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REVIEW 5 major objections 3 minor 43 references

From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media

T0 review · 5 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims a two-LLM retrieval pipeline can predict MBTI personality types from social media posts, beating classical and BERT classifiers by wide margins.

desk verdict The empirical claims are invalidated by test-set contamination: the forum posts contain explicit MBTI self-reports, so the LLM is largely reading the answer rather than inferring it. read the letter →

arxiv 2509.04461 v1 pith:IUZLNWLC submitted 2025-08-28 cs.CL cs.SI

Tian Ma , Kaiyu Feng , Yu Rong , Kangfei Zhao This is my paper
classification cs.CLcs.SI
keywords MBTIpredictionpersonalitylargelanguagemodelsretrieval-augmentedgenerationin-contextlearningSMOTEoversamplingsocialmediatextclassimbalance
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a two-stage large-language-model pipeline can predict a social media user's Myers-Briggs type from their posts more accurately than classical machine learning and fine-tuned BERT baselines. The proposed system, PostToPersonality (P2P), first has a locally fine-tuned LLM turn a user's posts into a psychological assessment, then retrieves similar post-label examples from a vector database, and finally asks an online LLM to make the four binary MBTI judgments with those examples as in-context demonstrations. To handle the population's imbalanced MBTI distribution, the local model is fine-tuned with synthetic minority oversampling in embedding space. On a public forum dataset of 8,675 users, P2P reports average improvements of 8.2% in accuracy, 20.17% in F1, and 4.1% in AUC over 10 baselines. If correct, this would make LLM-based retrieval-augmented inference a practical step beyond feature-engineered classifiers for personality prediction from text.

What carries the argument

The load-bearing mechanism is a retrieval-augmented dual-LLM loop. Step one: a locally deployed LLM, fine-tuned on the MBTI prediction task with synthetic minority oversampling, converts raw posts into an interpretable assessment of the user's social tendency, information processing, decision-making, and lifestyle. Step two: the assessment and the posts are embedded and concatenated into a query vector, and exact k-nearest-neighbor search over a vector database of training users returns the k most similar posts together with their ground-truth MBTI labels. Step three: the original posts, the local assessment, and the retrieved demonstrations are assembled into one prompt for an online LLM, w

What would settle it

Compute P2P's AUC using the online LLM's own predictive probabilities (for example, the log-probabilities of the four output letters) on the same test split and compare with baseline AUCs; if the gap vanishes or reverses, the claimed AUC superiority is an artifact of the retrieval-label proxy. Alternatively, run a k-NN-only predictor on the same vector database without any LLM and check whether it matches P2P's accuracy; if it does, the LLM stages add little beyond retrieval.

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Extended reading notes

Core claim

The central claim is that LLM-based MBTI prediction fails when a single model is asked to reason directly from raw posts, and that the failure can be repaired by splitting the task across two models connected by a retrieval index. The paper's P2P framework first runs a locally fine-tuned LLM over a user's posts to produce a short psychological feature text. Those features, together with the posts, are embedded and used to retrieve the top-k most similar training users from a vector database; the retrieved posts and their ground-truth MBTI labels become demonstrations in a prompt sent to an online LLM, which returns the four-letter type. The paper reports that this design raises average accur

Load-bearing premise

The load-bearing premise is that the ranking-quality metric is faithfully estimated from the ground-truth labels of the retrieved neighbor posts; if the online LLM's own confidence order differs from that neighbor-label distribution, the reported advantage over baselines is not established.

Editorial extensions

If this is right

  • Removing RAG drops I/E accuracy from 0.9321 to 0.8287, so retrieved demonstrations are the largest single contributor to P2P's performance.
  • Fine-tuning without SMOTE still beats no fine-tuning, but SMOTE recovers accuracy on minority MBTI types such as ESFJ and ESTJ.
  • Swapping the online LLM to a different family lowers accuracy, suggesting that model-family consistency between the feature extractor and the final predictor matters.
  • All models, including P2P, score lowest on judging versus perceiving, marking that dimension as the hardest to infer from text.
  • The full pipeline's gains over BERT-style encoders support the use of dual-LLM retrieval-augmented inference for personality-related text classification.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: A stricter evaluation would compute AUC from the online LLM's own predicted probabilities rather than from the retrieved neighbors' label distribution; that test could narrow the reported 4.1% AUC margin.
  • Inference: The same two-stage retrieval recipe should transfer to other text-based psychological inventories such as the Big Five, since nothing in the design is MBTI-specific.
  • Inference: Because retrieval alone carries much of the gain, a k-nearest-neighbor classifier on the same vector database would reveal how much of P2P's accuracy comes from the LLM stages versus the index itself.
  • Inference: Synthetic oversampling in embedding space creates training points that have no textual form; generating synthetic posts and re-embedding them might improve minority-type robustness further.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 3 minor

Summary. The paper proposes PostToPersonality (P2P), a dual-LLM framework for MBTI prediction from social media posts. P2P has three stages: a fine-tuned local LLM (DeepSeek-R1-8B) extracts interpretable psychological features; these features are combined with the original posts to retrieve the top-k most similar training entries from a vector database; and an online LLM (DeepSeek-V3) makes the final four-dimensional MBTI prediction using the original posts, the extracted features, and the retrieved demonstrations as in-context examples. The authors claim state-of-the-art performance on the PersonalityCafe dataset, reporting average gains of 8.2% in accuracy, 20.17% in F1, and 4.1% in AUC over 10 ML/DL baselines, with ablations for PEFT, SMOTE, RAG, and model choices.

Significance. If the reported results were trustworthy, P2P would be a meaningful step for MBTI inference from text and would demonstrate a useful integration of fine-tuned local LLMs, retrieval augmentation, and commercial LLM reasoning. The architecture is clearly described and the ablation study is reasonably comprehensive. However, the empirical contribution is currently undermined by two load-bearing problems: the test set appears to contain explicit MBTI self-reports that the online LLM can directly read, and the reported AUC is not computed from the LLM's own predictions but from the label distribution of retrieved neighbors. These issues affect every quantitative claim in the paper, so the central SOTA conclusion is not supported by the present evidence.

major comments (5)
  1. [§4.1 (Dataset) and Appendix C/D] The evaluation is contaminated by explicit MBTI self-reports. The preprocessing described in §4.1 does not remove MBTI type names, and the prompt in Appendix C feeds the original posts in <CONTENT> to the online LLM. The dataset is from PersonalityCafe, a forum dedicated to MBTI discussion, and the case studies in Appendix D confirm that users write statements such as "I'm serious considering whether I'm actually an ESTP" and "before I knew I was an INTJ". Under these conditions, high accuracy (e.g., 0.9321 on I/E) may reflect the LLM extracting a directly stated label rather than inferring personality from indirect linguistic cues. This makes the comparison with the 10 baselines unfair and invalidates the SOTA claim. The authors must quantify how many test posts contain explicit type mentions, remove such posts (or at least report performance on a filtered subset), and rerun all baselin
  2. [§4.1 (Evaluation Metrics)] The AUC reported for P2P is not the AUC of the online LLM. The paper states that "predictive probabilities for computing AUC are approximated by the retrieved k samples in the RAG" and that the probability is the distribution of ground-truth labels among those samples. This measures a k-NN classifier on the retrieval database, not the LLM's ranking ability. All baselines compute AUC from their own probability outputs, so the claimed 4.1% average AUC improvement is not a comparison of the same quantity. The paper should report AUC from the LLM's predictive probabilities (e.g., token logits) or omit AUC from the comparison.
  3. [Tables 1 and 2] All results are single-run point estimates with no error bars, confidence intervals, or significance tests. Since the pipeline uses stochastic API decoding and the retrieval-based approximation, differences such as 0.9306 vs. 0.9321 between rows (a) and (m) of Table 2 could easily be noise. The authors should report mean and standard deviation over multiple runs and, where appropriate, paired significance tests to support the claimed improvements.
  4. [§4.1 (Baselines) and Table 1] The DeepSeek-R1-8B baseline appears to be the unfine-tuned local LLM. Its near-chance accuracy on several dimensions (e.g., 0.4181 on N/S, 0.5401 on T/F) suggests that the comparison conflates the effect of fine-tuning with the effect of the full P2P framework. If the baseline is zero-shot, this should be stated explicitly; a stronger baseline would be the same local LLM fine-tuned without RAG, or an equivalently sized fine-tuned LLM.
  5. [Eq. (2)] The SMOTE update is written as x̂_i = x_i + λ(x_i − x_j), λ ∼ Uniform(0,1). This is not the standard SMOTE interpolation x_i + λ(x_j − x_i), and it is inconsistent with the preceding sentence that says P2P interpolates "between x_i and x_j". As written, the new sample moves away from x_j rather than along the segment connecting the two points. Please correct the sign or clarify the intended operation and verify the experiments use the corrected formula.
minor comments (3)
  1. [Figure 1] The text in Figure 1 is partially garbled in the manuscript image (e.g., "ENTJESFP……"), making the pipeline diagram hard to read. Please provide a clean version.
  2. [Notation, Eq. (2)-(3)] The symbols x_i and x_i are used inconsistently for the hidden representation and its average pooling. The same symbol k is used for the number of SMOTE neighbors in Eq. (3) and for the RAG retrieval size in §3.1; please disambiguate.
  3. [§4.2.2] Variant (d) in Table 2 shows a very large drop in N/S accuracy (0.4181) compared with variant (c), but this is not discussed. A short explanation would help readers understand whether this is an instability or an artifact of the setting.

Circularity Check

1 steps flagged · score 3.0 of 10

AUC metric is self-defined from retrieved training-label distribution; core accuracy/F1 results are independent.

  1. self definitional [Section 4.1, Evaluation Metrics]
    "For P2P, the F1 and Accuracy are computed from the final results of the API call, while predictive probabilities for computing AUC are approximated by the retrieved k samples in the RAG. Predictive probability is approximated by the distribution of the ground-truth labels of these samples with appropriate smoothing."

    The paper's reported AUC for P2P is not computed from the model's own output probabilities; instead the 'predictive probability' is defined as the distribution of ground-truth labels among the k retrieved training posts. Because those retrieved posts and labels are inputs to the RAG prompt, the AUC measures how well the retrieval step finds same-labeled training examples, not how well the LLM ranks personality types. The abstract's claim of '4.1% higher AUC' is therefore an artifact of this self-defined metric, not an independent prediction of the LLM. Accuracy and F1 are unaffected, but the AUC comparison against baselines is methodologically incommensurable.

full rationale

The paper contains no self-citation chain, no imported uniqueness theorem, and no equation-level derivation that reduces to its own inputs. The P2P framework is an empirical pipeline: local LLM feature extraction, RAG retrieval, and online LLM prediction. The headline accuracy and F1 scores are computed from the final four-letter output of the API call, so those results are independent of the retrieved-label distribution aside from normal training supervision. The only circularity-adjacent element is the AUC metric, which is explicitly defined in terms of the ground-truth label distribution among retrieved training samples rather than the model's own probabilities. This makes the '4.1% higher AUC' statement a defined property of the retrieval distribution, not a measured property of the LLM's ranking. A separate concern—that PersonalityCafe posts often contain explicit MBTI self-reports (e.g., 'before I knew I was an INTJ' in Appendix D)—is a benchmark-contamination risk rather than a circularity of the derivation, and is not counted in the score. Overall, the central accuracy/F1 superiority claim retains independent empirical content; the score reflects the partial self-definition of one of the three reported metrics.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The framework relies on several domain-specific assumptions about text-to-personality mapping and the validity of representation-space oversampling. No new physical or theoretical entities are introduced. The only tuned hyperparameters listed are k (validated in Table 2) plus an unspecified distance weight in Eq. (3).

free parameters (2)
  • top-k (number of retrieved demonstrations) = 5 (default; ablated over 2,3,4,5,6 in Table 2)
    Chosen via validation set; the central result depends on this hyperparameter.
  • distance weight in SMOTE neighbor selection = unspecified
    Eq. (3) uses a weighted sum of L2 and cosine distance but the weight is not reported; this affects which neighbors are chosen for interpolation.
assumptions (4)
  • domain assumption MBTI type can be reliably inferred from a user's 50 recent forum posts
    The task assumes text is a valid signal for self-reported MBTI and that PersonalityCafe labels are accurate ground truth; not validated.
  • domain assumption Fine-tuned local LLM's generated textual features preserve personality-relevant information
    Step 1 assumes feature extraction adds signal; ablation shows it contributes but no independent validation.
  • domain assumption Retrieved similar posts with ground-truth labels provide useful demonstrations for in-context learning
    RAG effectiveness is assumed and empirically supported on this dataset, but not tested for hallucination reduction directly.
  • domain assumption SMOTE interpolation in hidden space yields valid synthetic training examples
    Eq. (2)-(3) interpolate hidden representations; assumes the interpolation lies in a semantically meaningful region, plausible but not proven.

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Cite this review

Pith. "Pith review of From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media." pith.science (2026). https://pith.science/paper/IUZLNWLC

@misc{pith2026250904461,
  author       = {Pith},
  title        = {Pith review of: From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IUZLNWLC}},
  note         = {Machine review of arXiv:2509.04461}
}
read the original abstract

Personality prediction from social media posts is a critical task that implies diverse applications in psychology and sociology. The Myers Briggs Type Indicator (MBTI), a popular personality inventory, has been traditionally predicted by machine learning (ML) and deep learning (DL) techniques. Recently, the success of Large Language Models (LLMs) has revealed their huge potential in understanding and inferring personality traits from social media content. However, directly exploiting LLMs for MBTI prediction faces two key challenges: the hallucination problem inherent in LLMs and the naturally imbalanced distribution of MBTI types in the population. In this paper, we propose PostToPersonality (PtoP), a novel LLM based framework for MBTI prediction from social media posts of individuals. Specifically, PtoP leverages Retrieval Augmented Generation with in context learning to mitigate hallucination in LLMs. Furthermore, we fine tune a pretrained LLM to improve model specification in MBTI understanding with synthetic minority oversampling, which balances the class imbalance by generating synthetic samples. Experiments conducted on a real world social media dataset demonstrate that PtoP achieves state of the art performance compared with 10 ML and DL baselines.

Figures

Figures reproduced from arXiv: 2509.04461 by the authors.

Figure 1
Figure 1. illustrates an overview of the P2P framework, which predicts the personality of a user from her posts in three sequential steps, leveraging a fine-tuned local LLM, a post-personality vector data￾base, and online LLM calling. In step 1, P2P generates interpretable ENTJ ESFP … … Response: You appear to be a very interesting individual who enjoys adventure and exploring new areas, interacting with others, interpreting … view at source ↗
Figure 2
Figure 2. (a) illustrates a well-performed case where both the pre￾diction and the ground-truth label are ENTP. In this case, words such as ‘problem’ (0.81) and ‘controversy’ (0.94) receive high atten￾tion weights, closely aligning with the ENTP ‘Debater’ personality traits. Similarly, cognitive-related words like ‘thought’ (0.84) and ‘think’ (0.54) are also assigned prominent weights, reflecting the tendency of ENTP to analy… view at source ↗
Figure 3
Figure 3. Statistics of the Dataset A Implementation details of baselines In traditional ML models, the Multi-nomial Naive Bayes computes feature weights by counting term frequencies within posts and com￾bining these with the inverse document frequency, forming a sparse high-dimensional word feature space. The model utilizes TF-IDF weighted term frequency vectors as input features and employs the default smoothing parameter 𝛼… view at source ↗

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