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REVIEW 4 major objections 7 minor 52 references

The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support

T0 review · 4 major / 7 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A single fine-tuned LLM can chat empathetically and simultaneously emit interpretable emotion distributions that flag suicide-risk text with 0.95 recall.

desk verdict The RACLETTE emotion-classification and empathetic-response system is real engineering, but the screening claim rests on an undocumented split between reference and test subreddit posts, so the 0.95 recall is likely self-similarity. read the letter →

arxiv 2412.20068 v1 pith:IXM3FSE2 submitted 2024-12-28 cs.HC cs.AIcs.CL

Alessandro De Grandi , Federico Ravenda , Andrea Raballo , Fabio Crestani This is my paper · ORCID
classification cs.HCcs.AIcs.CL
keywords empatheticconversationalAIemotionembeddingsemotionalprofilesmentalhealthscreeningsuicideriskdetectionlargelanguagemodelsexplainablemarkerssupport
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 argues that one LLM can do both halves of mental-health support — talk empathetically and screen for risk — using a single shared representation: the user's emotional profile. It introduces RACLETTE, a fine-tuned 7-billion-parameter language model that predicts which of 32 emotions a speaker is feeling as a next-token generation task, replies empathetically, and accumulates the sampled emotion distributions into an explainable profile. The paper then claims these profiles are interpretable markers: comparing a profile to pre-computed emotion distributions from subreddits such as SuicideWatch and r/depression separates suicidal from ordinary text, with the combined detector reaching 0.95 recall. If correct, the approach offers preliminary screening without clinical labels or sensitive therapy data, since the signals are built from public text and from the emotions the model itself reads.

What carries the argument

The load-bearing object is the emotion embedding: a probability vector over 32 emotion classes built by repeated Top-K sampling from the fine-tuned decoder. For each prompt the model predicts an emotion as a next token; sampling ten times produces an empirical distribution over emotions, and averaging those distributions across the turns of a conversation gives the user's emotional profile. The same procedure applied to subreddit posts produces reference disorder profiles, so the profile is simultaneously the input to the empathetic response (the predicted emotion is inserted into the three-turn prompt) and the marker compared against reference embeddings for screening. Because the embedding is a weighted sum of named emotions, every prediction can be inspected as a list of named emotions with weights rather than an opaque score.

What would settle it

Rebuild the reference embeddings from one randomly chosen half of the SuicideWatch and CasualConversation posts and re-run the 0.95-recall experiment on the other half; if recall on the held-out half falls far below the reported combined score, then part of the signal is the test posts' own contribution to the reference profiles rather than their emotional content. A second probe: swap the reference source, building the 'suicide' profile from a clinically validated corpus of suicide notes, and check whether recall on the same test set survives the swap.

Watch

Extended reading notes

Core claim

Mental states can be represented as mixtures of discrete emotions. By fine-tuning a generative decoder model on the Empathetic Dialogues dataset with a three-turn prompt–emotion–response structure, the paper obtains a conversational agent that predicts a speaker's emotion as autoregressive next-token generation, samples top-10 emotions ten times per prompt, and aggregates these empirical distributions over a conversation into an emotion embedding. Applying the same sampling procedure to 1,000 posts from each of 15 mental-health subreddits yields characteristic disorder profiles, and comparing a user profile against the suicide profile with KL or JS divergence or cosine similarity labels the text at risk. On a binary test set of SuicideWatch versus CasualConversation posts, the method reaches recall 0.95 for suicide risk in its combined variant, while the reference distributions remain visually and mathematically interpretable: depression sits closest to suicide, the depression-plus-schizophrenia embedding lands near bipolar, and control-group profiles from DailyDialog are dominated by positive emotions.

Load-bearing premise

The screening results assume that the posts used to measure suicide risk were not also used to build the reference emotional profiles; the paper draws both from the same set of subreddit posts and never states that the test posts were set aside.

Editorial extensions

If this is right

  • A single fine-tuned LLM can handle both empathetic response generation and emotion classification, and conversation-level context improves emotion accuracy from 56% to 59% on the Empathetic Dialogues test set, beating the 51% of the CAiRE baseline.
  • Emotional profiles of mental disorders are separable and psychologically coherent: suicide overlaps with depression, addiction with alcoholism, schizophrenia with PTSD, and summing the depression and schizophrenia embeddings places the result near bipolar in t-SNE space.
  • Suicide-risk screening can be done without supervised clinical labels: comparing a post's emotion embedding to reference embeddings, with the combined KL/JS/cosine rule, achieves 0.95 recall for identifying suicidal text, at precision 0.63.
  • Because the marker is a distribution over 32 named emotions, a clinician or user can inspect why a text was flagged, addressing the explainability gap of black-box classifiers.
  • The approach avoids requiring sensitive real-world therapy conversations for training, using only public empathetic dialogues and public subreddit text.

Reading between the lines

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

  • Implicit extension: the emotional profile is built cumulatively over conversation turns, so the same comparison could track whether a user's distribution drifts toward the suicide reference over time; the paper reports one-shot classification, not trajectories, so a longitudinal study is a natural next step.
  • The near-arithmetic relation in the reduced space (depression + schizophrenia near bipolar) suggests emotional profiles might predict profiles for conditions with no dedicated community; testing this by synthesizing a profile and checking it against a held-out subreddit would confirm whether the arithmetic is semantically meaningful.
  • The 32-emotion simplex is coarse for clinical use; converting the profile into a small set of named dominant emotions (e.g., 'devastated, lonely, ashamed' for the suicide reference) could produce a plain-language summary a non-specialist can read, which is the direction the authors gesture at but do not implement.
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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

4 major / 7 minor

Summary. The paper introduces RACLETTE, a Mistral-7B model fine-tuned on Empathetic Dialogues with a three-turn prompt/emotion/response format, and evaluates it on emotion recognition and empathetic response generation. The authors then define "emotion embeddings" by sampling top-k emotion tokens and aggregating them over sentences, construct reference embeddings from Reddit mental-health subreddits and DailyDialog, and use these reference embeddings to classify SuicideWatch versus CasualConversation posts in a binary suicide-risk task. The central claim is that emotional profiles built from LLM predictions can serve as interpretable markers for preliminary mental-health screening. The empirical support for this claim rests on Section 6's classification experiment, which reports recall up to 0.95.

Significance. If the screening claim were validated, the idea of using generative LLM emotion distributions as explainable, low-cost markers would be of practical interest for preliminary triage, especially because the approach avoids requiring labeled clinical text. The paper has real strengths: it gives concrete fine-tuning details, uses public datasets, and includes useful qualitative examples of coexisting emotions and model uncertainty. However, the current evaluation does not establish the screening claim: the Section 6 experiment does not document that the reference prototypes and test posts are disjoint, and the decision rule is essentially a nearest-centroid classifier over subreddit-derived prototypes. The emotion-classification comparison in Section 4 is also a single run compared against literature numbers under unspecified protocols. The paper's contribution is therefore better viewed as an unvalidated proof-of-concept than as a demonstrated screening tool.

major comments (4)
  1. [Sections 5–6, Table 4] The load-bearing issue is independence between the reference embeddings and the test set. Section 5 states that reference embeddings for each disorder are computed by processing 1,000 posts from each subreddit, while Section 6 states that a 5% random test set (about 10,585 samples) is taken from a Kaggle collection of SuicideWatch and CasualConversation posts. The paper never states that the test posts are excluded from the 1,000-post reference computations or from the CasualConversation negative prototype. If the test posts overlap with the reference set, then a SuicideWatch post is compared against a centroid that includes its own source population, and the reported recall of 0.95 in Table 4 may reflect self-similarity rather than any diagnostic signal. The authors must document and enforce a strict disjoint split, ideally at the user level, and report results on that split.
  2. [Section 6.1, positive/negative label definitions] The method is described as unsupervised, but the positive and negative label sets are manually defined from subreddit membership, and the prototypes are centroids of those labeled sets. The "Combined" rule is an OR-ensemble over three similarity metrics that is explicitly chosen to maximize recall, so the comparison with the BERT/RoBERTa k-means systems in Table 4 is not at a like-for-like operating point. Moreover, the positive label set includes seven disorder subreddits, which makes high recall easy to obtain by construction: any post whose nearest prototype is one of those seven is labeled positive. The 0.95 recall therefore cannot be interpreted as evidence that the emotion markers outperform the stated baselines.
  3. [Section 4, Table 2] The claim of "superior emotional accuracy" is based on a single run of RACLETTE (0.59) compared with accuracy values taken directly from other papers, with no error bars and no statement about whether those values use the same evaluation protocol (prompt-level vs. conversation-level, exact test split, model selection). This makes the comparison unverifiable. Please provide variance estimates over multiple seeds and, if possible, re-run the baselines under the identical protocol, or report all numbers with explicit protocol caveats.
  4. [Sections 5–6 and 8] The reference "disorder embeddings" are derived from self-selected subreddit communities rather than clinically assessed populations. The paper itself acknowledges in Section 8 that self-reported information and social-media expression are noisy proxies for mental disorders. As a result, the Section 6 experiment demonstrates, at most, that emotion profiles can distinguish two subreddit communities; it does not demonstrate discrimination between clinical conditions. The abstract's conclusion that the method provides "a novel approach to preliminary screening" goes beyond what the data can support. A revision should either add a clinical or validated-label evaluation, or carefully re-frame the contribution as a study of subreddit-community discrimination.
minor comments (7)
  1. [Abstract] The word "intrinsecaly" is a typo and should be "intrinsically".
  2. [Figure 1 caption] The caption contains "coversation", which should be "conversation".
  3. [Table 3] Several KL divergence values are reported as infinite (anxiety, lonely, healthAnxiety); please explain how zero probabilities are handled, or use a smoothed divergence so that all values are finite and comparable.
  4. [Section 4 / Appendix D.1] The conversation-level evaluation seems to append the target response, not the model's predicted response, to the context during evaluation; this is stated implicitly in Appendix D.1 but should be stated explicitly in the main text because it affects the interpretation of the conversation-level accuracy gain.
  5. [Appendix D.1] The text says "establishing 'exited' as the prominent emotion"; this should be "excited".
  6. [Figure 4(A)] t-SNE is stochastic; please report the perplexity, learning rate, and random seed for reproducibility.
  7. [Tables 2 and 4] The use of green shading to indicate better performance is not accessible in grayscale print; please add boldface or symbols to mark the best values.

Circularity Check

1 steps flagged · score 6.0 of 10

Screening evaluation reduces to nearest-centroid self-matching: reference profiles and test posts come from the same subreddits with no documented split, so Table 4's 0.95 recall may reflect self-similarity, not predictive screening.

  1. fitted input called prediction [Section 5 (reference embeddings) and Section 6 (suicide-risk evaluation, Table 4)]
    "embeddings based on emotions obtained by processing 1,000 posts from each subreddit ... We used the two subreddits CasualConversation and SuicideWatch ... This dataset is part of a larger collection available on Kaggle ... We select 5% of the initial samples at random as a test set ... The final embedding aggregates these emotions across all sentences in the post, following a similar approach as used in Section 5 to extract reference embeddings."

    The reference 'suicide' prototype is the normalized sum of RACLETTE emotion distributions over 1,000 r/suicidewatch posts (Section 5), and the positive test set is a random 5% sample from a SuicideWatch/CasualConversation collection (Section 6). The paper never states that the 1,000 reference posts are excluded from this test pool. As described, a positive prediction is 'nearest prototype to suicide embedding,' i.e., nearest to the average emotional profile of the very subreddit the test post came from; the same issue applies to the CasualConversation negative control. Table 4's recall therefore can be high from self-similarity alone, with no independent screening signal demonstrated.

full rationale

The RACLETTE emotion-classification and empathetic-response evaluation (Section 4, Tables 1-2) is a normal external benchmark on the Empathetic Dialogues test set; those results are not circular, and the literature comparisons are appropriate. The self-citations in the paper (Crestani et al. 2022; Sekulic et al. 2021, 2024; Cena et al. 2023) are contextual and not load-bearing. The circularity concern is concentrated in Section 6: the suicide-risk experiment reuses the same subreddit populations to build the reference emotion embeddings and to draw the test set, without any documented exclusion of test posts from the reference computation. Under the protocol as written, the reported screening prediction can be satisfied by comparing each post to a centroid of its own source population, which is a fitted input renamed as a screening prediction. This makes the central screening claim partially circular, although the conversational-model contribution stands independently. Score 6 reflects partial circularity in the screening claim rather than in the whole paper.

Assumptions & free parameters 6 free parameters · 5 assumptions · 2 invented entities

The central claim depends on the assumption that emotional histograms computed by a model trained on everyday MTurk dialogues can serve as clinical markers. The method has several free choices (positive and negative label sets, sampling counts, subreddit sample sizes) and no external clinical validation. The suicide-risk evaluation is circular if the reference embeddings and test posts come from the same subreddit pool.

free parameters (6)
  • positive_label_set = suicide, depression, bpd, bipolar, ptsd, addiction, schizophrenia
    The set of disorder embeddings treated as positive markers for suicide risk is chosen manually based on literature, not learned or validated.
  • negative_label_set = normal (DailyDialog), uniform distribution, CasualConversation
    The control embeddings include a synthetic uniform distribution and a subreddit-based control; the choice affects the decision boundary.
  • emotion_samples_per_prompt = 10
    Each prompt's emotion distribution is estimated from 10 independent top-k samples; this is a hyperparameter.
  • top_k_sampling = 10
    Sampling pool restricted to top-10 tokens; affects the diversity of predicted emotions.
  • posts_per_subreddit = 1000
    Reference embeddings use 1,000 posts per subreddit, as shown in the figures.
  • test_split_fraction = 5%
    Random 5% of the Kaggle samples used as test set; the selection criteria for the remaining reference data are not specified.
assumptions (5)
  • domain assumption Subreddit membership is a valid proxy for clinically diagnosed mental disorders
    The paper uses r/suicidewatch, r/depression, and others as labels for suicide risk without clinical verification; it acknowledges this limitation in Section 8.
  • domain assumption The 32 Empathetic Dialogues emotion categories are sufficient to characterize mental-health-relevant emotional states
    Emotional profiles are histograms over these 32 classes; no evidence is given that this label space captures clinical affect.
  • domain assumption Averaging sampled emotion predictions over posts yields stable, meaningful disorder-level profiles
    The method sums and normalizes 10 samples per phrase; stability across samples or subreddit draws is not reported.
  • domain assumption DailyDialog represents a non-clinical control population
    Used as the normal embedding; no screening of the dataset for mental-health conditions is described.
  • domain assumption Causal language-model fine-tuning on the 3-turn format lets one model act as both classifier and generator
    The model is trained to predict the emotion token before the response; the paper relies on autoregressive conditioning.
invented entities (2)
  • Emotion embedding (emotional profile)
    purpose: Interpretable marker for mental health screening
    A normalized histogram of sampled emotion tokens is presented as a novel representation of mental state. No external validation links it to clinical outcomes; the only evidence is proximity to histograms of Reddit posts labeled by subreddit.
  • Reference disorder embeddings
    purpose: Class prototypes for suicide-risk classification
    Averaged histograms over 1,000 subreddit posts are treated as characteristic patterns of each disorder, without clinical ground truth.

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Pith. "Pith review of The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support." pith.science (2026). https://pith.science/paper/IXM3FSE2

@misc{pith2026241220068,
  author       = {Pith},
  title        = {Pith review of: The Emotional Spectrum of LLMs: Leveraging Empathy and Emotion-Based Markers for Mental Health Support},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IXM3FSE2}},
  note         = {Machine review of arXiv:2412.20068}
}
read the original abstract

The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to state-of-the-art benchmarks in both understanding users' emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.

Figures

Figures reproduced from arXiv: 2412.20068 by the authors.

Figure 1
Figure 1. An example of how our 3-turns coversation [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. A visual explanation of how the emotional [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of the main steps of RACLETTE pipeline. and Choi, 2017; Li et al., 2017). Reddit Mental Health Dataset (Low et al., 2020): a collection of posts from specific Reddit forums (also called subreddits, [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: (A) 2-Dimensional representation of mental disorders distribution after applying t-SNE dimensionality reduction. (B) Sorted emotion embedding of depression. (C.) Sorted emotion embedding of DailyDialog. Emotion KL JS CS suicide 0.000 0.000 1.000 depression 0.062 0.124 …
Figure 5
Figure 5. Figure 5: Emotional embeddings of subreddits related to high risk of suicide. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]
Figure 6
Figure 6. Figure 6: Emotional embeddings of different subreddits. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

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Works this paper leans on

52 extracted references · 32 canonical work pages

  1. [1]

    Alaa A Abd-Alrazaq, Mohannad Alajlani, Nashva Ali, Kerstin Denecke, Bridgette M Bewick, and Mowafa Househ. 2021. Perceptions and opinions of patients about mental health chatbots: scoping review. Journal of medical Internet research, 23(1):e17828

    2021

  2. [2]

    Carla Agurto, Guillermo Cecchi, Sarah King, Elif K Eyigoz, Muhammad A Parvaz, Nelly Alia-Klein, and Rita Z Goldstein. 2023. Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior. bioRxiv

    work page 2023

  3. [3]

    Carl Allen and Timothy Hospedales. 2019. Analogies explained: Towards understanding word embeddings. In International Conference on Machine Learning, pages 223--231. PMLR

    work page 2019

  4. [4]

    Aaron T Beck, Robert A Steer, and Gregory Brown. 1996. Beck depression inventory--ii. Psychological assessment

    work page 1996

  5. [5]

    Blei, Andrew Y

    David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res., 3(null):993–1022

    2003

  6. [6]

    Louise Br dvik. 2018. Suicide risk and mental disorders

    work page 2018

  7. [7]

    Federica Cena, Luca Console, Marta Micheli, and Fabiana Vernero. 2023. Combining genetic algorithms and temporal constraint satisfaction for recommending personalized tourist itineraries. In International Conference of the Italian Association for Artificial Intelligence, pages 441--452. Springer

    work page 2023

  8. [8]

    Xinhao Chen, Chong Yang, Man Lan, Li Cai, Yang Chen, Tu Hu, Xinlin Zhuang, and Aimin Zhou. 2024. Cause-aware empathetic response generation via chain-of-thought fine-tuning. arXiv preprint arXiv:2408.11599

    arXiv 2024

Show all 52 references
  1. [9]

    Itsugun Cho, Dongyang Wang, Ryota Takahashi, and Hiroaki Saito. 2022. A personalized dialogue generator with implicit user persona detection. arXiv preprint arXiv:2204.07372

    2022 arXiv

  2. [10]

    Cheryl M Corcoran, Vijay A Mittal, Carrie E Bearden, Raquel E Gur, Kasia Hitczenko, Zarina Bilgrami, Aleksandar Savic, Guillermo A Cecchi, and Phillip Wolff. 2020. Language as a biomarker for psychosis: a natural language processing approach. Schizophrenia research, 226:158--166

    2020

  3. [11]

    Hugo Corona Hern \'a ndez, Cheryl Corcoran, Am \'e lie M Achim, Janna N De Boer, Tessel Boerma, Sanne G Brederoo, Guillermo A Cecchi, Silvia Ciampelli, Brita Elvev g, Riccardo Fusaroli, et al. 2023. Natural language processing markers for psychosis and other psychiatric disord...

    2023

  4. [12]

    Fabio Crestani, David E Losada, and Javier Parapar. 2022. Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the ERisk Project, volume 1018. Springer Nature

    2022

  5. [13]

    Jean Decety. 2005. Perspective taking as the royal avenue to empathy. Other minds: How humans bridge the divide between self and others, 143:157

    2005

  6. [14]

    Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. arXiv preprint arXiv:2305.14314

    2023 arXiv

  7. [15]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://arxiv.org/abs/1810.04805 Bert: Pre-training of deep bidirectional transformers for language understanding . Preprint, arXiv:1810.04805

    2019 arXiv

  8. [16]

    Paul Dodemaide, Mark Merolli, Nicole Hill, and Lynette Joubert. 2022. Do social media impact young adult mental health and well-being? a qualitative study. The British Journal of Social Work, 52(8):4664--4683

    2022

  9. [17]

    Bjarke Felbo, Alan Mislove, Anders S gaard, Iyad Rahwan, and Sune Lehmann. 2017. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language...

    2017

  10. [18]

    Jun Gao, Yuhan Liu, Haolin Deng, Wei Wang, Yu Cao, Jiachen Du, and Ruifeng Xu. 2021. Improving empathetic response generation by recognizing emotion cause in conversations. In Findings of the association for computational linguistics: EMNLP 2021, pages 807--819

    2021

  11. [19]

    Giulia Gargano, Elisabetta Caletti, Cinzia Perlini, Nunzio Turtulici, Marcella Bellani, Carolina Bonivento, Marco Garzitto, Francesca Marzia Siri, Chiara Longo, Chiara Bonetto, et al. 2022. Language production impairments in patients with a first episode of psychosis. Plos one...

    2022

  12. [20]

    Ayaan Haque, Viraaj Reddi, and Tyler Giallanza. 2021. Deep learning for suicide and depression identification with unsupervised label correction. In Artificial Neural Networks and Machine Learning--ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratis...

    2021

  13. [21]

    Rui He, Claudio Palominos, Han Zhang, Maria Francisca Alonso-Sánchez, Lena Palaniyappan, and Wolfram Hinzen. 2024. https://doi.org/10.1016/j.psychres.2024.115752 Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational langu...

    2024

  14. [22]

    Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751

    2019 arXiv

  15. [23]

    Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825

    2023 arXiv

  16. [24]

    Sandra A Just, Erik Haegert, Nora Ko r \'a nov \'a , Anna-Lena Br \"o cker, Ivan Nenchev, Jakob Funcke, Andreas Heinz, Felix Bermpohl, Manfred Stede, and Christiane Montag. 2020. Modeling incoherent discourse in non-affective psychosis. Frontiers in Psychiatry, 11:846

    2020

  17. [25]

    Sidney H Kennedy. 2008. Core symptoms of major depressive disorder: relevance to diagnosis and treatment. Dialogues in clinical neuroscience, 10(3):271--277

    2008

  18. [26]

    Filiz Kulacaoglu and Samet Kose. 2018. Borderline personality disorder (bpd): in the midst of vulnerability, chaos, and awe. Brain sciences, 8(11):201

    2018

  19. [27]

    Aziliz Le Glaz, Yannis Haralambous, Deok-Hee Kim-Dufor, Philippe Lenca, Romain Billot, Taylor C Ryan, Jonathan Marsh, Jordan Devylder, Michel Walter, Sofian Berrouiguet, et al. 2021. Machine learning and natural language processing in mental health: systematic review. Journal ...

    2021

  20. [28]

    Joosung Lee. 2022. The emotion is not one-hot encoding: Learning with grayscale label for emotion recognition in conversation. arXiv preprint arXiv:2206.07359

    2022 arXiv

  21. [29]

    Qintong Li, Piji Li, Zhaochun Ren, Pengjie Ren, and Zhumin Chen. 2022. Knowledge bridging for empathetic dialogue generation. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 10993--11001

    2022

  22. [30]

    Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. Dailydialog: A manually labelled multi-turn dialogue dataset. arXiv preprint arXiv:1710.03957

    2017 arXiv

  23. [31]

    Zhaojiang Lin, Peng Xu, Genta Indra Winata, Farhad Bin Siddique, Zihan Liu, Jamin Shin, and Pascale Fung. 2020. Caire: An end-to-end empathetic chatbot. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 13622--13623

    2020

  24. [32]

    Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692

    2019 arXiv

  25. [33]

    Daniel M Low, Laurie Rumker, Tanya Talkar, John Torous, Guillermo Cecchi, and Satrajit S Ghosh. 2020. Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on reddit during covid-19: Observational study. Journal of medical In...

    2020

  26. [34]

    Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, and Pascale Fung. 2019. Personalizing dialogue agents via meta-learning. In Proceedings of the 57th annual meeting of the association for computational linguistics, pages 5454--5459

    2019

  27. [35]

    Robert R Morris, Kareem Kouddous, Rohan Kshirsagar, and Stephen M Schueller. 2018. Towards an artificially empathic conversational agent for mental health applications: system design and user perceptions. Journal of medical Internet research, 20(6):e10148

    2018

  28. [36]

    John A Naslund, Ameya Bondre, John Torous, and Kelly A Aschbrenner. 2020. Social media and mental health: benefits, risks, and opportunities for research and practice. Journal of technology in behavioral science, 5:245--257

    2020

  29. [37]

    Aengus OConghaile and Lynn E DeLisi. 2015. Distinguishing schizophrenia from posttraumatic stress disorder with psychosis. Current opinion in psychiatry, 28(3):249--255

    2015

  30. [38]

    Hannah Rashkin, Eric Michael Smith, Margaret Li, and Y-Lan Boureau. 2018. Towards empathetic open-domain conversation models: A new benchmark and dataset. arXiv preprint arXiv:1811.00207

    2018 arXiv

  31. [39]

    Ivan Sekuli \' c , Mohammad Aliannejadi, and Fabio Crestani. 2021. User engagement prediction for clarification in search. In Advances in Information Retrieval - 43rd European Conference on IR Research, ECIR 2021 , pages 619--633

    2021

  32. [40]

    Ivan Sekuli \'c , Silvia Terragni, Victor Guimar \ a es, Nghia Khau, Bruna Guedes, Modestas Filipavicius, Andre Ferreira Manso, and Roland Mathis. 2024. Reliable llm-based user simulator for task-oriented dialogue systems. In Proceedings of the 1st Workshop on Simulating Conve...

    2024

  33. [41]

    Judy Hanwen Shen and Frank Rudzicz. 2017. Detecting anxiety through reddit. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology—From Linguistic Signal to Clinical Reality, pages 58--65

    2017

  34. [42]

    Hyeongshin Shin, Hwaran Lee, and Kyomin Kim. 2019. Happybot: Generating empathetic dialogue responses by improving user experience look-ahead. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6789--6794

    2019

  35. [43]

    Yoojin Song, Sang Jin Rhee, Hyunju Lee, Min Ji Kim, Daun Shin, and Yong Min Ahn. 2020. Comparison of suicide risk by mental illness: a retrospective review of 14-year electronic medical records. Journal of Korean medical science, 35(47)

    2020

  36. [44]

    Alvin Subakti, Hendri Murfi, and Nora Hariadi. 2022. The performance of bert as data representation of text clustering. Journal of big Data, 9(1):1--21

    2022

  37. [45]

    Xiaofei Sun, Xiaoya Li, Jiwei Li, Fei Wu, Shangwei Guo, Tianwei Zhang, and Guoyin Wang. 2023. Text classification via large language models. arXiv preprint arXiv:2305.08377

    2023 arXiv

  38. [46]

    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R \'e mi Louf, Morgan Funtowicz, et al. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771

    2019 arXiv

  39. [47]

    Jiachen Xu, Xiaodong Wu, Zhe Wang, Yidong Feng, and Ping Wang. 2018. Emotion detection from text via ensemble classification using word embeddings. ACM Transactions on Internet Technology (TOIT), 18(4):1--17

    2018

  40. [48]

    Sayyed M Zahiri and Jinho D Choi. 2017. Emotion detection on tv show transcripts with sequence-based convolutional neural networks. arXiv preprint arXiv:1708.04299

    2017 arXiv

  41. [49]

    Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675

    2019 arXiv

  42. [50]

    Ming Zhou, Minlie Huang, and Xiaoyan Zhu. 2020. Emotion-aware chatbots: A survey of recent advances and future research directions. Information Fusion, 59:103--127

    2020

  43. [51]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  44. [52]

    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 gl...

Pith tools

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