Towards Human-Level Text Coding with LLMs: The Case of Fatherhood Roles in Public Policy Documents

• URL: http://arxiv.org/abs/2311.11844v3
• Date: Wed, 28 Aug 2024 16:26:16 GMT
• Title: Towards Human-Level Text Coding with LLMs: The Case of Fatherhood Roles in Public Policy Documents
• Authors: Lorenzo Lupo, Oscar Magnusson, Dirk Hovy, Elin Naurin, Lena Wängnerud,
• Abstract summary: Large language models (LLMs) promise automation with better results and less programming. In this study, we evaluate LLMs on three original coding tasks involving typical complexities encountered in political science settings. We find that the best prompting strategy consists of providing the LLMs with a detailed codebook, as the one provided to human coders.
• Score: 19.65846717628022
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in large language models (LLMs) like GPT-3.5 and GPT-4 promise automation with better results and less programming, opening up new opportunities for text analysis in political science. In this study, we evaluate LLMs on three original coding tasks involving typical complexities encountered in political science settings: a non-English language, legal and political jargon, and complex labels based on abstract constructs. Along the paper, we propose a practical workflow to optimize the choice of the model and the prompt. We find that the best prompting strategy consists of providing the LLMs with a detailed codebook, as the one provided to human coders. In this setting, an LLM can be as good as or possibly better than a human annotator while being much faster, considerably cheaper, and much easier to scale to large amounts of text. We also provide a comparison of GPT and popular open-source LLMs, discussing the trade-offs in the model's choice. Our software allows LLMs to be easily used as annotators and is publicly available: https://github.com/lorelupo/pappa.

Related papers

• PDL: A Declarative Prompt Programming Language [1.715270928578365]
  This paper introduces the Prompt Declaration Language (PDL) PDL is a simple declarative data-oriented language that puts prompts at the forefront, based on YAML. It supports writing interactive applications that call large language models (LLMs) and tools, and makes it easy to implement common use-cases such as chatbots, RAG, or agents.
  arXiv  Detail & Related papers  (2024-10-24T20:07:08Z)
• ReMoDetect: Reward Models Recognize Aligned LLM's Generations [55.06804460642062]
  Large language models (LLMs) generate human-preferable texts. In this paper, we identify the common characteristics shared by these models. We propose two training schemes to further improve the detection ability of the reward model.
  arXiv  Detail & Related papers  (2024-05-27T17:38:33Z)
• Large Language Models: A Survey [69.72787936480394]
  Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
  arXiv  Detail & Related papers  (2024-02-09T05:37:09Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• Large Language Model-Aware In-Context Learning for Code Generation [75.68709482932903]
  Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. We propose a novel learning-based selection approach named LAIL (LLM-Aware In-context Learning) for code generation.
  arXiv  Detail & Related papers  (2023-10-15T06:12:58Z)
• How to use LLMs for Text Analysis [0.0]
  This guide introduces Large Language Models (LLM) as a highly versatile text analysis method within the social sciences. As LLMs are easy-to-use, cheap, fast, and applicable on a broad range of text analysis tasks, many scholars believe that LLMs will transform how we do text analysis.
  arXiv  Detail & Related papers  (2023-07-24T19:54:15Z)
• Open-Source LLMs for Text Annotation: A Practical Guide for Model Setting and Fine-Tuning [5.822010906632045]
  This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis.
  arXiv  Detail & Related papers  (2023-07-05T10:15:07Z)
• PALR: Personalization Aware LLMs for Recommendation [7.407353565043918]
  PALR aims to combine user history behaviors (such as clicks, purchases, ratings, etc.) with large language models (LLMs) to generate user preferred items. Our solution outperforms state-of-the-art models on various sequential recommendation tasks.
  arXiv  Detail & Related papers  (2023-05-12T17:21:33Z)
• Low-code LLM: Graphical User Interface over Large Language Models [115.08718239772107]
  This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability.
  arXiv  Detail & Related papers  (2023-04-17T09:27:40Z)
• Can Large Language Models Transform Computational Social Science? [79.62471267510963]
  Large Language Models (LLMs) are capable of performing many language processing tasks zero-shot (without training data) This work provides a road map for using LLMs as Computational Social Science tools.
  arXiv  Detail & Related papers  (2023-04-12T17:33:28Z)

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.