Successive Prompting for Decomposing Complex Questions

• URL: http://arxiv.org/abs/2212.04092v1
• Date: Thu, 8 Dec 2022 06:03:38 GMT
• Title: Successive Prompting for Decomposing Complex Questions
• Authors: Dheeru Dua, Shivanshu Gupta, Sameer Singh, Matt Gardner
• Abstract summary: Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting. We introduce Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Our best model (with successive prompting) achieves an improvement of 5% absolute F1 on a few-shot version of the DROP dataset.
• Score: 50.00659445976735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Answering complex questions that require making latent decisions is a challenging task, especially when limited supervision is available. Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting by demonstrating how to output intermediate rationalizations while solving the complex question in a single pass. We introduce ``Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Successive prompting decouples the supervision for decomposing complex questions from the supervision for answering simple questions, allowing us to (1) have multiple opportunities to query in-context examples at each reasoning step (2) learn question decomposition separately from question answering, including using synthetic data, and (3) use bespoke (fine-tuned) components for reasoning steps where a large LM does not perform well. The intermediate supervision is typically manually written, which can be expensive to collect. We introduce a way to generate a synthetic dataset which can be used to bootstrap a model's ability to decompose and answer intermediate questions. Our best model (with successive prompting) achieves an improvement of ~5% absolute F1 on a few-shot version of the DROP dataset when compared with a state-of-the-art model with the same supervision.

Related papers

• LaSagnA: Language-based Segmentation Assistant for Complex Queries [39.620806493454616]
  Large Language Models for Vision (vLLMs) generate detailed perceptual outcomes, including bounding boxes and masks. In this study, we acknowledge that the main cause of these problems is the insufficient complexity of training queries. We present three novel strategies to effectively handle the challenges arising from the direct integration of the proposed format.
  arXiv  Detail & Related papers  (2024-04-12T14:40:45Z)
• Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling [6.635572580071933]
  Multi-hop question generation aims to generate complex questions that requires multi-step reasoning over several documents. Previous studies have predominantly utilized end-to-end models, wherein questions are decoded based on the representation of context documents. This paper introduces an end-to-end question rewriting model that increases question complexity through sequential rewriting.
  arXiv  Detail & Related papers  (2024-03-31T06:03:54Z)
• Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity [59.57065228857247]
  Retrieval-augmented Large Language Models (LLMs) have emerged as a promising approach to enhancing response accuracy in several tasks, such as Question-Answering (QA) We propose a novel adaptive QA framework, that can dynamically select the most suitable strategy for (retrieval-augmented) LLMs based on the query complexity. We validate our model on a set of open-domain QA datasets, covering multiple query complexities, and show that ours enhances the overall efficiency and accuracy of QA systems.
  arXiv  Detail & Related papers  (2024-03-21T13:52:30Z)
• In-Context Ability Transfer for Question Decomposition in Complex QA [6.745884231594893]
  We propose icat (In-Context Ability Transfer) to solve complex question-answering tasks. We transfer the ability to decompose complex questions to simpler questions or generate step-by-step rationales to LLMs. We conduct large-scale experiments on a variety of complex QA tasks involving numerical reasoning, compositional complex QA, and heterogeneous complex QA.
  arXiv  Detail & Related papers  (2023-10-26T11:11:07Z)
• Can Large Language Models Understand Real-World Complex Instructions? [54.86632921036983]
  Large language models (LLMs) can understand human instructions, but struggle with complex instructions. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions. We propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
  arXiv  Detail & Related papers  (2023-09-17T04:18:39Z)
• Answering Ambiguous Questions via Iterative Prompting [84.3426020642704]
  In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. One approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. We present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions.
  arXiv  Detail & Related papers  (2023-07-08T04:32:17Z)
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks [55.42850359286304]
  We propose Decomposed Prompting to solve complex tasks by decomposing them (via prompting) into simpler sub-tasks. This modular structure allows each prompt to be optimized for its specific sub-task. We show that the flexibility and modularity of Decomposed Prompting allows it to outperform prior work on few-shot prompting.
  arXiv  Detail & Related papers  (2022-10-05T17:28:20Z)
• Coarse-grained decomposition and fine-grained interaction for multi-hop question answering [5.88731657602706]
  Lots of complex queries require multi-hop reasoning. Bi-DAF generally captures only the surface semantics of words in complex questions. We propose a new model architecture for multi-hop question answering.
  arXiv  Detail & Related papers  (2021-01-15T06:56:34Z)
• Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models [61.480085460269514]
  We propose a framework for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator.
  arXiv  Detail & Related papers  (2020-09-01T23:45:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.