The paper proposes ProtoMBTI to infer one of 16 MBTI types from user posts. It augments Kaggle and Pandora with GPT-4o and GPT-4o-mini, filters synthetic examples through a four-dichotomy classifier, LoRA-fine-tunes encoders of at most 2B parameters, and stores each text, embedding, and label as a prototype. At inference, GPT-4o-mini, Qwen2-72B, or Llama-3.1-70B receives a query and its three nearest prototypes for reasoning and voting. In the main results, the best single-model variant reaches 85.14% average dichotomy accuracy and 71.42% direct 16-type accuracy on Kaggle; on Pandora, GPT-4o-mini reaches 71.41% and 60.22%. Mixed-training experiments report as much as 96.41% average dichotomy accuracy and 92.13% 16-type accuracy for within-Pandora evaluation, and roughly 81% 16-type accuracy in cross-domain settings. The baseline comparison is not equivalent for the 16-type task: baseline figures are calculated by multiplying four dichotomy accuracies under an independence assumption, whereas ProtoMBTI is evaluated using a direct multiclass prediction. More importantly, Algorithm 3 compares every test prediction with the example’s ground-truth label and, only when correct, inserts that now-labeled test example into the prototype bank for subsequent predictions. This is sequential supervised test-time adaptation, not label-free inference; it makes results order-dependent and may inflate the mixed-setting results in particular. No ablation separately removes retention. Although the paper says test sets remain raw, every test input also undergoes LLM-generated explanation/augmentation before classification. The appendices do not provide a coherent reconstruction of evaluation: post-augmentation totals differ between Tables 7 and 8, Pandora test counts change across dichotomies, and several confusion matrices contain about 4,800 balanced observations rather than the stated 835 raw test users. Figure 10–12 plot titles also conflict with their captions. ProtoMBTI is therefore an interesting retrieval and augmentation architecture with useful component ablations, but the reported gain magnitude and claimed human-like cognitive alignment are not established reliably. Labels are self-reported MBTI types from online communities, class prompts encode type stereotypes, and interpretability rests on model-generated explanations without human faithfulness validation.
Research question
Can a retrieval and reasoning architecture inspired by prototype theory improve inference of the four dichotomies and the 16 MBTI types from text, explain its decisions, and transfer between the Kaggle and Pandora datasets?