NLP

Wikipedia, the world’s largest online encyclopedia, relies on a massive community of volunteers to maintain its accuracy and neutrality. But with so many editors, how do you ensure edits adhere to Wikipedia’s strict policies? I decided to explore whether Large Language Models (LLMs) could be used to automatically detect policy violations in Wikipedia edits. Here’s what I found. Wikipedia has well-defined policies to ensure content quality. These include: WP:NPOV (Neutral Point of View): Avoiding bias and presenting information objectively. WP:NOR (Original Research): Preventing the inclusion of unsourced or synthesized claims. WP:PEACOCK (Promotional Language): Discouraging exaggerated or boastful language. WP:WEASEL (Weasel Words): Eliminating vague or unattributed statements. WP:BUZZ (Marketing Buzzwords): Avoiding trendy but meaningless jargon. WP:VANDALISM: Preventing malicious or destructive edits. Manually reviewing every edit for these violations is what the reviewers do. But with the volume of edits on Wikipedia, this is a daunting task. Could LLMs help automate this process? ...

This is a blog post version a paper titled “Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering” available at https://arxiv.org/abs/2501.11301. In the world of Large Language Models (LLMs) and question answering systems, hallucination - where models generate plausible but incorrect information - remains a significant challenge. This is particularly problematic when dealing with encyclopedic knowledge sources like Wikipedia, where accuracy is paramount. Today, I’ll discuss a novel approach that addresses this challenge through question-to-question retrieval. ...

Sentence segmentation is a fundamental process in natural language processing. It involves breaking down a given text into individual sentences, a task that finds applications in various contexts. Whether you need to split a paragraph into sentences for further analysis or present sentence boundaries in a user-friendly frontend application, sentence segmentation is crucial. At first glance, identifying sentence boundaries might seem straightforward – just look for a period or full stop. However, it quickly becomes complex when you consider cases where a period is used for abbreviations such as “Dr.” or in numerical values like “3.14.” This simple punctuation mark doesn’t always signal the end of a sentence. ...