Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT

• URL: http://arxiv.org/abs/2210.04186v2
• Date: Tue, 11 Oct 2022 01:31:37 GMT
• Title: Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
• Authors: Bhavya Bhavya, Jinjun Xiong, Chengxiang Zhai
• Abstract summary: We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies. We found that it is feasible to prompt InstructGPT to generate meaningful analogies and the best prompts tend to be precise imperative statements. We also conducted human evaluation on 1.4k of the generated analogies and found that the quality of generations varies substantially by model size.
• Score: 47.81902362511172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG). We found that it is feasible to prompt InstructGPT to generate meaningful analogies and the best prompts tend to be precise imperative statements especially with a low temperature setting. We also systematically analyzed the sensitivity of the InstructGPT model to prompt design, temperature, and injected spelling errors, and found that the model is particularly sensitive to certain variations (e.g., questions vs. imperative statements). Further, we conducted human evaluation on 1.4k of the generated analogies and found that the quality of generations varies substantially by model size. The largest InstructGPT model can achieve human-level performance at generating meaningful analogies for a given target while there is still room for improvement on the AEG task.

Related papers

• Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models [0.0]
  This article investigates a zero-shot approach to hypernymy prediction using large language models (LLMs) Experiments demonstrate a strong correlation between the effectiveness of language model prompts and classic patterns. We also explore prompts for predicting co-hyponyms and improving hypernymy predictions by augmenting prompts with additional information.
  arXiv  Detail & Related papers  (2024-01-09T12:13:55Z)
• StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding [72.38872974837462]
  We evaluate the ability to identify and generate analogies by constructing a first-of-its-kind large-scale story-level analogy corpus. textscStory Analogy contains 24K story pairs from diverse domains with human annotations on two similarities from the extended Structure-Mapping Theory. We observe that the data in textscStory Analogy can improve the quality of analogy generation in large language models.
  arXiv  Detail & Related papers  (2023-10-19T16:29:23Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)
• Large Language Models as Analogical Reasoners [155.9617224350088]
  Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks. We introduce a new prompting approach, analogical prompting, designed to automatically guide the reasoning process of large language models.
  arXiv  Detail & Related papers  (2023-10-03T00:57:26Z)
• ANALOGYKB: Unlocking Analogical Reasoning of Language Models with A Million-scale Knowledge Base [51.777618249271725]
  ANALOGYKB is a million-scale analogy knowledge base derived from existing knowledge graphs (KGs) It identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large language models (LLMs)
  arXiv  Detail & Related papers  (2023-05-10T09:03:01Z)
• A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis [90.24921443175514]
  We focus on aspect-based sentiment analysis, which involves extracting aspect term, category, and predicting their corresponding polarities. We propose to reformulate the extraction and prediction tasks into the sequence generation task, using a generative language model with unidirectional attention. Our approach outperforms the previous state-of-the-art (based on BERT) on average performance by a large margins in few-shot and full-shot settings.
  arXiv  Detail & Related papers  (2022-04-11T18:31:53Z)
• Hybrid Autoregressive Transducer (hat) [11.70833387055716]
  This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model. It is a time-synchronous encoderdecoder model that preserves the modularity of conventional automatic speech recognition systems. We evaluate our proposed model on a large-scale voice search task.
  arXiv  Detail & Related papers  (2020-03-12T20:47:06Z)

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.