Large Language Models (LLMs) are powerful tools for summarizing data on well-studied topics. However, they are not good at analyzing new topics that constantly emerge in our societies, which have been missing in their training data and may also be hard to collect real-time data for.

Additionally, LLMs fall short if the data available for a topic on the internet is biased, for example, when certain viewpoints are under-represented online.

LLMs also suffer from the hallucination problem, meaning they occasionally produce surprisingly incorrect results.

Despite these limitations, LLMs may generate a valuable set of initial arguments. Humans can use these as a starting point or study the data returned by LLMs to create better topics.

Fact-checking is primarily accomplished through the collective efforts of users. This crowdsourced approach is increasingly popular on social media and often viewed as less biased and more accurate than fact-checking conducted by a single organization.

The platform facilitates this process by making it mandatory for authors to select a Source Type when submitting an argument. There are two source types to choose from: Self-explanatory and Linked References. The Self-explanatory type refers to arguments that are merely based on logical principles and do not require external references. In contrast, when the Linked References option is selected, the argument submitter acknowledges that (i) certain parts of the argument require external references, and that (ii) they are linking those references to the submission. That's where the name Linked References comes from.

The requirement to provide a Source Type nudges users to check if any references are needed to support their claims and, if so, to provide them. (Learn more)

Our answer consists of two parts.

This is a valid concern, as a good argument may well be presented with different wordings by different people. If the platform’s ranking algorithm functions properly, all variants will rise to the top, leading to redundancy among the top items. To address this, the platform includes a mechanism to identify and remove duplicate arguments.

When users click the Evaluate Arguments button under a viewpoint, the platform occasionally ask the following question along with two selected arguments: Are the following arguments (essentially) making the same point? Responses to these questions are used to identify and eliminate duplicate arguments. (Learn More)

This is a great question! nlite is a valuable tool in the following two situations: (1) You are new to a topic and want to learn about it quickly and efficiently, or (2) You are already deeply knowledgeable and experienced in a particular area and wish to share your expertise with others but lack an effective platform to do so. These scenarios are discussed in more detail below.

For learners: If you are new to a topic, nlite significantly increases the efficiency with which you can learn about it. This is due to the to-the-point structure of topic pages and the rigor with which the top arguments are identified. The alternative would be to study lengthy documents or spend many hours listening to debates, and even then, you wouldn't know for sure whether you've just learned about the insights of a specific group of experts, or what you've come across are truly the best arguments that ever exist for the viewpoints.

For experts: If you are knowledgeable about a topic (we all have our own areas of expertise!) and would like to enlighten society with your knowledge, but lack a platform to do so, nlite gives you a powerful opportunity to share your insights. Notably, financial, political, and societal backgrounds do not play a significant role on nlite. What matters is the strength of the arguments you submit.