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Found 6 papers, 0 papers with code
Dynamic Strategy Planning for Efficient Question Answering with Large Language Models
Research has shown the effectiveness of reasoning (e. g., Chain-of-Thought), planning (e. g., SelfAsk), and retrieval augmented generation strategies to improve the performance of Large Language Models (LLMs) on various tasks, such as question answering.
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering.
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering
A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate.
