The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results. However, these rankers are hindered by two major drawbacks: (1) they fail to follow a standardized comparison guidance during the ranking process, and (2) they struggle with comprehensive considerations when dealing with complicated passages. To address these shortcomings, we propose to build a ranker that generates ranking scores based on a set of criteria from various perspectives. These criteria are intended to direct each perspective in providing a distinct yet synergistic evaluation. Our research, which examines eight datasets from the BEIR benchmark demonstrates that incorporating this multi-perspective criteria ensemble approach markedly enhanced the performance of pointwise LLM rankers.

In the digital world, natural language processing (NLP) is becoming as important as air. With or without knowing, we are using the subset of artificial intelligence at every moment of our life. Starting from AI assistants like Alexa to search engines, everything is powered by NLP technology. Not just simple works, natural language processing can even do big jobs extracting data from documents and enhancing the capabilities of machine learning algorithms. NLP stands as an umbrella term that covers other processes like sentiment analysis, text extraction, machine translation, conversational AI, text summarization, document AI, etc. Owing to the increasing usage of technology, NLP jobs are also becoming popular.