Search

Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre‐trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common‐sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research. This article is categorized under:Fundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

With the launch of Chat GPT by open AI, a whole new world has opened up (and potentially a can of worms) in the scientific and publishing world. The potential role of Chat GPT and artificial intelligence (AI) in oral surgery and scientific writing can be significant, offering many potential benefits, but also raising important ethical considerations. Potential drawbacks and ethical considerations: Lack of human oversight: While AI can automate certain tasks, it cannot replace human expertise in scientific writing. [Extracted from the article]

Computational linguistics encompasses a broad range of ideas and research areas, and only a brief introduction is possible here. We chose to include areas in computational linguistics where statisticians can contribute, hoping to provide inspiration to the reader. We describe three main aspects of this discipline-formal languages, information retrieval, and machine learning. These support the overarching goal, which is the representation and analysis of meaning from unstructured text. We then provide an example where text analysis has been applied to unstructured text fields in survey records and conclude with some applications and computational resources. WIREs Comput Stat 2015, 7:258-274. doi: 10.1002/wics.1353 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]