by evaluating various LLM-based systems' performance in instruction-following and automatic evaluation for controllable summarization.

The Dataset

To conduct their research, Liu et al. curated an evaluation-only dataset specifically designed for instruction controllable summarization. The dataset includes source articles from CNN/Daily Mail news articles paired with natural language requirements for desired summary characteristics such as length or sentiment.

The use of an evaluation-only dataset allows researchers to focus solely on evaluating LLMs' performance without being influenced by training data biases or system-specific nuances.

Methodology

The study involved two main experiments: human evaluations and benchmarking LLM-based automatic evaluation methods.

For human evaluations, five different LLM-based systems were evaluated based on how well they followed instructions provided in the input when generating summaries. Human annotators were asked to rate each summary's overall quality as well as its alignment with the given instructions on a scale from 1 to 5.

In the second experiment, the researchers benchmarked LLM-based automatic evaluation methods using four different evaluation protocols and 11 LLMs. This resulted in a total of 40 evaluation methods, which were then compared to human evaluations for alignment.

Findings

The results of the study revealed that instruction controllable text summarization remains a challenging task for LLMs. Despite being trained on large amounts of data, all evaluated models still made factual errors and other types of mistakes in their summaries.

Furthermore, none of the LLM-based evaluation methods were able to achieve strong alignment with human annotators when assessing summary quality. This highlights the need for further research and development in this area to improve automatic evaluation capabilities for instruction controllable summarization.

Additionally, significant performance gaps were observed among different LLMs in both summary generation and evaluation capabilities. This suggests that not all LLMs are equally suitable for instruction controllable summarization tasks and highlights the importance of carefully selecting an appropriate model for specific use cases.

Supporting Resources

To support future research in this area, Liu et al. have provided additional resources including a GitHub repository (https://github.com/yale-nlp/InstruSum) containing code and data used in their experiments. They have also created an LLM-evaluators Leaderboard (https://huggingface.co/spaces/yale-nlp/InstruSumEval) where interested parties can explore various LLM-based systems' performance on different datasets and tasks related to instruction controllable summarization.

Conclusion

In conclusion, Liu et al.'s study sheds light on the current state-of-the-art performance of large language models in instruction controllable text summarization. The findings highlight areas where these models excel as well as areas that require further improvement.

This research serves as a valuable resource for researchers and practitioners in the NLP community, providing insights into the capabilities and limitations of LLMs for instruction controllable summarization. The provided resources also allow for further exploration and development in this area, ultimately contributing to advancements in automatic text summarization techniques.