Question Answering
2861 papers with code • 143 benchmarks • 361 datasets
Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context.
Question answering can be segmented into domain-specific tasks like community question answering and knowledge-base question answering. Popular benchmark datasets for evaluation question answering systems include SQuAD, HotPotQA, bAbI, TriviaQA, WikiQA, and many others. Models for question answering are typically evaluated on metrics like EM and F1. Some recent top performing models are T5 and XLNet.
( Image credit: SQuAD )
Libraries
Use these libraries to find Question Answering models and implementationsDatasets
Subtasks
- Open-Ended Question Answering
- Open-Domain Question Answering
- Conversational Question Answering
- Answer Selection
- Answer Selection
- Knowledge Base Question Answering
- Community Question Answering
- Zero-Shot Video Question Answer
- Multiple Choice Question Answering (MCQA)
- Long Form Question Answering
- Science Question Answering
- Generative Question Answering
- Cross-Lingual Question Answering
- Mathematical Question Answering
- Temporal/Casual QA
- Logical Reasoning Question Answering
- Multilingual Machine Comprehension in English Hindi
- True or False Question Answering
- Question Quality Assessment
Latest papers with no code
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
This approach focuses the reasoning process on generating an attribution-centric output.
Reasoning on Efficient Knowledge Paths:Knowledge Graph Guides Large Language Model for Domain Question Answering
Especially for the question that require a multi-hop reasoning path, frequent calls to LLM will consume a lot of computing power.
Find The Gap: Knowledge Base Reasoning For Visual Question Answering
2) How do task-specific and LLM-based models perform in the integration of visual and external knowledge, and multi-hop reasoning over both sources of information?
How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior
However, when the reference document is perturbed with increasing levels of wrong values, the LLM is more likely to recite the incorrect, modified information when its internal prior is weaker but is more resistant when its prior is stronger.
Consistency and Uncertainty: Identifying Unreliable Responses From Black-Box Vision-Language Models for Selective Visual Question Answering
We find that neighborhood consistency can be used to identify model responses to visual questions that are likely unreliable, even in adversarial settings or settings that are out-of-distribution to the proxy model.
Is Table Retrieval a Solved Problem? Join-Aware Multi-Table Retrieval
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems.
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs
In this work, we first introduce an approach for constructing datasets based on the model knowledge for detection and intervention methods in closed-book and open-book question-answering settings.
TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding
The image overview stage provides a comprehensive understanding of the global scene information, and the coarse localization stage approximates the image area containing the answer based on the question asked.
HOI-Ref: Hand-Object Interaction Referral in Egocentric Vision
Our results demonstrate that VLMs trained for referral on third person images fail to recognise and refer hands and objects in egocentric images.
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning
The emergence of Large Language Models (LLMs) with capabilities like In-Context Learning (ICL) has ushered in new possibilities for data generation across various domains while minimizing the need for extensive data collection and modeling techniques.