Large Language Models for Interpretable Mental Health Diagnosis

Brian Hyeongseok Kim; Chao Wang

REVIEW 4 major objections 5 minor 35 references

Large Language Models for Interpretable Mental Health Diagnosis

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read An LLM-translated, expert-corrected Datalog program diagnoses 30 of 30 synthetic mood-disorder patients correctly, with every rule open to inspection.

desk verdict The 30/30 result is a closed-loop self-consistency check, not a faithful ICD-11 validation, but the LLM-to-Datalog-with-expert-review integration is novel and worth referee time. read the letter →

arxiv 2501.07653 v2 pith:YTMUIUVD submitted 2025-01-13 cs.AI cs.LO

Brian Hyeongseok Kim , Chao Wang This is my paper · ORCID

classification cs.AIcs.LO

keywords largelanguagemodelsconstraintlogicprogrammingDatalogclinicaldecisionsupportsystemmentalhealthdiagnosisICD-11CDDRinterpretabilityexpert-in-the-loop

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

This paper proposes a clinical decision support system that combines large language models with constraint logic programming for mental health diagnosis. The idea is to have an LLM translate diagnostic criteria from a manual such as the ICD-11 CDDR into Datalog rules, let a domain expert review and correct those rules, and then run them in a Datalog engine to answer patient-specific queries. The paper reports that the expert-corrected program gives 30 correct diagnoses out of 30 synthetic patients, while LLM-only diagnosis and unedited LLM-generated programs score at most 22 out of 30. If true, the central payoff is not just accuracy but interpretability: every diagnosis is backed by explicit rules that a clinician can inspect and edit.

What carries the argument

The engine of the method is a Datalog program whose rules map a patient's observed symptoms and episode history to a disorder diagnosis. Input relations Observed(Patient, Symptom, Week) and History(Patient, Condition, Count) feed aggregate count rules that separate core from qualifying symptoms, sum them, and apply thresholds such as at least one core symptom, at least two symptoms total, and at least one prior mood episode to derive Diagnosis(Patient, Disorder). A one-shot prompted GPT-4o produces the candidate program from ICD-11 CDDR text, and the load-bearing step is the expert review that repairs logic errors such as a cyclic definition of MixedEpisode and an exclusive reliance on History rather than current symptoms. The Soufflé Datalog engine then evaluates the rules, which makes the reasoning deterministic and inspectable.

What would settle it

Run the finalized Datalog program on an independent set of real patient records with clinician-adjudicated diagnoses, including comorbid and subthreshold cases, and compare outputs; if accuracy drops materially from the reported 30/30, the rule encoding misses clinical context.

Watch

Extended reading notes

Core claim

The central claim is that LLM-generated logic programs, after expert correction, can faithfully encode the ICD-11 CDDR diagnostic criteria for four mood disorders—Bipolar I, Bipolar II, Single Episode Depressive Disorder, and Recurrent Depressive Disorder—and that running these rules on patient data in a Datalog engine yields diagnoses that are both correct and auditable. The authors show this by building a pipeline in which GPT-4o translates the manual's text into a Datalog program, a clinician reviews and modifies it (57 lines added, 10 removed from the initial 107 lines), and the resulting program answers queries against Observed(Symptom, Weeks) and History(Condition, Count) facts for 30 synthetic patients. The finalized program scores 10/10 on the first ten patients and 30/30 overall, whereas the best unedited LLM-generated program scores 22/30 and the best LLM-only baseline also scores 22/30. The paper argues this demonstrates that LLMs are useful for drafting rules but not sufficient on their own, and that the hybrid design avoids sending patient data to an LLM, addressing privacy concerns.

Load-bearing premise

The claim rests on the assumption that the ICD-11 mood disorder criteria can be captured losslessly by deterministic Datalog rules that only count symptom names, durations, and history counts, leaving out clinical judgment, severity, and context.

Editorial extensions

If this is right

LLM-only diagnosis, even when accurate, remains opaque and should not be used alone for clinical decisions.
Unedited LLM-generated Datalog programs cannot be trusted for diagnosis; the best as-is program still missed or wrongly added diagnoses in 8 of 30 cases.
A human expert can audit and fix the generated rules in a few dozen lines of code, making the pipeline practical.
The hybrid design lets clinicians see the exact rule and data that produced a diagnosis, which is a prerequisite for clinical accountability.
Because patient facts are consumed by a local Datalog engine rather than an LLM, the approach reduces privacy risk.

Reading between the lines

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

The same translation-and-review pipeline could be applied to DSM-5-TR or to other branches of ICD-11, though the one-shot schizophrenia example in the prompt may need to be replaced by domain-matched examples.
The encoding's reliance on symptom names and durations suggests it will need a richer patient schema—severity, onset, impairment, and substance-induced exclusions—before it can transfer from synthetic patients to real electronic health records.
One could measure the expert effort more precisely by having multiple clinicians independently correct the same LLM-generated program and checking whether the resulting rule sets agree; the paper reports line-change statistics for a single correction pass.
If LLM code-generation quality improves, the remaining expert workload should shift from fixing logic errors to validating coverage, which could be tested by repeating the experiment on later model versions.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague—

If you care about LLM-to-logic pipelines for medical decision support, read this, but don't trust the headline number. The 30/30 in Section 4.3 is a self-consistency check, not validation against an independent gold standard.

What's new: combining LLM translation of diagnostic manuals into Datalog with expert review and a CLP engine is genuinely novel for mental-health CDSS, as far as the cited literature goes. The paper also does real work: it tests three LLMs and gives concrete error analyses. GPT latches onto History and ignores current symptoms; Gemini ignores History and emits conflicting diagnoses; Llama uses arbitrary thresholds. The RQ3 numbers (57 lines added, 10 removed from 107 initial LoC) are an honest measure of expert effort. And they don't oversell—the ethical statement calls it hypothetical proof-of-concept.

The soft spots are real. The "Known Disorder" labels come from the authors' own manually written Datalog program, and the same people who corrected the GPT program also validated those labels. Cross-checking against the manual doesn't break the circularity, because the schema and the labels share the same interpretation. Second, the schema is too thin: Observed(Symptom, Weeks) and History(Condition, Count) cannot express exclusion clauses, clinically significant distress/impairment, or course specifiers that ICD-11 CDDR requires. Section 6 defers these to future work. So even a perfect gold standard would only show that the Datalog program agrees with the authors' simplified criteria on 30 synthetic vignettes. The interpretability claim inherits this: inspectable rules don't reveal which official criteria were omitted. And 30 synthetic patients, no inter-rater or statistical validation, is thin quantitative evidence.

That said, the architecture is plausible, and the qualitative conclusion—LLMs need expert oversight for this task—is well-supported by the error analysis. This is a useful proof of concept, not a validated clinical tool. I'd send it to peer review: a good referee can push for an independent gold standard (or at least a second annotator), a more expressive schema, and more honest claims. As it stands, major revision is needed before the 30/30 claim can be taken at face value.

Worth a reading group slot on evaluation design for LLM-based CDSS. I'd cite it as related work in logic-based LLM translation, but not as evidence of diagnostic accuracy.

Referee Report

4 major / 5 minor

Summary. The paper proposes a clinical decision support system (CDSS) that uses an LLM to translate ICD-11 CDDR mood disorder criteria into a Datalog program, which is then manually reviewed and corrected by a domain expert and executed with a Soufflé CLP engine to produce diagnoses from structured patient facts (Observed and History relations). The authors evaluate the approach on 30 synthetic patients, comparing (i) direct LLM diagnosis, (ii) LLM-generated Datalog programs without expert correction, and (iii) their expert-corrected program ('Our CDSS'). They report that the corrected program achieves 10/10 and 30/30 correct diagnoses, and they provide qualitative error analyses for GPT, Gemini, and Llama, along with an account of the manual corrections in terms of lines of code added and removed.

Significance. If the central claim were established, the paper would make a useful contribution: it demonstrates a concrete pipeline for turning LLM-generated rules into an inspectable, executable logic program, which addresses a real need for interpretability and verifiability in AI-assisted mental health diagnosis. The authors are also appropriately candid about the limitations of direct LLM use and about the need for expert oversight. The interpretability benefit is inherent to the Datalog representation and is credible. However, the quantitative 30/30 result is not yet evidence of diagnostic accuracy, because the gold standard is the authors' own manually written Datalog interpretation and because the input schema cannot express several ICD-11 criteria. The paper is best read as a proof-of-concept for LLM-to-Datalog translation with expert repair; as it stands, the headline accuracy claim is not supported.

major comments (4)

[Section 4.3, Tables 1 and 2] The 'Known Disorder' labels used as the gold standard are derived from the authors' own manually written Datalog program, not from an independent clinical assessment. The finalized expert-corrected program was then manually patched until it matched those labels, so a 30/30 score largely demonstrates self-consistency between two author-produced encodings rather than diagnostic accuracy. To support the paper's accuracy claim, the authors need an external gold standard (e.g., diagnoses by independent clinicians, or at least inter-rater reliability statistics) or must explicitly reframe the result as 'agreement with the authors' Datalog encoding of ICD-11.'
[Section 3.1, Listing 2, and Section 6] The input schema, consisting only of Observed(Symptom, Week) and History(Condition, Count), cannot represent elements that ICD-11 CDDR mood disorder criteria require, including exclusion of symptoms attributable to substance use or a medical condition, clinically significant distress or functional impairment, and course specifiers that distinguish single-episode from recurrent disorders. The mixed-episode logic in Listing 3 also goes beyond the simple schema by introducing symptom thresholds that are not specified in the paper's input relations. Section 6 itself defers 'more nuanced diagnostic criteria and specifiers' to future work. Therefore the 30/30 result cannot establish that the program 'faithfully encodes' ICD-11 CDDR; it only shows that the program classifies the 30 synthetic vignettes in agreement with the authors' simplified encoding.
[Section 4.3, RQ3, Listing 3] The expert-effort analysis quantifies corrections only as lines of code added (57) and removed (10), with a claim that the first set of corrections required significant domain expertise. This is not a meaningful measure of the effort or reliability of expert review: it omits time spent, number of iterations, and the expert's qualifications, and the expert is not independent of the authors. Since the paper's central value proposition is that expert review guarantees faithfulness to the diagnostic manual, the authors should report the expert's credentials, specify whether more than one expert was involved, and provide inter-rater reliability or a structured review protocol.
[Section 4.3 and Appendix A] The evaluation is based on 30 synthetic patients with no statistical analysis, no confidence intervals, and no comparison on real clinical data. The Ethical Statement correctly describes the data as hypothetical and intended for proof of concept, but the abstract and conclusion make stronger claims of accuracy and safety. The authors should either temper the claims to match the proof-of-concept scope or add validation on a realistic dataset with statistical measures.

minor comments (5)

[Table 2, final row] The notation '(8+4)/30' for Gemini+Datalog is ambiguous; clarify whether partial credit is counted and, if so, how it is weighted.
[Section 4.3, RQ1 paragraph] The statement 'GPT performs the best with 7 correct diagnoses out of 10' could confuse because GPT also achieves 9/10 in the LLM-only column; specify that the 7/10 refers to the LLM+Datalog baseline.
[Appendix C.1] There is a typo: 'Scizhophrenia' should be 'Schizophrenia' in the prompt example.
[Section 3.2] The phrase 'the diagnoses that they provide are guaranteed to be correct' is conditional on the rules accurately reflecting the manual; rephrase to avoid an unconditional guarantee.
[Table 3] The 'Mood Episode' column appears to report a derived classification, but the table caption does not explain whether this is an input fact or an output of the program; clarify its role.

Circularity Check

1 steps flagged · score 7.0 of 10

The 30/30 accuracy claim is self-referential: the gold-standard labels and the tested program are both the authors' own Datalog encodings of ICD-11, manually aligned with each other.

fitted input called prediction [Section 4.3, Table 1 caption and results paragraph (also Table 2 caption)]
"Columns 1-2 list patient numbers and their disorders based on our manually written Datalog program, validated against the ICD-11 CDDR criteria. ... We extend the most accurate program generated by GPT and implement logical changes to align with the ICD-11 CDDR criteria for the mood disorders. The expert-reviewed program, shown in Column 9, produces 10 correct diagnoses out of 10 (30 out of 30)."

The 'Known Disorder' gold standard is the authors' own manually written Datalog program, not an independent clinical label set. The system being scored ('Our CDSS') is the authors' expert-corrected Datalog program, produced by manually patching LLM output (57 lines added, 10 removed) to implement the same ICD-11 criteria used to build those labels. Reporting 10/10 (30/30) therefore measures agreement between two hand-written encodings by the same authors and is forced by the editing process; it does not demonstrate that the encoding faithfully matches the ICD-11 manual. The LLM-only and LLM+Datalog baseline numbers remain non-circular, but the central accuracy claim is.

full rationale

The paper's headline quantitative claim is circular: the 'Known Disorder' column in Tables 1-2 is described as 'based on our manually written Datalog program', and 'Our CDSS' is the same authors' expert-corrected Datalog program. The final program was created by manually adding and removing rules to align with the same criteria used to define the labels, so 30/30 is a self-consistency score rather than an external accuracy measure. The non-circular parts are the LLM-only and LLM+Datalog baselines (GPT 22/30, Gemini 8+4/30, Llama 9/30) and the reported code diff (57 lines added, 10 removed); these genuinely support the claim that LLM-generated programs require expert correction. However, those observations do not rescue the central '30/30' result. Section 6 concedes future work must 'extend the Datalog encoding to address more nuanced diagnostic criteria and specifiers', and the Observed/History schema cannot express exclusions or functional impairment, so even the intended gold standard is a simplified author interpretation. No self-citation load-bearing chain is present. Score 7: the central accuracy claim reduces substantially to the authors' own encoding, though the paper contains independent empirical content.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper's contribution is a software pipeline rather than a model with fitted constants. The only human-chosen elements are the expert corrections to the LLM code, the manual thresholds taken from ICD-11 (not fitted), and the gold-standard labels, which are the authors' own manual encoding. No metric is fitted to the data, so the free-parameter count is zero, but the evaluation contains a self-referential gold standard captured in the axioms.

assumptions (3)

domain assumption The expert-reviewed Datalog program faithfully encodes the ICD-11 CDDR diagnostic criteria for Bipolar I, Bipolar II, Single Episode Depressive Disorder, and Recurrent Depressive Disorder.
The 30/30 accuracy result in Section 4.3 depends on the authors' manual interpretation of the manual being the correct one; no external clinician or independent expert validation is provided.
domain assumption All clinically relevant patient information is expressible as Observed(Symptom, Weeks) and History(Condition, Count) facts.
Listing 2 in Section 3.1 defines the schema; the ethical statement concedes real patient data 'may be more complex', so this simplification is load-bearing for clinical applicability.
ad hoc to paper The 'Known Disorder' labels in Tables 1 and 2, derived from the authors' own manually written Datalog program and validated manually, are correct diagnoses for the 30 test patients.
Section 4 defines the gold standard as the authors' own encoding of the same manual used to build the system under test, which makes the perfect score partly circular.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Large Language Models for Interpretable Mental Health Diagnosis." pith.science (2026). https://pith.science/paper/YTMUIUVD

@misc{pith2026250107653,
  author       = {Pith},
  title        = {Pith review of: Large Language Models for Interpretable Mental Health Diagnosis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YTMUIUVD}},
  note         = {Machine review of arXiv:2501.07653}
}

read the original abstract

We propose a clinical decision support system (CDSS) for mental health diagnosis that combines the strengths of large language models (LLMs) and constraint logic programming (CLP). Having a CDSS is important because of the high complexity of diagnostic manuals used by mental health professionals and the danger of diagnostic errors. Our CDSS is a software tool that uses an LLM to translate diagnostic manuals to a logic program and solves the program using an off-the-shelf CLP engine to query a patient's diagnosis based on the encoded rules and provided data. By giving domain experts the opportunity to inspect the LLM-generated logic program, and making modifications when needed, our CDSS ensures that the diagnosis is not only accurate but also interpretable. We experimentally compare it with two baseline approaches of using LLMs: diagnosing patients using the LLM-only approach, and using the LLM-generated logic program but without expert inspection. The results show that, while LLMs are extremely useful in generating candidate logic programs, these programs still require expert inspection and modification to guarantee faithfulness to the official diagnostic manuals. Additionally, ethical concerns arise from the direct use of patient data in LLMs, underscoring the need for a safer hybrid approach like our proposed method.

Figures

Figures reproduced from arXiv: 2501.07653 by the authors.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

35 extracted references · 15 canonical work pages

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...
[3]

American Psychiatric Association . 2022. Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR. American Psychiatric Association Publishing. ISBN 9780890425763

work page 2022
[4]

American Psychological Association . 2023. Psychologists reaching their limits as patients present with worsening symptoms year after year

work page 2023
[5]

Bai, Z.; Wang, P.; Xiao, T.; He, T.; Han, Z.; Zhang, Z.; and Shou, M. Z. 2024. Hallucination of multimodal large language models: A survey. arXiv preprint arXiv:2404.18930

arXiv 2024
[6]

Berner, E. S. 2007. Clinical decision support systems, volume 233. Springer

work page 2007
[7]

Bowles, J.; Caminati, M.; Cha, S.; and Mendoza, J. 2019. A framework for automated conflict detection and resolution in medical guidelines. Science of Computer Programming, 182: 42--63

work page 2019
[8]

Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, ...

arXiv 2020

Show all 35 references

[9]

M.; and Colomo-Palacios, R

Casado-Lumbreras, C.; Rodríguez-González, A.; Álvarez Rodríguez, J. M.; and Colomo-Palacios, R. 2012. PsyDis: Towards a diagnosis support system for psychological disorders. Expert Systems with Applications, 39(13): 11391--11403

2012
[10]

Dong, Q.; Li, L.; Dai, D.; Zheng, C.; Wu, Z.; Chang, B.; Sun, X.; Xu, J.; and Sui, Z. 2022. A survey on in-context learning. arXiv preprint arXiv:2301.00234

2022 arXiv
[11]

Friha, O.; Amine Ferrag, M.; Kantarci, B.; Cakmak, B.; Ozgun, A.; and Ghoualmi-Zine, N. 2024. LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness. IEEE Open Journal of the Communications Society, 5: 5799--5856

2024
[12]

Gemini Team, Google . 2024. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv:2403.05530

2024 arXiv
[13]

Gorvin, L.; and Brown, D. 2012. The psychology of feeling like a burden: A review of the literature. Social Psychology Review, 14(1): 28--41

2012
[14]

Huang, J.; and Chang, K. C.-C. 2023. Towards Reasoning in Large Language Models: A Survey. arXiv:2212.10403

2023 arXiv
[15]

Huang, L.; Yu, W.; Ma, W.; Zhong, W.; Feng, Z.; Wang, H.; Chen, Q.; Peng, W.; Feng, X.; Qin, B.; and Liu, T. 2023. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. arXiv:2311.05232

2023 arXiv
[16]

Imani, S.; Du, L.; and Shrivastava, H. 2023. Mathprompter: Mathematical reasoning using large language models. arXiv preprint arXiv:2303.05398

2023 arXiv
[17]

Jiang, J.; Wang, F.; Shen, J.; Kim, S.; and Kim, S. 2024. A Survey on Large Language Models for Code Generation. arXiv preprint arXiv:2406.00515

2024 arXiv
[18]

Jordan, H.; Scholz, B.; and Suboti \' c , P. 2016. Souffl \'e : On Synthesis of Program Analyzers. In Chaudhuri, S.; and Farzan, A., eds., Computer Aided Verification, 422--430. Cham: Springer International Publishing. ISBN 978-3-319-41540-6

2016
[19]

G.; and Scott Morton, M

Keen, P. G.; and Scott Morton, M. S. 1978. Decision support systems: an organizational perspective. (No Title)

1978
[20]

M.; Li, D.; Cao, H.; Ren, T.; Liao, Z.; and Wu, J

Liu, J. M.; Li, D.; Cao, H.; Ren, T.; Liao, Z.; and Wu, J. 2023. ChatCounselor: A Large Language Models for Mental Health Support. arXiv:2309.15461

2023 arXiv
[21]

Llama Team, AI @ Meta . 2024. The Llama 3 Herd of Models. arXiv:2407.21783

2024 arXiv
[22]

Moore, J.; Deshpande, T.; and Yang, D. 2024. Are Large Language Models Consistent over Value-laden Questions? arXiv:2407.02996

2024 arXiv
[23]

OpenAI. 2024. GPT-4 Technical Report. arXiv:2303.08774

2024 arXiv
[24]

Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C. L.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; Schulman, J.; Hilton, J.; Kelton, F.; Miller, L.; Simens, M.; Askell, A.; Welinder, P.; Christiano, P.; Leike, J.; and Lowe, R. 2022. Training language model...

2022 arXiv
[25]

Scholz, B.; Jordan, H.; Suboti\' c , P.; and Westmann, T. 2016. On fast large-scale program analysis in Datalog. In Proceedings of the 25th International Conference on Compiler Construction, CC '16, 196–206. New York, NY, USA: Association for Computing Machinery. ISBN 9781450342414

2016
[26]

M.; and Singh, R

Ullah, E.; Parwani, A.; Baig, M. M.; and Singh, R. 2024. Challenges and barriers of using large language models (LLM) such as ChatGPT for diagnostic medicine with a focus on digital pathology--a recent scoping review. Diagnostic pathology, 19(1): 43

2024
[27]

Wang, H.; Zhao, S.; Qiang, Z.; Xi, N.; Qin, B.; and Liu, T. 2024. Beyond Direct Diagnosis: LLM-based Multi-Specialist Agent Consultation for Automatic Diagnosis. arXiv preprint arXiv:2401.16107

2024 arXiv
[28]

V.; Zhou, D.; et al

Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35: 24824--24837

2022
[29]

World Health Organization . 2024. Clinical descriptions and diagnostic requirements for ICD-11 mental, behavioural and neurodevelopmental disorders. World Health Organization. ISBN 9789240077263

2024
[30]

K.; and Wang, D

Xu, X.; Yao, B.; Dong, Y.; Gabriel, S.; Yu, H.; Hendler, J.; Ghassemi, M.; Dey, A. K.; and Wang, D. 2024. Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Tech...

2024
[31]

Yang, K.; Ji, S.; Zhang, T.; Xie, Q.; Kuang, Z.; and Ananiadou, S. 2023. Towards Interpretable Mental Health Analysis with Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics

2023
[32]

Ye, X.; Chen, Q.; Dillig, I.; and Durrett, G. 2023. SatLM: Satisfiability-Aided Language Models Using Declarative Prompting. In Oh, A.; Naumann, T.; Globerson, A.; Saenko, K.; Hardt, M.; and Levine, S., eds., Advances in Neural Information Processing Systems, volume 36, 45548-...

2023
[33]

Zhang, Y.; Mao, S.; Ge, T.; Wang, X.; de Wynter, A.; Xia, Y.; Wu, W.; Song, T.; Lan, M.; and Wei, F. 2024. LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models. arXiv:2404.01230

2024 arXiv
[34]

Zhao, H.; Chen, H.; Yang, F.; Liu, N.; Deng, H.; Cai, H.; Wang, S.; Yin, D.; and Du, M. 2023. Explainability for Large Language Models: A Survey. arXiv:2309.01029

2023 arXiv
[35]

Zheng, K.; Decugis, J.; Gehring, J.; Cohen, T.; Negrevergne, B.; and Synnaeve, G. 2024. What Makes Large Language Models Reason in (Multi-Turn) Code Generation? arXiv:2410.08105

2024 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.a...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in capitalize " " * FUNCT...

[3] [3]

American Psychiatric Association . 2022. Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR. American Psychiatric Association Publishing. ISBN 9780890425763

work page 2022

[4] [4]

American Psychological Association . 2023. Psychologists reaching their limits as patients present with worsening symptoms year after year

work page 2023

[5] [5]

Bai, Z.; Wang, P.; Xiao, T.; He, T.; Han, Z.; Zhang, Z.; and Shou, M. Z. 2024. Hallucination of multimodal large language models: A survey. arXiv preprint arXiv:2404.18930

arXiv 2024

[6] [6]

Berner, E. S. 2007. Clinical decision support systems, volume 233. Springer

work page 2007

[7] [7]

Bowles, J.; Caminati, M.; Cha, S.; and Mendoza, J. 2019. A framework for automated conflict detection and resolution in medical guidelines. Science of Computer Programming, 182: 42--63

work page 2019

[8] [8]

Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, ...

arXiv 2020