At Outread, our mission is to empower professionals by providing them with insightful summaries of academic literature. However, our journey hasn’t been without its challenges. A pivotal moment came when we received feedback from our customers about inaccuracies in our summaries. This was a wake-up call, prompting us to explore innovative solutions to enhance the accuracy and reliability of our content.
The Limitations of Reinforcement Learning from Human Feedback (RLHF)
Our initial approach involved using RLHF, a method widely believed to improve the truthfulness of summaries. However, we quickly realised that this technique had its limitations, especially given the advanced capabilities of current generation models like GPT-4. These models can summarise complex information rapidly, but non-expert users may struggle to identify subtle inaccuracies in these summaries. RLHF, in this context, risked leading to summaries that were compelling yet potentially misleading.
Pioneering Factored Verification
To address this, we turned to a novel method called factored verification. This technique involves breaking down summaries into smaller segments – claims or sentences – and verifying each segment individually. This method has two key benefits:
Simplicity in Correction: It’s easier for both the model and human reviewers to verify and correct a single claim than an entire summary.
Scalability and Safety: This approach aligns with the concept of factored cognition, enhancing safety and allowing for effective supervision of more powerful models by less aligned or weaker ones.
We implement this by constructing factored critiques that combine each erroneous claim with the model’s reasoning, asking the model to revise its summary accordingly. This process not only improves accuracy but also prevents the alteration of correct statements, a common issue with chain-of-thought-based self-correction methods.
Expanding on Enhancing User Engagement: Audio Summaries and Categorisation
Embracing Audio Summaries
At Outread, our journey towards enhancing user engagement took a significant leap with the introduction of audio summaries. Recognising the diverse preferences and lifestyles of our users, we sought to offer a more dynamic way to access information. Audio summaries emerged as a powerful solution, serving multiple needs:
Multitasking Capability: In today’s fast-paced world, the ability to multitask is invaluable. Our audio summaries enable users to absorb critical information while engaged in other activities, be it commuting, exercising, or even performing household chores.
Accessibility: Audio content is not only a preference but a necessity for some of our users, including those with visual impairments or reading difficulties. By offering audio summaries, we ensure our platform is more inclusive and accessible to all.
Engagement and Retention: Research suggests that auditory learning can enhance retention and engagement with content. Our audio summaries are designed to be engaging, leveraging tone, pace, and emphasis to make complex topics more understandable and memorable.
Revolutionising Content Discovery through Categorisation
Another significant stride in enhancing user engagement has been the implementation of categorisation. This feature revolutionises how users interact with our vast library of content:
Intuitive Navigation: With categories, users can easily navigate through our expansive collection of summaries. This organisation transforms the user experience from one of searching to discovering.
Personalised Content Discovery: Users can explore content that aligns with their specific interests or professional needs. Whether it’s technology, science, business, or arts, categorisation enables a tailored experience.
Encouraging Exploration: By categorising content, we inadvertently encourage users to explore new topics. This not only broadens their knowledge base but also sparks curiosity in areas they might not have considered before.
Community Building: Categories enable users with similar interests to connect and engage with each other. This fosters a sense of community and shared learning on our platform.
Analytics for Improvement: Categorisation also helps us in understanding user preferences better. Analysing which categories are most frequented allows us to tailor our content and improve our offerings.