FAQ

Large Language Models (LLMs) provide strong summarization tools and can be used to summarize 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 over-represented online.

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

Despite these limitations, LLMs can sometimes generate a valuable set of initial arguments. Humans can then refine these arguments or study the retrieved data to create more focused topics.

Fact-checking is primarily achieved through the collective effort of argument evaluators. This approach, which leverages the collective knowledge of users, is gaining popularity on social media and is seen as less biased and more accurate than fact-checking by a single organization.

The platform facilitates this process by making it mandatory for authors to select a Source Type when submitting their arguments. There are two source types to choose from: Self-explanatory and Linked References. The Self-explanatory type refers to cases where the argument is based merely on logical principles and does 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 aims to nudge users to check if any references are needed to support their claims. (Learn more)

Our answer consists of two parts.

  1. Firstly, it has been frequently observed in practical settings that the impact of misbehaving users on social platforms is often less significant than initially expected. As an example, consider the online encyclopedia Wikipedia. When this platform was initially launched, many doubted that it could ever succeed given the risk of manipulation by ill-intentioned users. However, the platform is now one of the most visited, and used, websites on the internet. As another example, consider the transportation company Uber. Again, many individuals originally doubted that such a decentralized system could ever work due to the possibility of harassment by misbehaving drivers and passengers. However, the company has ended up becoming very popular.
  2. In the second part of our response, we emphasize our efforts at nlite to mitigate the impact of misbehaving users. One 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 publicize it, potentially damaging the perpetrators' reputation more than any early benefits they might gain.

This is a valid concern, as a good argument may 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 repeated arguments 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 the 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)

nlite can be 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 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 explored in more detail below.

First, if you are new to a topic, nlite significantly increases the efficiency with which you can educate yourself about it. This is due to the to-the-point structure of topic pages and the rigor with which the top arguments are identified. Note that the alternative would be to study lengthy documents or spend many hours listening to debates. Even then, you wouldn't know for sure whether you've just learned about the insights of a limited number of experts, or what you've come across are truly the best arguments that ever exist for the pertaining viewpoints.

Second, 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 the platform to do so, nlite gives you a powerful opportunity to achieve your goal. Note that once the top arguments for all sides are identified, it would be hard for the audience to look only at the arguments submitted for one side and ignore the others. Therefore, the strong arguments you submit for the viewpoints you endorse will be seen by many people and help promote the viewpoint.

Overview