arXiv AI
Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis
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arXiv AI
Aug 3
Human-LLM Collaborative Inductive Coding for Conceptualizing K-12 Educator AI Use
arXiv:2607. 28889v1 Announce Type: cross Abstract: Qualitative researchers increasingly encounter interaction corpora whose scale exceeds what manual coding alone can address, and large language models (LLMs) are frequently proposed as analytic assistants.
arXiv AI
3d ago
Agent-as-Peer-Debriefer: A Multi-Agent Framework with Perspective-Based Refinement for Qualitative Analysis
The paper introduces Agent-as-Peer-Debriefing, a multi‑agent framework that incorporates peer debriefing into qualitative data analysis with large language models. A Hierarchical Coding Agent generates codes and reflections, which are then refined by three Peer‑Debriefing Agents applying Theory‑Driven, Data‑Driven, or Applied perspectives. Experiments on three datasets show that perspective‑based refinement aligns more closely with human codes than a single‑LLM baseline, and that the choice of perspective offers meaningful trade‑offs.
arXiv AI
Jun 11
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
arXiv:2601. 17717v3 Announce Type: replace Abstract: Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities.
arXiv AI
Aug 25
LLMs for Survey Text Analysis - A Performance Comparison Between Humans and GPT-5 on Inductive Content Analysis
arXiv:2608.22417v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support text analysis in qualitative research, yet evidence on their performance in inductive con...
arXiv AI
Jul 15
So Many Opinions, So Many LLMs: Comparing Large Language Models to Traditional Machine Learning for Open- Ended Survey Analysis
arXiv:2607. 11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale.
arXiv AI
Jun 8
Auditing Training Data in Domain-adapted LLMs: LoRA-MINT
arXiv:2606. 06946v1 Announce Type: cross Abstract: We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP) tasks through Low-Rank Adaptation (LoRA).