The audience often doesn't have time to follow numerous rounds of back-and-forth—that’s precisely the problem nlite aims to solve: making the investigation of controversial topics more efficient.

To address the point raised, the platform has implemented a thoughtful mechanism. When an argument is challenged by a counterargument, the platform notifies the original submitter, who is then given the opportunity to revise their argument within certain limits (they may add new content but cannot make significant changes to existing content).

If the argument is updated, the counterargument submitter is notified and given a chance to revise their submission. If they choose to do so, the original argument submitter receives a new notification again, and the process continues.

While this system places some additional responsibility on the two parties involved in the discussion, it greatly benefits the neutral audience, who no longer need to follow a lengthy exchange history. This tradeoff is generally acceptable, as those submitting arguments and counterarguments are often motivated to inform others.

Notably, the platform marks all changes with color highlights and strikethroughs, allowing the two parties to quickly grasp what has been added or modified by the other side.

Large Language Models (LLMs) are powerful tools for summarizing data on well-studied topics. However, they are not good at reasoning about new topics that constantly emerge in our societies, which may not be well-represented 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.

At present, LLMs also suffer from the hallucination problem, meaning they occasionally produce inaccurate or misleading results.

Despite these limitations, LLMs might be able to generate a valuable set of initial arguments. Humans can use this initial output as a starting point or study the data retrieved to craft better, more nuanced questions.

Consider the following two cases:

1. Friend groups: One way nlite is commonly used is to investigate controversial topics within friend groups. While people may have differing opinions in these environments, they often do not intentionally distort data to misrepresent the other side. In such cases, nlite serves as a tool for efficient discussion.
2. Broader environments: In more public or diverse settings, where bad-faith actors may be present, nlite incorporates safeguards to minimize manipulation. A current area of focus is the development of an algorithm that helps detect the possible presence of two subgroups of users: one with good intentions that aims at ranking arguments in the proper order, and another with bad intentions that aims at ranking arguments either in reverse order or randomly. It's important to note that if manipulative behavior is detected, the platform can always publicize it, potentially damaging the perpetrators' reputation more than any early benefits they might gain.