Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
A compelling approach to complex question answering is to convert the question to a sequence of actions, which can then be executed on the knowledge base to yield the answer, aka the programmer-interpreter approach. Use similar training questions to the test question, meta-learning enables the programmer to adapt to unseen questions to tackle potential distributional biases quickly. However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive. In this paper, we present a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision, i.e., the system's performance with respect to the produced answers. To the best of our knowledge, this is the first attempt to train the retrieval model with the programmer jointly. Our system leads to state-of-the-art performance on a large-scale task for complex question answering over knowledge bases. We have released our code at https://github.com/DevinJake/MARL.
Code (1)
Tasks
Knowledge Base Question AnsweringMeta-LearningQuestion AnsweringRetrievalSimilar Papers 제목 키워드 기반
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Complex question-answering (CQA) involves answering complex natural-language questions on a knowledge base (KB). However, the conventional neural program induction (NPI) approach exhibits uneven performance when the ques…
Knowledge Base Question AnsweringMeta Reinforcement LearningProgram inductionQuestion Answering+2Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Existing LLM-KG integration frameworks typically rely on predefined opera…
Answer GenerationKnowledge GraphsPreAct: Computer-Using Agents that Get Faster on Repeated Tasks
Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full…
Self-Bootstrapped Visual-Language Model for Knowledge Selection and Question Answering
While large visual-language models (LVLM) have shown promising results on traditional visual question answering benchmarks, it is still challenging for them to answer complex VQA problems which requires diverse world kno…
Language ModelingLanguage ModellingLarge Language ModelPassage Retrieval+6Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction. In this paper, we present Alexandria -- a system …
Knowledge Base ConstructionVocal Bursts Intensity Prediction