What does NLI mean in LANGUAGE & LITERATURE

NLI is a subfield of natural language processing (NLP) that deals with determining the relationship between two text fragments. Specifically, NLI aims to infer whether the meaning of one text (the hypothesis) can be logically deduced from the meaning of another text (the premise).

NLI tasks can be categorized into three main types:

• Recognizing Textual Entailment (RTE): Determining if the hypothesis is necessarily true given the premise.
• Answering Factual Questions (AFQ): Answering a question using the information provided in a context passage.
• Natural Language Inference (NLI): Determining whether the hypothesis can be inferred from the premise, regardless of whether the premise is true or false.

NLI has numerous practical applications, including:

• Question Answering Systems: Providing relevant answers to user queries based on given documents.
• Machine Translation: Translating text from one language to another while preserving the meaning.
• Chatbots and Dialogue Systems: Understanding user intentions and generating appropriate responses in natural language conversations.
• Information Extraction: Automatically extracting specific facts or entities from unstructured text.

NLI research faces several challenges, such as:

• Ambiguity and Context: Natural language is often ambiguous and context-dependent, making it difficult for computers to accurately determine relationships between text fragments.
• Subtle Inferences: Some inferences require subtle reasoning and understanding of world knowledge, which can be challenging for NLI systems to capture.
• Dataset Bias: NLI datasets may exhibit biases that can limit the performance of models trained on them.

As of 2023, large language models (LLMs) such as GPT-3 and BLOOM have achieved impressive performance on NLI tasks. These models leverage massive training datasets and advanced transformer architectures to make sophisticated inferences.