Question Answering (QA) is the NLP task of automatically generating accurate answers to natural language questions posed by users, either by extracting answers from text passages (extractive QA) or generating free-form responses (generative QA). QA systems employ reading comprehension models, retrieval-augmented generation, and knowledge reasoning to power applications including search engines, virtual assistants, and customer support.
Semantic Classification
Content
- Question Answering (QA) is the NLP task of automatically generating accurate answers to natural language questions posed by users, either by extracting answers from text passages (extractive QA) or generating free-form responses (generative QA). QA systems employ reading comprehension models, retrieval-augmented generation, and knowledge reasoning to power applications including search engines, virtual assistants, and customer support.
VQ-AD Method by NVIDIA and University of Toronto
- VQ-AD Research Page - VQAD (Visually-grounded Question Answering for Documents) is a dataset and benchmark for question answering tasks requiring reasoning about both visual and textual information within document images.
- The dataset is automatically generated, mitigating annotation costs and enabling scalability.
- The VQAD dataset features questions that demand understanding of layout, spatial relationships, and textual content within documents.
- It offers different question types to assess various aspects of document understanding, including finding information, reasoning, and comparing data.
- Researchers can utilise VQAD to train and evaluate machine learning models designed to process and extract information from visually complex documents.
- The resource provides tools and evaluation metrics to aid researchers in assessing the performance of their VQAD models.
- VQAD aims to promote advancements in document AI, enabling more effective information retrieval and analysis from document images.
- The data helps develop models which are colour blind, in the sense that colour isn’t a cue to help answer the question.
Basic Inquiries:
- Both models perform similarly in explaining basic concepts and answering various questions.
Key Features
- Enter a research question and get back relevant papers along with AI-generated summaries of each paper with respect to the query
- Extract structured data from papers into tabular format (e.g. population studied, methods used, interventions, outcomes)
- Provide a description or instructions for data to extract, allowing custom extraction
- Group or pivot extracted data by arguments, effects, interventions, techniques rather than just by paper
- Notebook interface allows users to interactively search for papers, extract data, analyze results, and share templates of analysis with others
VQ-AD Method by NVIDIA and University of Toronto
- VQ-AD Research Page - VQAD (Visually-grounded Question Answering for Documents) is a dataset and benchmark for question answering tasks requiring reasoning about both visual and textual information within document images.
- The dataset is automatically generated, mitigating annotation costs and enabling scalability.
- The VQAD dataset features questions that demand understanding of layout, spatial relationships, and textual content within documents.
- It offers different question types to assess various aspects of document understanding, including finding information, reasoning, and comparing data.
- Researchers can utilise VQAD to train and evaluate machine learning models designed to process and extract information from visually complex documents.
- The resource provides tools and evaluation metrics to aid researchers in assessing the performance of their VQAD models.
- VQAD aims to promote advancements in document AI, enabling more effective information retrieval and analysis from document images.
- The data helps develop models which are colour blind, in the sense that colour isn’t a cue to help answer the question.
Basic Inquiries:
- Both models perform similarly in explaining basic concepts and answering various questions.
Key Features
- Enter a research question and get back relevant papers along with AI-generated summaries of each paper with respect to the query
- Extract structured data from papers into tabular format (e.g. population studied, methods used, interventions, outcomes)
- Provide a description or instructions for data to extract, allowing custom extraction
- Group or pivot extracted data by arguments, effects, interventions, techniques rather than just by paper
- Notebook interface allows users to interactively search for papers, extract data, analyze results, and share templates of analysis with others
Systematic Review Automation
- Elicit’s workflow is inspired by the systematic review or meta-analysis process used to summarize scientific literature
- Makes this capability more accessible for any research question
- Group or pivot extracted data by arguments, effects, interventions, techniques rather than just by paper
- Notebook interface allows users to interactively search for papers, extract data, analyze results, and share templates of analysis with others
AI Research Assistant
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Elicit’s workflow is inspired by the systematic review or meta-analysis process used to summarize scientific literature
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Makes this capability more accessible for any research question
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Group or pivot extracted data by arguments, effects, interventions, techniques rather than just by paper
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Notebook interface allows users to interactively search for papers, extract data, analyze results, and share templates of analysis with others
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Elicit Basic is still completely free to use. With these changes, you can now use Elicit Basic indefinitely, until you’re ready to upgrade to Elicit Plus or Elicit Pro.
Key principles for writing a good plain language summary include:
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Accuracy, clarity, and conciseness
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Avoiding prior knowledge assumptions
Google Assistant can summarize web pages using Generative AI. However, this service is currently only available on Pixel 8 and Pixel 8 Pro devices in English, and it cannot summarize paywalled articles or content less than 200 words. Users can provide feedback on summaries, which helps improve the service. The Assistant Summarize feature filters out sensitive information like pornography, violence, and hate speech. 🤖
Large Language Models (LLMs)
- Introduction to LLMs
- Large language models are advanced computer programs capable of generating text, answering questions, and more, trained on vast internet text. Examples include OpenAI’s GPT-3.
- Projects and Implementations
- Browser-based models, such as the Web LLM project, which discusses a browser-based version of the vicuna-7b Large Language Model.
Semantic Grounding, Reasoning, and Explainability**
- Ontology-Guided Question Answering and Reasoning
- Formalizing knowledge representations [20]
- Techniques for inference and reasoning over LLMs and KGs [21]
- Logic and Constraints for LLMs
- Integrating rule-based systems and ontologies [22]
- Challenges and potential of hybrid approaches [23]
- Explainable AI and the Role of Knowledge Graphs
- Tracing LLM decisions through ontologies [24]
- Case studies in explainability [25]
Large Language Models (LLMs)
- Introduction to LLMs
- Large language models are advanced computer programs capable of generating text, answering questions, and more, trained on vast internet text. Examples include OpenAI’s GPT-3.
- Projects and Implementations
- Browser-based models, such as the Web LLM project, which discusses a browser-based version of the vicuna-7b Large Language Model.
Semantic Grounding, Reasoning, and Explainability**
- Ontology-Guided Question Answering and Reasoning
- Formalizing knowledge representations [20]
- Techniques for inference and reasoning over LLMs and KGs [21]
- Logic and Constraints for LLMs
- Integrating rule-based systems and ontologies [22]
- Challenges and potential of hybrid approaches [23]
- Explainable AI and the Role of Knowledge Graphs
- Tracing LLM decisions through ontologies [24]
- Case studies in explainability [25]
Large Language Models (LLMs)
- Introduction to LLMs
- Large language models are advanced computer programs capable of generating text, answering questions, and more, trained on vast internet text. Examples include OpenAI’s GPT-3.
- Projects and Implementations
- Browser-based models, such as the Web LLM project, which discusses a browser-based version of the vicuna-7b Large Language Model.
Large Language Models (LLMs)
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Introduction to LLMs
- Large language models are advanced computer programs capable of generating text, answering questions, and more, trained on vast internet text. Examples include OpenAI’s GPT-3.
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Projects and Implementations
-
Browser-based models, such as the Web LLM project, which discusses a browser-based version of the vicuna-7b Large Language Model.
Core Characteristics
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Extractive or Generative: Span extraction vs. answer generation
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Open-Domain or Closed-Domain: Unrestricted vs. domain-specific QA
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Retrieval-Augmented: Integration of information retrieval with generation
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Multi-Hop Reasoning: Answering questions requiring multiple inference steps
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Conversational QA: Multi-turn question answering dialogues
Relationships
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Subclass: Natural Language Processing
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Related: Information Retrieval, Reading Comprehension, Dialogue System
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Models: BERT-QA, RoBERTa-QA, T5, GPT with RAG
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Datasets: SQuAD, Natural Questions, HotpotQA, TriviaQA
Key Literature
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Rajpurkar, P., et al. (2016). “SQuAD: 100,000+ questions for machine comprehension of text.” EMNLP, 2383-2392.
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Kwiatkowski, T., et al. (2019). “Natural questions: A benchmark for question answering research.” TACL, 7, 452-466.
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Lewis, P., et al. (2020). “Retrieval-augmented generation for knowledge-intensive NLP tasks.” NeurIPS, 9459-9474.
See Also
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Core Characteristics
-
Extractive or Generative: Span extraction vs. answer generation
-
Open-Domain or Closed-Domain: Unrestricted vs. domain-specific QA
-
Retrieval-Augmented: Integration of information retrieval with generation
-
Multi-Hop Reasoning: Answering questions requiring multiple inference steps
-
Conversational QA: Multi-turn question answering dialogues
Relationships
-
Subclass: Natural Language Processing
-
Related: Information Retrieval, Reading Comprehension, Dialogue System
-
Models: BERT-QA, RoBERTa-QA, T5, GPT with RAG
-
Datasets: SQuAD, Natural Questions, HotpotQA, TriviaQA
Key Literature
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Rajpurkar, P., et al. (2016). “SQuAD: 100,000+ questions for machine comprehension of text.” EMNLP, 2383-2392.
-
Kwiatkowski, T., et al. (2019). “Natural questions: A benchmark for question answering research.” TACL, 7, 452-466.
-
Lewis, P., et al. (2020). “Retrieval-augmented generation for knowledge-intensive NLP tasks.” NeurIPS, 9459-9474.
See Also
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