Language Models Benefit from Preparation with Elicited Knowledge

• URL: http://arxiv.org/abs/2409.01345v2
• Date: Fri, 6 Sep 2024 03:35:21 GMT
• Title: Language Models Benefit from Preparation with Elicited Knowledge
• Authors: Jiacan Yu, Hannah An, Lenhart K. Schubert,
• Abstract summary: The zero-shot chain of thought (CoT) approach is often used in question answering (QA) by language models (LMs) We introduce a simple general prompting technique, called PREP, that involves using two instances of LMs. PREP is designed to be general and independent of the user's domain knowledge, making it applicable across various QA tasks without the need for specialized prompt engineering.
• Score: 0.38233569758620056
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The zero-shot chain of thought (CoT) approach is often used in question answering (QA) by language models (LMs) for tasks that require multiple reasoning steps, typically enhanced by the prompt "Let's think step by step." However, some QA tasks hinge more on accessing relevant knowledge than on chaining reasoning steps. We introduce a simple general prompting technique, called PREP, that involves using two instances of LMs: the first (LM1) generates relevant information, and the second (LM2) answers the question based on this information. PREP is designed to be general and independent of the user's domain knowledge, making it applicable across various QA tasks without the need for specialized prompt engineering. To evaluate the effectiveness of our prompting method, we create a dataset of 100 binary-choice questions, derived from an extensive schematic dataset on artifact parts and material composition. These questions ask which of two artifacts is less likely to share materials with another artifact. Such questions probe the LM's knowledge of shared materials in the part structure of different artifacts. We test our method on our dataset and three published commonsense reasoning datasets. The average accuracy of our method is consistently higher than that of all the other tested methods across all the tested datasets.

Related papers

• Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent [102.31558123570437]
  Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs) We propose the first self-adaptive planning agent for multimodal retrieval, OmniSearch.
  arXiv  Detail & Related papers  (2024-11-05T09:27:21Z)
• MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering [3.1651118728570635]
  In today's fast-paced industry, professionals face the challenge of summarizing a large number of documents and extracting vital information from them on a daily basis. To address this challenge, the approach of Table Question Answering (QA) has been developed to extract the relevant information. Recent advancements in Large Language Models (LLMs) have opened up new possibilities for extracting information from tabular data using prompts.
  arXiv  Detail & Related papers  (2024-03-28T03:14:18Z)
• Automatic Question-Answer Generation for Long-Tail Knowledge [65.11554185687258]
  We propose an automatic approach to generate specialized QA datasets for tail entities. We conduct extensive experiments by employing pretrained LLMs on our newly generated long-tail QA datasets.
  arXiv  Detail & Related papers  (2024-03-03T03:06:31Z)
• SEMQA: Semi-Extractive Multi-Source Question Answering [94.04430035121136]
  We introduce a new QA task for answering multi-answer questions by summarizing multiple diverse sources in a semi-extractive fashion. We create the first dataset of this kind, QuoteSum, with human-written semi-extractive answers to natural and generated questions.
  arXiv  Detail & Related papers  (2023-11-08T18:46:32Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Self-Prompting Large Language Models for Zero-Shot Open-Domain QA [67.08732962244301]
  Open-Domain Question Answering (ODQA) aims to answer questions without explicitly providing background documents. This task becomes notably challenging in a zero-shot setting where no data is available to train tailored retrieval-reader models. We propose a Self-Prompting framework to explicitly utilize the massive knowledge encoded in the parameters of Large Language Models.
  arXiv  Detail & Related papers  (2022-12-16T18:23:43Z)
• Modern Question Answering Datasets and Benchmarks: A Survey [5.026863544662493]
  Question Answering (QA) is one of the most important natural language processing (NLP) tasks. It aims using NLP technologies to generate a corresponding answer to a given question based on the massive unstructured corpus. In this paper, we investigate influential QA datasets that have been released in the era of deep learning.
  arXiv  Detail & Related papers  (2022-06-30T05:53:56Z)
• QASem Parsing: Text-to-text Modeling of QA-based Semantics [19.42681342441062]
  We consider three QA-based semantic tasks, namely, QA-SRL, QANom and QADiscourse. We release the first unified QASem parsing tool, practical for downstream applications.
  arXiv  Detail & Related papers  (2022-05-23T15:56:07Z)
• HeteroQA: Learning towards Question-and-Answering through Multiple Information Sources via Heterogeneous Graph Modeling [50.39787601462344]
  Community Question Answering (CQA) is a well-defined task that can be used in many scenarios, such as E-Commerce and online user community for special interests. Most of the CQA methods only incorporate articles or Wikipedia to extract knowledge and answer the user's question. We propose a question-aware heterogeneous graph transformer to incorporate the multiple information sources (MIS) in the user community to automatically generate the answer.
  arXiv  Detail & Related papers  (2021-12-27T10:16:43Z)

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.