Question Answering
Question answering is the NLP task of automatically generating answers to questions posed in natural language. Modern QA systems range from extractive (finding answers in text) to generative (producing new answer text).
Understanding Question Answering
Question answering (QA) is a natural language processing task where a system generates or extracts accurate answers to questions posed in natural language. QA systems fall into several categories: extractive QA, which identifies answer spans within a given context document; generative QA, where large language models compose answers from their parametric knowledge; and retrieval-augmented QA, which first retrieves relevant documents and then generates answers grounded in that evidence. Applications include customer support automation, medical information systems, educational tutoring platforms, and enterprise knowledge bases. Benchmark datasets like SQuAD and Natural Questions have driven advances in the field, while modern systems built on transformers like BERT and GPT-4 have achieved near-human performance on many QA tasks. Retrieval-augmented generation has become the preferred approach for factual question answering, as it reduces hallucination by grounding responses in verified source material.
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.