What is Question Answering?

Question Answering (QA) is a subfield of natural language processing (NLP) that focuses on developing computer systems capable of answering questions posed in natural language. This involves not only understanding the question but also retrieving relevant information from various sources and providing accurate answers.

Question Answering
Figure 1 - Question Answering

Where can you find AI text classificiation models

This is the link to use to filter Hunggingface models for Question Answering:

https://huggingface.co/models?pipeline_tag=question-answering&sort=trending

Our favourite Model Authors:

The most interesting Question Answering project

One of the most interesting Table Question Answering projects is called BERT.

Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:

  • Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
  • Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.

This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs.

https://huggingface.co/google-bert/bert-large-cased

History of Question Answering

The concept of QA has been around since the early days of artificial intelligence research. In the 1960s, the first QA system was developed at MIT, which used a combination of natural language processing and database querying to answer questions. Since then, QA has evolved significantly, with advancements in machine learning and deep learning enabling the development of more sophisticated QA systems.

How Does Question Answering Work?

  1. Natural Language Processing (NLP): NLP is responsible for understanding the meaning of the question and extracting relevant features.
  2. Information Retrieval (IR): IR is responsible for retrieving relevant information from various sources, such as databases, documents, and web pages.
  3. Ranking and Scoring: Ranking and scoring algorithms are used to rank the retrieved information and select the most relevant answer.
  4. Post-processing: Post-processing involves refining the answer to ensure it is accurate and complete.

Types of Question Answering

  • Open-Domain QA: Open-domain QA involves answering questions without any prior knowledge of the domain.
  • Closed-Domain QA: Closed-domain QA requires specific knowledge of the domain to answer the question.
  • Multi-Hop QA: Multi-hop QA involves answering questions that require multiple steps or reasoning.
  • Common Sense QA: Common sense QA involves answering questions that require common sense or real-world knowledge.

Challenges in Question Answering

  1. Ambiguity and Uncertainty: Ambiguity and uncertainty in language can make it difficult for QA systems to understand the question and provide accurate answers.
  2. Limited Domain Knowledge: Limited domain knowledge can make it challenging for QA systems to answer questions that require specialized knowledge.
  3. Lack of Contextual Understanding: Lack of contextual understanding can lead to QA systems providing answers that are not relevant to the question.
  4. Scalability: Scalability is a major challenge in QA, particularly when dealing with large volumes of data.

Applications of Question Answering

  • Customer Service: QA systems can be used to automate customer service by providing instant answers to frequently asked questions.
  • Research Assistance: QA systems can assist researchers by providing access to relevant information and helping them find relevant studies and papers.
  • Education: QA systems can be used to create personalized learning experiences by providing students with tailored answers to their questions.
  • Healthcare: QA systems can be used to provide patients with accurate information about their medical conditions and treatment options.
  • Business Intelligence: QA systems can be used to analyze large amounts of data and provide insights that can inform business decisions.

Future Directions in Question Answering

  1. Deep Learning: Deep learning techniques have shown significant promise in improving the accuracy and robustness of QA systems.
  2. Transfer Learning: Transfer learning enables QA systems to leverage pre-trained models and adapt them to new domains and tasks.
  3. Explainability: Explainability is becoming increasingly important in QA, particularly in high-stakes applications where transparency and accountability are critical.
  4. Multimodal Interaction: Multimodal interaction enables QA systems to interact with users through multiple modalities, such as text, speech, and vision.

Conclusion

QA has come a long way since its inception, with significant advancements in recent years. Despite the challenges that remain, QA holds great promise for transforming various industries and revolutionizing the way we interact with technology. As research continues to advance, we can expect to see even more sophisticated QA systems that can accurately and efficiently answer complex questions.

How to setup a Question Answering LLM on Ubuntu Linux

If you are ready to setup your first Question Answering system follow the instructions in our next page:

How to setup a Question Answering system

Image sources

Figure 1: https://www.nlplanet.org/course-practical-nlp/_images/question_answering_21.png

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