Results of the summarizing process for the arXiv paper: 2206.05802v2

In this study, we focus on the task of topic-based summarization which involves generating summaries that capture non-trivial aspects of a text rather than simply summarizing the entire text. We collected a dataset of over 6,000 distinct topical queries and summaries from various sources such as short stories, Wikipedia articles and web articles. The summaries are typically up to a paragraph long and aim to capture non-trivial aspects of the text. To train our models, we collect demonstrations for different tasks including critiqueability, critique, refinement and helpfulness tasks. We use both model-generated and human-generated inputs for these tasks. After each round of data collection we train a model jointly on all the task demonstrations collected so far. We find that larger language models are more effective at writing natural language critiques and can help humans identify flaws in summaries that they might have missed. These models are able to detect flaws in both model-generated and human-written summaries including intentional flaws introduced by humans to mislead readers. Furthermore, larger models demonstrate better self-critiquing abilities despite producing outputs that are harder to critique. They can also integrate their own self-critiques as feedback to improve the quality of their own summaries. We introduce a framework for comparing critiquing ability with generation and discrimination ability. Our measurements indicate that even large models may possess relevant knowledge that they cannot or do not articulate as critiques. Overall, our findings demonstrate the potential of using AI-assisted human feedback to scale the supervision of machine learning systems in evaluating tasks that are challenging for humans to assess directly. We provide access to our training datasets and samples from our critique assistance experiments for further research purposes.

• Topic-based summarization involves capturing non-trivial aspects of a text
• Dataset of over 6,000 distinct topical queries and summaries collected from various sources
• Training models using demonstrations for critiqueability, critique, refinement, and helpfulness tasks
• Larger language models are more effective at writing natural language critiques and identifying flaws in summaries
• Models can detect intentional flaws introduced by humans to mislead readers
• Larger models demonstrate better self-critiquing abilities and can integrate their own self-critiques as feedback
• Framework for comparing critiquing ability with generation and discrimination ability introduced
• Large models may possess relevant knowledge that they cannot or do not articulate as critiques
• AI-assisted human feedback can scale the supervision of machine learning systems in evaluating challenging tasks
• Access provided to training datasets and samples from critique assistance experiments for further research purposes.

AI-Assisted Human Feedback for Topic-Based Summarization

In recent years, artificial intelligence (AI) has become increasingly important in the field of natural language processing. One task that AI can help with is topic-based summarization, which involves generating summaries that capture non-trivial aspects of a text rather than simply summarizing the entire text. In this study, we focus on this task and present a framework for using AI-assisted human feedback to scale the supervision of machine learning systems in evaluating tasks that are challenging for humans to assess directly.

Data Collection

To train our models, we collected a dataset of over 6,000 distinct topical queries and summaries from various sources such as short stories, Wikipedia articles and web articles. The summaries were typically up to a paragraph long and aimed to capture non-trivial aspects of the text. We also collected demonstrations for different tasks including critiqueability, critique, refinement and helpfulness tasks. We used both model-generated and human-generated inputs for these tasks. After each round of data collection we trained a model jointly on all the task demonstrations collected so far.

Results

We found that larger language models are more effective at writing natural language critiques and can help humans identify flaws in summaries that they might have missed. These models were able to detect flaws in both model-generated and human-written summaries including intentional flaws introduced by humans to mislead readers. Furthermore, larger models demonstrated better self-critiquing abilities despite producing outputs that were harder to critique due their complexity. They could also integrate their own self-critiques as feedback to improve the quality of their own summaries.

Evaluation Framework

We introduced a framework for comparing critiquing ability with generation and discrimination ability when it comes to summarizing topics based on texts or other sources of information. Our measurements indicated that even large models may possess relevant knowledge that they cannot or do not articulate as critiques but still use them effectively when generating summary outputs or discriminating between good vs bad ones according to certain criteria set by experts or users alike .

Conclusion

Overall, our findings demonstrate the potential of using AI assisted human feedback when it comes scaling up machine learning systems evaluation processes which are often difficult or impossible for humans alone assess accurately without assistance from intelligent machines like those powered by deep learning algorithms such as recurrent neural networks (RNNs). We provide access to our training datasets and samples from our critique assistance experiments for further research purposes so others can build upon what we have done here today..