Natural Language Inference
Natural Language Inference (NLI) is the task of determining whether a hypothesis is entailed by, contradicts, or is neutral to a given premise. NLI benchmarks test a model's understanding of logical relationships in text.
Understanding Natural Language Inference
Natural language inference is a fundamental natural language processing task that involves determining the logical relationship between two text segments: a premise and a hypothesis. The model must classify the relationship as entailment (the hypothesis follows from the premise), contradiction (the hypothesis conflicts with the premise), or neutral (insufficient information to determine the relationship). NLI benchmarks like SNLI and MultiNLI have been instrumental in advancing language understanding, and strong NLI performance is often indicative of a model's general reasoning capabilities. Real-world applications include fact-checking systems, question answering verification, and hate speech detection where understanding logical relationships between claims is essential. NLI models typically leverage transformer architectures with unsupervised pre-training and are evaluated as part of comprehensive benchmark suites like GLUE and SuperGLUE for measuring progress in artificial intelligence.
Category
Natural Language Processing
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Abstractive Summarization
Abstractive summarization generates new text that captures the key points of a longer document, rather than simply extracting existing sentences. It requires deep language understanding and generation capabilities.
Beam Search
Beam search is a decoding algorithm that explores multiple candidate sequences simultaneously, keeping only the top-k most promising at each step. It balances between greedy decoding and exhaustive search in text generation.
BERT
BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that reads text in both directions simultaneously. BERT revolutionized NLP by enabling deep bidirectional pre-training for language understanding tasks.
Bigram
A bigram is a contiguous sequence of two items (typically words or characters) from a given text. Bigram models estimate the probability of a word based on the immediately preceding word.
Byte Pair Encoding
Byte Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of characters or character sequences. BPE is widely used in modern language models to handle rare words and multilingual text.
Corpus
A corpus is a large, structured collection of text documents used for training and evaluating natural language processing models. The quality and diversity of a training corpus significantly impacts model performance.
Extractive Summarization
Extractive summarization selects and combines the most important sentences directly from a source document to create a summary. It preserves the original wording but may lack the coherence of abstractive approaches.
Grounding
Grounding in AI refers to connecting a model's language understanding to real-world knowledge, data, or sensory experience. Grounded AI systems produce more factual and contextually relevant outputs.