Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity

• URL: http://arxiv.org/abs/2309.06364v3
• Date: Sun, 4 Feb 2024 19:46:38 GMT
• Title: Framework-Based Qualitative Analysis of Free Responses of Large Language Models: Algorithmic Fidelity
• Authors: Aliya Amirova, Theodora Fteropoulli, Nafiso Ahmed, Martin R. Cowie, Joel Z. Leibo
• Abstract summary: Large-scale generative Language Models (LLMs) can simulate free responses to interview questions like those traditionally analyzed using qualitative research methods. Here we consider whether artificial "silicon participants" generated by LLMs may be productively studied using qualitative methods.
• Score: 1.7947441434255664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today, using Large-scale generative Language Models (LLMs) it is possible to simulate free responses to interview questions like those traditionally analyzed using qualitative research methods. Qualitative methodology encompasses a broad family of techniques involving manual analysis of open-ended interviews or conversations conducted freely in natural language. Here we consider whether artificial "silicon participants" generated by LLMs may be productively studied using qualitative methods aiming to produce insights that could generalize to real human populations. The key concept in our analysis is algorithmic fidelity, a term introduced by Argyle et al. (2023) capturing the degree to which LLM-generated outputs mirror human sub-populations' beliefs and attitudes. By definition, high algorithmic fidelity suggests latent beliefs elicited from LLMs may generalize to real humans, whereas low algorithmic fidelity renders such research invalid. Here we used an LLM to generate interviews with silicon participants matching specific demographic characteristics one-for-one with a set of human participants. Using framework-based qualitative analysis, we showed the key themes obtained from both human and silicon participants were strikingly similar. However, when we analyzed the structure and tone of the interviews we found even more striking differences. We also found evidence of the hyper-accuracy distortion described by Aher et al. (2023). We conclude that the LLM we tested (GPT-3.5) does not have sufficient algorithmic fidelity to expect research on it to generalize to human populations. However, the rapid pace of LLM research makes it plausible this could change in the future. Thus we stress the need to establish epistemic norms now around how to assess validity of LLM-based qualitative research, especially concerning the need to ensure representation of heterogeneous lived experiences.

Related papers

• Large Language Model for Qualitative Research -- A Systematic Mapping Study [3.302912592091359]
  Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools. This study systematically maps the literature on the use of LLMs for qualitative research. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes.
  arXiv  Detail & Related papers  (2024-11-18T21:28:00Z)
• 'Simulacrum of Stories': Examining Large Language Models as Qualitative Research Participants [13.693069737188859]
  Recent excitement around generative models has sparked a wave of proposals suggesting the replacement of human participation and labor in research and development. We conducted interviews with 19 qualitative researchers to understand their perspectives on this paradigm shift.
  arXiv  Detail & Related papers  (2024-09-28T18:28:47Z)
• Agentic Society: Merging skeleton from real world and texture from Large Language Model [4.740886789811429]
  This paper explores a novel framework that leverages census data and large language models to generate virtual populations. We show that our method produces personas with variability essential for simulating diverse human behaviors in social science experiments. But the evaluation result shows that only weak sign of statistical truthfulness can be produced due to limited capability of current LLMs.
  arXiv  Detail & Related papers  (2024-09-02T08:28:19Z)
• Beyond Human Norms: Unveiling Unique Values of Large Language Models through Interdisciplinary Approaches [69.73783026870998]
  This work proposes a novel framework, ValueLex, to reconstruct Large Language Models' unique value system from scratch. Based on Lexical Hypothesis, ValueLex introduces a generative approach to elicit diverse values from 30+ LLMs. We identify three core value dimensions, Competence, Character, and Integrity, each with specific subdimensions, revealing that LLMs possess a structured, albeit non-human, value system.
  arXiv  Detail & Related papers  (2024-04-19T09:44:51Z)
• Explaining Large Language Models Decisions Using Shapley Values [1.223779595809275]
  Large language models (LLMs) have opened up exciting possibilities for simulating human behavior and cognitive processes. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain. This paper presents a novel approach based on Shapley values to interpret LLM behavior and quantify the relative contribution of each prompt component to the model's output.
  arXiv  Detail & Related papers  (2024-03-29T22:49:43Z)
• Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension [63.330262740414646]
  We study how to characterize and predict the truthfulness of texts generated from large language models (LLMs) We suggest investigating internal activations and quantifying LLM's truthfulness using the local intrinsic dimension (LID) of model activations.
  arXiv  Detail & Related papers  (2024-02-28T04:56:21Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model [0.0]
  The paper presents results and reflection of an experiment done to use the model GPT 3.5-Turbo to emulate some aspects of an inductive Thematic Analysis. The objective of the paper is not to replace human analysts in qualitative analysis but to learn if some elements of LLM data manipulation can to an extent be of support for qualitative research.
  arXiv  Detail & Related papers  (2023-05-22T13:16:07Z)
• Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision [84.31474052176343]
  Recent AI-assistant agents, such as ChatGPT, rely on supervised fine-tuning (SFT) with human annotations and reinforcement learning from human feedback to align the output with human intentions. This dependence can significantly constrain the true potential of AI-assistant agents due to the high cost of obtaining human supervision. We propose a novel approach called SELF-ALIGN, which combines principle-driven reasoning and the generative power of LLMs for the self-alignment of AI agents with minimal human supervision.
  arXiv  Detail & Related papers  (2023-05-04T17:59:28Z)
• Can Large Language Models Be an Alternative to Human Evaluations? [80.81532239566992]
  Large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided. We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation.
  arXiv  Detail & Related papers  (2023-05-03T07:28:50Z)

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.