Towards Interpretable Mental Health Analysis with Large Language Models
Authors: Kailai Yang
Shaoxiong Ji
Tianlin Zhang
Qianqian Xie
Ziyan Kuang
Sophia Ananiadou
Venue: EMNLP 2023 Main (2023)
Keywords: mental health analysis
large language models
prompt engineering
explainability

Abstract: The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis.
However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance of exploring LLMs for explainability.
To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning ability of LLMs on 11 datasets across 5 tasks. We explore the effects of different prompting strategies with unsupervised and distantly supervised emotional information. 
Based on these prompts, we explore LLMs for interpretable mental health analysis by instructing them to generate explanations for each of their decisions. 
We convey strict human evaluations to assess the quality of the generated explanations, leading to a novel dataset with 163 human-assessed explanations.
We benchmark existing automatic evaluation metrics on this dataset to guide future related works. 
According to the results, ChatGPT shows strong in-context learning ability but still has a significant gap with advanced task-specific methods. 
Careful prompt engineering with emotional cues and expert-written few-shot examples can also effectively improve performance on mental health analysis. 
In addition, ChatGPT generates explanations that approach human performance, showing its great potential in explainable mental health analysis.

Towards Interpretable Mental Health Analysis with Large Language Models
Kailai Yang
Shaoxiong Ji +2 Tianlin Zhang Qianqian Xie t1
Ziyan Kuang Sophia Ananiadou 1.3
The University of Manchester UUiivesstty of Helsinki
Artificial Intelligence Research Center, AIST 4 Jiangxi Normal University
(kailai fail yang, tianlin. zhangg@oggaag manchester ac. uk
shaoxiong jingggjieheee ji@helsinkkii fi; [xqq. sincere, plumj 225 (2g5)emm.ll... com sophia hre.iaaaanaaccccddeenneeeeeter.. ac..kk
Abstract
health care (Zhang et al., 2022a). NLP researchers have devoted much effort to automatic mental health analysis, with current mainstream meth- ods leveraging the Pre-trained Language Models (PLMs) (Yang et al., 2022; Abed-Essfahani et al., 2019). Most recently Large Language Models (LLMs) (Brown et al., 2020; Ouyang et al., 2022), especially ChatGPT and GPT-4 (OpenAI, 2023), have exhibited strong general language processing ability (Wei et al., 2022; Luo al., 2023; Yuan et al., 2023). In mental health analysis. Lamich- hane (2023) evaluated ChatGPT on stress, depres- sion, and suicide detection and glimpsed its strong language understanding ability to mental health- related texts. Amin et al. (2023) compared the zero- shot performance of ChatGPT on suicide and de- pression detection with previous fine-tuning-base methods.
The latest large language models (LL.Ms) such as ChatGPT, exhibit strong capabilities in au- tomated mental health analysis. However, existing relevant studies bear several limita- tions, including inadequate evaluations, lack of prompting strategies, and ignorance of explor- ing LLMs for explainability. To bridge these gaps, we comprehensively evaluate the mental health analysis and emotional reasoning abil- ity of LLMs on Il datasets across 5 tasks. We explore the effects of different prompting strate- gies with unsupervised and distantly super- vised emotional information. Based on these prompts, we explore I.LMs for interpretable mental health analysis by instructing them to generate explanations
each their deci- sions. We convey striet human evaluations to assess the quality of the generated explanations, leading to a novel dataset with 163 human- assessed explanations'. : We benchmark existing automatic evaluation metrics on this dataset to guide future related works. According the results, ChatGPT shows strong in-context learn- ing ability but still has a significant gap with ad- vanced task-specific methods. Careful prompt engineering with emotional cues and expert- written few-shot examples can also effectively improve performance mental health analysis. addition, ChatGPT generates explanations that approach human performance, showing its great potential in explainable mental health analysis.
Though previous works depict a promising fu- ture for a new LLM-based paradigm in mental health analysis, several issues remain unresolved. Firstly, mental health condition detection is safe- critical task requiring careful evaluation and high transparency for any predictions (Zhang et al.. 2022a), while these works simply tested few binary mental health condition detection tasks and lack the explainability on detection results. More- over, other important mental health analysis tasks, such as the cause/factor detection of mental health conditions (Mauriello et al., 2021; Garg et al., 2022), were ignored. Secondly, previous works mostly use simple prompts to detect mental health conditions directly. These vanilla methods ig- nore useful information, especially emotional cues, which are widely utilized for mental health analysis in previous works (Zhang et al., 2023). We believe requires comprehensive exploration and evalu- ation the ability and explainability of LLMss on mental health analysis, including mental health de- tection, emotional reasoning, and cause detection
1 Introduction
This paper contains examples and de- scriptions that are depressive in nature.
WARNING:
Mental health conditions such as depression and suicidal ideation seriously challenge global
Equal contribution, listed alphabetically. Corresponding author. Qianqian now affiliated with Yale University. The work was done when she was at The University Manchester.
'The data is released at https://gittub... com/ StevekGYang/Mentallle
Pnttps://openail httt:s////opeaai.coo/mo///eeeeee

---

Consider this Emorion-enhanced Prompt
(d) Evalvatoons
scored on juck off what comedown gning be like from belrgjoccedd jacked up or adrenaline for days stroight. I have not jelt tired whotsorver during this time.."
Post References
Score
Port
Consider the emotion expressed from the post, then answer this question: the
suffer very esplain your
You
Tarpet Response
Golden
Prompting
Ratiinss
Responses
Ratings
Vos, the poster lieely suffer from very severe stress.
The emotional language used the post indicates high lovel fear and ansivrty, with the phrase "scared as fuck" conveying extremn distress. The fact that they have been jacked on adronaline for five straight suggetts they have been in highly stressful period
Human
Dvellation
Amsotator
Armotator
Rumncy realustee he calerence roadibility the cxplasation.
Armotator
Balability messures how trantsothinnss of the ganersted explanation... Completeress measure how well the explanation cover relexent axpecti...
Flsanay Retibilltyy 3
situation
the combination emotional physical tymptoms suggests poster experiencing severe stress.
Complnteneen:
Ovorall
DO
Figure 1:
The pipeline obtaining and evaluating the LLM--gnnrated explanations for mental health analysis. In LLM responses, red, green, and blue words marked as relevant clues for rating fluency, reliability, and completeness in human evaluations.
of mental health conditions. Therefore, we raise the following three research questions (RQ):
to generate natural language explanations for each of its results on mental health analysis. To assess the quality of LLMMs-generated explanations, we perform human evaluations by following a strict annotation protocol designed by domain experts, and thus create the novel dataset with 163 human- assessed explanations of posts from L.Mss, aimed at facilitating the investigating of explainable men- tal health analysis methods and automatic evalu- ation metrics. We benchmark numerous existing automatic evaluation metrics on the corpus to guide future research on automatically evaluating explain- able mental health analysis. We conclude our find- ings as follows:
RQ 1: How well can LLMs perform in gen- cralized mental health analysis and emotional reasoning with zero- prro-shoo/few-hhho settings?
RQ 2: How do different prompting strategies and emotional cues impact the mental health analysis ability of ChatGPT?
RQ 3: How well can ChatGPT generate ex- planations for its decisions on mental health analysis?
To respond to these research questions, we first conduct a preliminary study of how LLMs perform on mental health analysis and emotional reasoning. We evaluate four LLMs with varying model sizes including ChatGPT, InstructGPT-3 (Ouyang et al.. 2022), LLaMA-13B, and LLaMA-7B (Touvron al., 2023), on datasets across tasks including binary/mulli-claas mental health condition detec- tion, cause/factor detection of mental health con- ditions, emotion recognition in conversations, and causal emotion entailment. We then delve into the effectiveness of different prompting strategies on mental health analysis, including zero-shot prompt- ing, Chain-of- (CoT) prompting (Kojima al., 2022), emotion-enhanced prompting, and few-shot emotion-enhancel prompting. Finally, we explore how LLMs perform for interpretable mental health analysis, where we instruct two rep- resentative LLMs: ChatGPT and InstructGPT-3,
1) Overall Performance. ChatGPT achieves the best performance among all examined LLMs, al- though it still significantly underperforms advanced supervised methods, highlighting the challenges of emotion-related subjective tasks.
2) Prompting Strategies. While simple CoT trigger sentence is ineffective for mental health analysis, ChatGPT with unsupervised emotion- enhanced CoT prompts achieves the best perfor- mance, showing the importance of prompt engi- neering in leveraging emotional cues for mental health analysis. Few-shot learning from expert- written examples also significantly improves model performance.
3) Explainability. ChatGPT can generate
aeproaching-human explanations for its classifica- tions, indicating its potential to enhance the trans-

---

parency of mental health analysis. Current best automatic evaluation metries can moderately cor- relate with human evaluations, indicating the need for developing customized automatic evaluation methods explainable mental health analysis.
2) InstructGPT-3. InstructGPT-3 continually trains GPT-3 (Brown al., 2020) with instruction tuning (Ouyang et al., 2022), which enables the model to solve tasks in a question-answerss for- mat. We utilize curie-instruct-beta version (13 billion parameters) in our experiments.
4) Limitations. Although its great potential, ChatGPT bears limitations inaccurate reasoning and unstable predictions caused by its excessive sensitivity to minor alterations in prompts, inspir- ing future directions on improving ChatGPT and prompts. Unstable prediction problems can be mit- igated by few-shot learning.
3) ChatGPT. ChatGPT (gpt-3.5 (gpt-3.5-turbo) is trained based on the 175 billion parameters version of In- structGPT (Ouyang al., 2022) and continually optimized through reinforcement learning from hu- man feedback (RLHF) (Stiennon et al., 2020).
2.2 In-context Learning as Explainable Mental Health Analyzer
Our contributions can be summarized as follows: 1) We evaluate four representative LLMs mental health analysis. We investigate the effectiveness of prompting strategies including CoT, emotion- enhanced prompts, and few-shot learning for men- tal health analysis, 3) We explore LLMs for explain- able mental health analysis, and conduct human and automatic evaluations on -engrrrree ex- planations, 4) We create the first evaluation dataset with LLMs-generated explanations rigorously as- sessed by domain experts, for examining and de- veloping of automatic evaluation metrics, 5) We analyze the potential and limitations of LLMs and different prompting strategies for mental health analysis.
In-context learning (Brown et al., 2020) elicits the powerful ability of LLMs given the information provided in the context without explicit updates of model parameters. We instruct LLMs with task- specific instructions to trigger their ability the zero-shot analyzer for different mental health anal- ysis tasks. We systematically explore three differ- ent prompting strategies for mental health analy- sis, i.e., straightforward zero-shot prompting with natural language query, emotion-cnhannee Chain- of- Thought (CoT) (Wei et al., 2022), and distantly supervised emotion-enhanced instructions. The straightforward zero-shot prompting guides four LLMs by asking for classification result from their responses. For example, for binary mental health condition detection, we design the following prompt:
2 Methodology
This section introduces the details of evaluated LLMs and different prompting strategies for im- proving LLMs' efficiency and explainability in mental health analysis. Due to the page limits, all evaluations, experiments, and analyses on emo- tional reasoning are presented in Appendix B. We also perform human evaluations on the quality of Len-ggeeetted explanations and benchmark isting automatic evaluation metrics on the human evaluation results, where an example is shown in Figure 1.
Post: "[Post]". Consider this post to answer the question: Is the poster likely to suffer from very severe [Condition]? Only return Yes or No.
Emotion-enhanced Prompts Moreover, we de- sign three emotion-enhancee prompting strategies to better instruct ChatGPT to conduct explainable mental health analysis: 1) Emotion-enhanced CoT prompting. We perform emotion infusion by designing unsupervised emotion-enhanced zero- shot CoT prompts, where the emotion-related part inspires the LLM to concentrate on the emotional clues from the post, and the CoT part guides the LLM to generate step-by-step explanations for its decision. This improves the explainability of LLMs' performance. For example, for the binary detection task, modify the zero-shot prompt follows:
2.1 Large Language Models
We benchmark the following powerful LLMs for the zero-shot mental health analysis:
1) LLAMA-7B/13B. LLaMA (Touvron et al., 2023) is set of open-source LLMs developed by Meta AI, which are generatively pre-trained on entirely publicly available datasets. We test the zero-shot mental health analysis tasks on L.LaMA models with 7 billion (LLaMA-7B) and 13 billion (LLaMA-13B) -3B) parameters.
Post: "[Post]". Consider the emotions ex- pressed from this post to answer the question:

---

Is the poster likely to suffer from very severe [Condition]? Only return Yes or No, then ex- plain your reasoning step by step.
recognizing one potential cause of mental health condition from multiple causes. More details about the prompt design and examples of the prompts are presented in Appendix C.2.
2) Supervised emotion-enhanced prompting. In addition, we propose distantly supervised emo- tion fusion method by using sentiment and emo- tion lexicons. We utilize the VADER (Hutto and Gilbert, 2014) and NRC EmoLex (Mohammad and Turney, 2010, 2013) lexicons to assign a sen- timent/emotion score to each post and convert the score to sentiment/emmttion labels. Then we design smotion-enhancee prompts by adding the sentiment/emotioo labels to the proper positions of the zero-shot prompt. 3) Few-shot Emotion- enhanced Prompts. We further evaluate the im- pact of few-shot examples on emotion-enhanced prompts. We invite domain experts (Ph.D. students majoring in quantitative psychology) to write one response example for each label class within a test set, where all responses consist of a prediction and an explanation describing the rationale behind the decision. We then include these examples in the emotion-enhanced prompts to enable in in-context learning of the models. For example, for the binary detection task, modify the original emotion- enhanced prompt to combine N expert-written ex- planations in a unified manner:
2.3 Evaluation for Explainability
Human Evaluation We examine the quality of the generated explanations by two representative LLMs: ChatGPT and InstructGPT-3, with human evaluations on the binary mental health condi- tions detection task. We utilize ChatGPT and InstructGPT-3 to simultaneously generate expla- nations for the same posts with the same emotion- enhanced CoT prompts. The annotation protocol is developed through collaborative efforts with 2 do- main experts (Ph.D. students majoring in quantita- tive psychology) and considerations of human eval- uation criteria for other text generation tasks (Wal- lace et al., 2021; De Young et al., 2021). Specifi- cally, four key aspects are assessed: 1) Fluency: the coherence and readability of the explanation. 2) Reliability: the trustworthiness of the gener- ated explanations to support the prediction results. 3) Completeness: how well the generated expla- nations cover all relevant aspects of the original post. 4) Overall: the general effectiveness of the generated explanation.
You will be presented with a post. Consider the emotions expressed in this post to identsfy whether the poster suffers from [condition]. Only return Yes No, then explain your reasoning step by step. Here are N examples: Post: [example 1]
Each aspect is divided into four standards rating from to 3. Higher ratings reflect more satisfactory performance and 3 denotes approaching human performance. Each LLM-generated explanation is assigned a score by 3 annotators for each corre- sponding aspect, followed by the examination of I domain expert. All annotators are PhD students with high fluency in English. We evaluate 121 posts that are correctly classified by both ChatGPT (ChatGPTerue)) and InstructGPT-3 enable fair comparisons. 42 posts that are incorrectly classi- fied by ChatGPT (ChatGPT are also collected for еттor analysis and examination of the automatic evaluation metrics. We will release the annotated corpus for facilitating future research. Details of the criteria are described in Appendix E.
Response: [response 1]
Post: [example N]
Response: [response N]
Post: [Post]
Response:
Task-specific Instructions We conduct broad tests of LLMs'' mental health analysis ability on the following three tasks: binary mental health con- dition detection, multi-class mental health condi- tion detection, and cause/factor detection of mental health conditions. Binary mental health condition detection is modeled the mental health condition, such as depression and stress from a post. contrast, multi-class de- tection identifies one label from multiple mental health conditions. Cause/factor detection aims at
Automatic Evaluation Though human evalua- tions provide an accurate and comprehensive view of the generated explanations' quality, they quire huge human efforts, making it hard to be extended large-scale datasets. Therefore, we ex- plore utilizing automatic evaluation metrics, orig- inally developed for generation tasks such as text summarization, to benchmark the evaluation on
yes/no classification of

---

our annotated corpus. We rely on the ability of the evaluation models score the fluency, relia- bility, and completeness of the explanations. We select the following widely utilized metrics to au- tomatically evaluate LLM-ggeneraed explanations: BLEU (Papineni et al., 2002), ROUGE-I, ROUGE- 2. ROUGE-L (Lin, 2004), GPT3-Score (Fu et al., 2023) (davinci-003), and BART-Score (Yuan et al., 2021). We also use the BERT-sccre-basee (Zhang al., 2020) methods with different PLMs, in- cluding the domain-specific PLMs MenalBERT and MentalRoBERTa (Ji et al., 2022b), except for BERT and RoBERTa.
Evaluation for Explainability For the human evaluation results, we evaluate the quality of the annotations by calculating the inter-evaluator agree- ment: Fleiss' Kappa statistics (Fleiss et al., 2013) for each aspect. Any annotations with a major- ity vote are considered as reaching an agreement. To compare the automatic evaluation methods, we also compute Pearson's ''s correlation coefficients be- tween the automatic evaluation results and the hu- man evaluation results, where higher values reflect more linear correlations between the two sets of data.
4 Results and Analysis
3 Experimental Settings
We conduct all LLaMA experiments a single Nvidia Tesla A100 GPU with 80GB of memory. InstructGPT-3 and ChatGPT results are obtained via the OpenAl API. Each prompt fed indepen- dently to avoid the effects of dialogue history.
Mental Health Analysis
Firstly, we introduce the benchmark datasets, baseline models, and au- tomatic evaluation metrics for the classification results of mental health analysis.
4.1 Mental Health Analysis
Datasets. For binary mental health condi- tion detection, we select two depression detec- tion datasets Depression Reddit (DR) (Pirina and çöltekin, 2018), CLPsych15 (Coppersmith al., 2015), and another stress detection dataset Dread- dit (Turcan and McKeown, 2019). For multi-class mental health condition detection, we utilize the dataset T-SID (Ji et al., 2022a). For cause/factor detection of mental health conditions, we use a stress cause detection dataset called SAD (Mau- riello et al., 2021) and depressiion/uiicide cause detection dataset CAMS (Garg et al., 2022). More details of these datasets are presented in Table 8 in the appendix.
The experimental results of mental health analy- sis are presented in Table 1. We first compare the zero-shot results of LLMs to gain straight view of their potential in mental health analysis. then an- alyze ChatGPT's performance with other prompts enhanced by emotional information.
Zero-shot Prompting. In the comparison of LLMs, ChatGPT significantly outperforms LLaMA-7B/13B and InstructGPT-3 all datasets. LLaMA-7B2s displays random-gus gondoo-geessing perfor- mance on multi-class detection (T-SID) and cause detection (SAD, CAMS), showing its inability to perform these more complex tasks. With an expanded model size, LLaMA-13B achieves no better performance than LLaMA-7B. Though trained with instruction tuning, InstructGPT-32s still does not improve performance, possibly because the model size limits the LLM's learning ability. Compared with supervised methods, ChatGPTZs significantly outperforms traditional Eight-weighter neural network-based methods such as CNN and GRU on binary detection and cause/factor detection, showing its potential in cause analysis for mental health-related texts. However, ChatGPTZs struggles to achieve compa- rable performance fine-tuning methods such MentalBERT and MentalRoBERTa. Particularly, ChatGPT zs achieves much worse performance than all baselines on T-SID. We notice that T-SID collects mostly short posts from Twitter with many
Baseline Models. We select the following base- line models: CNN (Kim, 2014), GRU (Cho et al., 2014), BiLSTM Att (Zhou et al., 2016), fast- Text (Joulin et al., 2017), BERT/RoBERTa (De- vlin et al., 2019; Liu et al., 2019), and Mental- BERT/MeenalRRBERRTS (Ji et al., 2022b). Details about these baseline models are in Appendix D.2.
Metrics. We evaluate the model performance using the recall and weighted-F1 scores as the eval- uation metric for all mental health datasets. Due to imbalanced classes in some datasets such as DR, CLPsych15, and T-SID, following previous methods. In addition, it cru- cial minimize false negatives, which refers to cases where the model fails identify individuals with mental disorders. Therefore, we also report the recall scores.
weighted-F1 scores

---

DR
CLPsych15
Dreaddit
T-SID
SAD
CAMS
Model
Rec.
F1
Rec.
F1
Rec.
F1
Rec.
F]
Rec.
F1
Rec.
FI
Supervised Methods
CNN
80.54
79.78
51.67
40.28
65.31
64.99
71.88
71.77
39.71
38.45
36.26
34.63
GRU
61.72
63.13
50.00
46.76
55.52
54.92
67.50
67.35
35.91
34.79
34.19
29.33
Bi
79.56
79.41
51.33
39.20
63.22
62.88
66.04
65.77
37.23
38.50
34.98
29.49
Text
83.99
83.94
58,00
56.48
66,99
66.92
69.17
69.09
38.98
38.32
40.10
34.92
BERT
91.13
90.90
64.67
62.75
78.46
78.26
88,44
88.51
62.77
62.72
40.26
34.92
RoBERTa
95.07
95.11
67.67
66.07
80.56
80.56
88.75
88.76
66.86
67.53
41.18
36.54
MentalBERT
94,58
94.62
64.67
62.63
80.28
80.04
88.65
88
67 45
67.34
45.69
39.73
MentalRoBERTa
94.33
94.23
70.33
69.71
81.82
81.76
88.96
89.01
68.61
68.44
50.48
47.62
Zero-shot
LLM-baaee Methods
LLuMA-7Bze
63.55
58.91
57.0
56.26
54.83
53.51
23.04
25.55
10.53
11.04
13.92
16.34
LLaMA-133gg
67.24
54.07
50.0
39.29
47.83
36.28
23.04
25.27
12.57
13.2
13.12
14.64
InstrucGGPT-329
58.87
58. 66
50.33
49.86
50.07
49.88
27.66
26.27
12.70
9.36
10.70
12.23
ChatGPTzs
82.76
82.41
60.33
56.31
72.72
71.79
39.79
33.30
55.91
54.05
32.43
33.85
Emotton-enhanced
CoT M---baseed Methods
ChatGPTv
79.51
78.01
59.20
56.34
74.23
73.99
40.04
33.38
52.49
50.29
28.45
29.00
ChatGPTy
80.00
78.86
58.19
55.50
70.87
70.21
39.00
32.02
52.92
51.38
26.88
27.22
CharGPTy T_emo
79.51
78.41
58.19
53.87
73.25
73.08
39.00
32.25
54.82
52.57
35.20
35.11
ChatGPTeur
82.72
82.9
56.19
50.47
70,97
70.87
37.66
32.89
55.18
52.92
39.19
38.76
ChatGPTeot TooT emoo
83.17
83.10
61.41
58.24
75.07
74.83
34.76
27.71
56.68
43.11
42.29
ChatGPTew7 CTT emoo FS
85.73
84.22
63.93
61.63
77.80
75.38
49.03
43.95
63.56
48.75
45.99
Table 1: Test results on the mental health analysis tasks. ChatGPyy ChatGPT and ChatGPT N_emo Lemo denote the emotion-enhanced prompting methods with VADER sentiments, NRC Emol.ex sentiments, and NRC Emoo _ex emotions. ChatGPTcor and CharGPTcoT_eeem denote the zero-shot and emotion-enhanced CoT methods on the expert-written emo, FS combines expert- written few-shot examples the emotion-enhanced
corresponding task. ChatGPT CoT _oT_emo prompt. The results of baseline methods are referenced from (Ji et al., 2022b).
usernames, hashtags, and slang words. The huge gap between the posts and ChatGPT's training data can make zero-shot detection difficult (Kocoú et al.. 2023). Moreover, although the zero-shot CoT prompting is proven to be effective on most NLP tasks (Zhong et al., 2023; Wei et al., 2022; Kojima et al., 2022), ChatGPT has comparable performance with ChaGGPTss. This illustrates that the simple CoT trigger sentence is not effective in mental health analysis. Overall, ChatGPT significantly outperforms other LLMs, and exhibited some generalized ability for mental health analysis. However, it still underperforms fine-tuning- based methods, leaving huge gap in further exploring LLMs' mental health analysis ability.
expressed in the posts. Therefore, we incorporate fine- uneeggrined emotion labels from NRC EmoLex into the zero-shot prompt. The results show that __emo
ChatGPT Lemo outperforms ChatGPTN __sen on most datasets, especially on CAMS (a 7.89% im- provement). However, ChatGPT NoUGPTN__mme N_emo still un- derperforms ChatGPT most datasets, pos- sibly because texicon-based emotion labels still not accurate in representing multiple emo- tions that co-exist post, especially in datasets with rich content, such as CLPsych15 and DR. Therefore, we explore the more flexible unsuper- vised emotion-enhanced prompts with CoT. As result, ChaiGPT _uT__emo .emo outperforms all other zero-shot methods most datasets, which proves that emotion-enhanced CoT prompting is effeective for mental health analysis. Finally, with few-shot expert- written examples, ChatGPT _e______FFF_ F5 sig- nificantly outperforms all zero-shot methods on all datasets, especially in complex-task datascts: t-sid 16.24% improvement, SAD 6.88% improvement, and CAMS 3.7% improvement (very approaching state-of ofte--of-the-att supervised method). These encour- aging results show that in-context learning is ef- fective in calibrating LLM''s decision boundaries for complex and subjective tasks mental health analysis. We provide case studies in Appendix F.I.
surprisingly find that worse
Emotion-enhanced Prompting. We further test ChatGPT with emotion-enhanced prompts on all datasets. Firstly, with the sentiment information from the lexicon VADER and NRC EmoLee, we notice that ChatGPTy and ChatGPT sen perform worse than ChatGPTzs most datasets, showing that these prompts are not effective in enhancing model performance. possible reason that the coarse-grained sentiment classifications based on the two lexicons cannot describe complex emotions

---

Model
Sample Num.
Avg. Token Num.
Agreement
Fluency
Reliability
Completeness
Overall
ChatGPT
163
237
96.6%
0.94
0.53
0.39
0.36
ChatGT
121
203
95 9%
0.95
0,58
0.40
0.38
ChaiGiT Fatee
42
335
98.8%
0.91
0.34
0.38
0.28
InstructGPT33
121
203
89.9%
0.55
0.58
0.63
0.62
Table Fleiss' Kappa and other statistics of human ovaluations on ChatGPT and InstructGPT-3 results. "Sample Num." and "Avg Token Num." denote the sample numbers and average token numbers of the posts. "Agreement" denotes the percentages of results that reached a final agreement with majority vote from the three assignments.
Placcc
Compltorees
Ovooll
20
2/0
7.0
L.b
CransP Taber
InStUCtiPT3
ChatsPlaw
Insrudtor
Cratsellw
Chatt
Intructort
(hattPP
Chande Taler
I1STUCTIPT
Figure 2: Box plots of the aggregated human evaluation scores for each aspect. Orange lines denote the median scores and green lines denote the average scores.
4.2 Evaluation Results for Explainability
InstructGPT-3 achieves much worse performance in fluency with a median score and an average score less than 1.0. These results prove ChatGPT a fluent explanation generator for mental health analysis. In reliability, ChatGPT trae achieves a me- dian score of 3 and over 2.7 in average score, show- ing ChatGPT as trustworthy reasoner in support- ing its classifications. Only few of InstructGPT- 3's explanations generate moderately reliable infor- mation while most of them are unreliable. For com- pleteness, ChatGPT trwe obtains over 2.5 scores on average, indicating that ChatGPT can cover most of the relevant content in the posts to explain its classifications, while InstructGP1-3 ignores key as- pects by obtaining less than 0.5 on average. Over- all, ChatGPT has average score of over 2.5, proving that ChatGPT can generate human-level ex- planations for correct classifications regarding flu- ency, reliability, and completeness and significantly outperforms previous LLMs such as InstructGPT-33 More cases are Appendix F.2.
Human Evaluation In the above subsection, we have shown that emotion-enhancee CoT prompts can enhance ChatGPT's zero-shot performance in mental health analysis. Moreover, it prompt LLMs to provide an explanation of their step-by- step reasoning for each response. This can signifi- cantly improve the explainability of the predictions, which a key advantage compared with most previ- ous black-box methods. In this subsection, we pro- vide carefully designed human evaluations to gain clear view of LLMs' (ChatGPT and InstructGPT-33) explainability their detection results.
The Fleiss' Kappa results and agreement per- centages are presented in Table 2. We aggregate each score by averaging assignments from three annotators, and the distributions are presented in Figure 2. Firstly, the three annotators reach high agreements on evaluation. Over 95% of ChatGPT evaluations and 89.9% of InstructGPT-3 results reach agreement. According to the widely utilized interpretation criterion all Fleiss' Kappa statis- tics achieve least fair agreement (>0.21) and 10 out 16 results reach at least moderate agreement (20.41). These outcomes prove the quality of the human annotations.
Automatic Evaluation The automatic evaluation results on the ChatGPT explanations are presented in Table 3. In ChatGPT, BART-Score achieves the highest correlation scores all aspects, show- ing its potential in performing human-like evalua- tions for explainable mental health analysis. Specif- ically, BART-Score outperforms all BERT-Score- based methods, which shows that generative mod- els can be more beneficial in evaluating natural
As shown in Figure 2. ChatGPT true almost achieves average of 3.0 in fluency and stably maintains outstanding performance, while
Phttps: http::///ewwwwiiiiiiii.. wta.iia.oo//vkk//lessst// https:///w..kkaaaaaa.rrgg//s/sss kappa

---

ChatGPT
ChatGPT
Metric
Value
Fluency
Reliability
Compleseness
Overall
Value
Fluency
Reliability
Completeness
Overall
BLEU-1
0.026
0.050
0.091
0.268
0.205
0.005
-0.002
0.344
0.405
0.352
ROUGE-1
0.226
0.210
0.258
0.400
0.351
0.177
-0.010
0.256
0.539
0.315
ROUGEE2
0.068
8.105
0.153
0.281
0.247
0.057
0,028
0.349
0.515
ROUGE-L
0.140
0.196
0.249
0.398
0.361
0.099
0,072
0,282
0.505
0.352
GPT3 Score
-1.903
0.350
0.139
0.266
0.277
-1.998
-0.043
0.034
0.389
0.129
BART-Score
-4.046
0.590
0.404
0.446
0.428
-4.49
-0.076
-0.114
0.257
-0.044
BERT Ssure-buseet Methods
BERT
0.495
0.080
0.104
0.224
0.172
00470
0.133
0.192
0.321
0.180
RoBERTa 0.834
0.116
0.139
0.304
0.250
0.825
0,095
0.206
0.306
0.219
MenialDERT 0.552
0.207
0.214
0.297
0.263
0.534
0.148
0.240
0.255
0.214
McnialRoBERTa
0.763
0.217
0.177
0.286
0.260
0.752
0.129
0.354
0.427
0.373
Table 3: Pearson's 's correlation coefficients between human evaluation and existing automatic evaluation results on ChatGPT explanations. Best values are highlighted in bold.
language texts. Unexpectedly, BART-Score also significantly outperforms GPT3-Score, zero-shot evaluation method based the powerful LLM GPT-3, in all aspects. These results show that task-specific pre-training is important to trigger the language model's ability for the evaluation tasks. BART-score also fine-tuned on text summariza- tion and paraphrasing tasks, which are crucial to assess relevance and coherence, the two important factors for providing satisfactory evaluations. How- ever, in ChatGPT false: BART-Score becomes less competitive. BERT-Score achieves the best per- formances fluency and reliability, and ROUGE methods outperform others on completeness and overall. A possible reason that BERT-based methods can better distinguish false semanties in the explanations than BART. With longer posts in ChatGPT false (Table 2), the matching-ban method ROUGE can more accurately detect the uncovered aspects in the posts, and completeness play more important role in determining overall In performance in falsely classified samples. methods, MentalBERT and MeRRal significantly outperform BERT and RoBERTa in most aspects, showing that pre- training on large-scale mental health texts can also benefit automatic evaluation performances of lan- guage models. More experiments on InstructGPT-3 results presented in Appendix G.
ChatGPT
DR
CLPsych15
Dreaddit
Zero-shot prompts
any
82,41
56.31
53.10
some
74.44
56.59
50.62
very severe
78.65
47.55
71.79
Variance
10.6
17.62
89.29
Few-shot prompts
any some
84.74
57.24
65.62
86.9
61.63
62.0
very severe
84.22
55.19
75.38
Variance 1.34
7.21
31.93
Table Change of ChatGPT's weighted-FI perfor-
mance with different adjectives with zero- sero-shot/few-shho prompting strategies.
the prompt, especially on binary mental health con- dition detection. While keywords describing the tasks are easy to control, some other words such as adjectives, are hard to optimize. For example, we replace the adjective describing the mental health condition with different degrees in the zero-shot prompt for binary mental health detection:
...Is the poster likely suffer from [Adjective of Degree] [Condition]?...
where the adjective (marked red) is replaced with one keyword from (any, some, very severe), and the results on three binary detection datasets are shown Table 4. As shown, ChatGPTZs shows very unstable performance on all three datasets, with high variance of 10.6 on DR, 17.62 on CLP- sysch15, 15, and 89.29 on Dreaddit. There are also no global optimal adjectives the best adjective changes with the datasets. This sensitivity makes ChatGPT's performance very unstable even with slightly different prompts. We believe this prob- lem due to the subjective nature of mental health conditions. The human annotations only answer
4.3
Error Analysis
We further analyze some typical errors during our experiments to inspire future efforts of improving ChatGPT and emotion-enhanced prompts for men-
tal health analysis.
Unstable Predictions. We notice that ChatGPT's performance on mental health analysis can vary drastically with the change of a few keywords in

---

Yes/No for each post, which makes the human cri- teria of predictions hard to learn for ChatGPT in zero-shot setting. To alleviate this problem, we fur- ther explore the effectiveness of few-shot prompts in these settings, where the same expert-written few-shot examples in Sec. 2.2 are included in the zero-shot prompts. As the results in Table 4 show, with few-shot prompts, ChatGPT achieves vari- ance of 1.34 on DR, 7.21 CLPsych15, and 31.93 on Dreaddit, which are all significantly lower than those of zero-shot prompts. These results prove that expert-written examples can stabilize Chat- GPT's predictions, because they can provide ac- curate references for the subjective mental health detection and cause detection tasks. The few-shot solution is also efficient as it instructs the model in in-context learning manner, which doesn't 't require high-cost model fine-tuning.
mental health-related instruction-tuning datasets. We leave LLM-finetunng as future work. More cases are provided in Appendix F.3.
Conclusion
In this work, we comprchensively studied LLMs pero-shot/few-shol mental health analysis and the impact of different emotion-enhanced prompts. We explored the potential of LLMs in explainable mental health analysis, by explaining their predic- tions via CoT prompting. We developed a reli- able annotation protocol for human evaluations of LLM-generated explanations and benchmarked ex- isting automatic evaluation metrics on the human annotations. Experiments demonstrated that men- tal health analysis is still challenging for LLMs, but emotional information with proper prompt en- gineering can better trigger their ability. Human evaluation results showed that ChatGPT can gen- erate human-level explanations for its decisions, and current automatic evaluation metrics need fur- ther improvement to properly evaluate explainable mental health analysis. ChatGPT also bears limita- tions, including unstable predictions and inaccurate reasoning.
Inaccurate Reasoning. Though ChatGPT is proven capable of generating explanations for its classifications, there are still many cases showing its inaccurate reasoning leading to incorrect results. To investigate the contributing factors behind these mistakes, we further compare the human evalua- tion results between the correctly and incorrectly classified results ChatGPTTr true and ChatGPT false- The results are presented in Figure 2. As shown, ChatGPT false still achieves comparable fluency scores ChatGPT but performs worse on both completeness and reliability. For completeness, the average score of ChatGPTfale fales drops below 2.0. We also notice that the average token num- ber of ChatGPT Jalse reaches 335 (Table 2), which exceeds ThatGPTrrue by over 130 tokens. These results indicate that ChatGPT struggles to cover all relevant aspects of long-context posts. For relia- bility, more than half of ChatGPTfaies results give unreliable inconsistent explanations (below 1.0), possibly due the lack of mental health-related knowledge. few ChatGPT false samples provide mostly reliable reasoning (above 2.0) but miss key information due to the lack of completeness. Over- all, the mistakes of ChatGPT are mainly caused by ignorance of relevant information in long posts and unreliable reasoning process. Therefore, fu- ture works should improve ChatGPT's long-context modeling ability and introduce more mental health- related knowledge to benefit its performance. Inac- curate reasoning also reflects a lack of alignment between LLMs and mental health analysis tasks. A possible solution is to tume the LLMs with
In future work, we will explore domain-specinn fine-tuning for LLMs to alleviate inaccurate reason- ing problems. We will also extend the interpretable settings with LLMs to other research domains.
A Acknowledgements
This work is supported by the computational shared facility the University of Manchester and the University of Manchester President's Doctoral Scholar award. This work is supported by the project JPNP20006 from New Energy and In- dustrial Technology Development Organization (NEDO). Shaoxiong Ji is supported by the Eu- ropean Union's Horizon 2020 research and inno- vation program (agreement No 771113) and the EU's Horizon Europe research and innovation pro- gram under grant agreement No 101070350 and UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee Igrant number 10052546), also thanks the CSC IT Center for Science, Finland for computational resources.
Limitations
Unexpected Responses. Though ChatGPT makes predictions in most its responses as

---

requested by the prompts, there are few cases where it refuses to make classification. There are two main reasons: 1) the lack of evidence from the post to make a prediction; 2) the post contains content that violates the content policy of OpenAl'. For example, ChatGPT can respond: "As an AI language model, 1 cannot accurately diagnose mental illnesses or predict what may have caused them in this post." In our experiments, directly exclude these responses because they are very rare, but future efforts are needed to alleviate these problems.
public from Reddit and Twitter. The SMS-like SAD dataset (Mauriello et al., 2021) has been re- leased publicly on GitHub by the authors. All examples presented our paper have been para- phrased and obfuscated using the moderate disguis- ing scheme (Bruckman, 2002) to avoid misuse. We also do not use the user profile on social media, identify the users or interact with them. Our study aims to use social media as an early source of infor- mation to assist researchers or clinical practition- ers in detecting mental health conditions for non- clinical use. The model predictions cannot replace psychiatric diagnoses. addition, we recognize that some mental disorders are subjective (Keilp et al., 2012), and the interpretation of our analysis may differ (Puschman, 2017) because we do not understand the actual intentions of the posts.
Limitations of Lexicons. The motivation for us- ing sentiment and emotion lexicons to provide additional context with distant supervision for the prompts, which, however, have several limitations. The two lexicons, VADER (Hutto and Gilbert, 2014) and NRC EmoLex (Mohammad and Tur- ney, 2010, 2013) we used were developed a decade ago with human annotation using social media data, is inevitable that they suffer from annotation bias in the sentiment/emotion scores and only reflect the language used when they were developed. The Internet language evolves rapidly, and our exper- iments also use some recent datasets such as T- SID (Ji et al., 2022a) and CAMS (Garg et al., 2022). Besides, these lexicons have limited vocabulan laries. Manual rules to aggregate sentence-level sentiment and emotions could underspecified. Prompt en- gincering with other advanced resources with extra emotional information can be explored in future work. We also see the limitation of the dataset. Ji (2022) showed that the sentiment distribution has no significant difference in the binary case of T-SID dataset. Although the mentiment-enhancee prompt with VADER gains slightly better perfor- mance than other prompts on T-SID dataset, we cannot clearly explain if the choice of lexicon con- tributes to the improvement due to the black-box nature of ChatGPT.
References
Pegah Abed-Esfahani, Derek Howard, Marta Maslej, Sejal Patel, Vamika Mann, Sarah Goegan, and Leon French. 2019. Transfer learning for depression: Early detection and severity prediction from social media postings. In CLEF (Working Notes).
Mostafa Amin, Erik Cambria, and Bjöm W Schuller. 2023. Will affective computing emerge from foun- dation models and general ai? a first evaluation chatgpt. arXiv preprint arXiv:2303.03186
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wen- liang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al. 2023. A multi- task, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity, arXiv preprint 2rxiv:230..00022
James RA Benoit. 2023. Chatgpt for clinical vignette generation, revision, and evaluation. medRxiv, pages 2023 02.
Adrian Benton, Glen Coppersmith, and Mark Dredze. 2017. Ethical research protocols for social media health research. In Proceedings of the first ACL work- shop on ethics in natural language processing, pages 94 102.
Ethical Considerations
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam. Girish Sastry, Amanda Askell, al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
Although the datasets used are anonymously posted, our study adheres to striet privacy proto- cols (Benton et al., 2017; Nicholas et al., 2020) and minimizes privacy impact as much as pos- sible, as social media datasets can reveal poster thoughts and may contain sensitive personal infor- mation. We use social posts that are manifestly
Amy Bruckman. 2002. Studying the amateur artist: A perspective disguising data collected in human subjects research on the internet. Ethics and Infor- mation Technology, 4:217-231.

---

Carlos Busso, Murtaza Bulut, Chi-Chun Lee. Abe Kazemzadeh, Emily Mower, Samuel Kim, Jean- nette N. Chang, Sungbok Lee, and Shrikanth S. Narayanan. 2008. IEMOCAP: interactive emotional dyadic motion capture database. Lang. Resour. Eval- uation, 42(4):335-355.
Sooji Han, Rui Mao, and Erik Cambria. 2022. Hierar- chical attention network for explainable depression detection on Twitter aided by metaphor concept map- pings. In Proceedings of the 29th International Con- Jerence on Computational Linguistics, pages 94-104, Gyeongju, Republic of Korea. International Commit- tee Computational Linguistics.
Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng. Jie Zhou, Tao Gui, Qi Zhang, and Xuanjing Huang. 2023. How robust is gpt-3.5 to pre- decessors? comprehensive study on language un- derstanding tasks. arXiv preprint arXiv:2303.0022
Clayton Hutto and Eric Gilbert. 2014. Vader: parsi- monious base model for sentiment analysis of social media text. In Procee Proceedings of the international AAAI conference on web and social media, volume 8, pages 216-225.
Kyunghyun Cho, Bart Merrienboer, Caglar Gulcchre, Fethi Bougares, Holger Schwenk, and Yoshua Ben- gio. 2014. Learning phrase representations using encoder-decoder for statistical machine translation. In EMNLP.
Shaoxiong Ji. 2022. Towards intention understanding in suicidal risk assessment with natural language pro- cessing. In Findings of EMNLP, pages 4028-4038.
Shaoxiong Ji, Xue Li, Zi Huang, and Erik Cambria. 2022a. Suicidal ideation and mental disorder detec- tion with attentive relation networks. Neural Com- puting and Applications, 34: 10309-10319.
Glen Coppersmith, Mark Dredze, Craig Harman, Kristy Hollingshead, and Margaret Mitchell. 2015. Clpsych 2015 shared task: Depression and ptsd on twitter. In Proceedings of the 2nd Workshop on CLPsych, pages 31-39.
Shaoxiong Ji, Tianlin Zhang. Luna Ansari, Jie Fu, Prayag Tiwari, and Erik Cambria. 2022b. Mental- bert: Publicly available pretrained language mod- els for mental healthcare. In Proceedings of the Thirteenth Language Resources and Evaluation Con- ference, pages 7184-7190, European Language Re- sources Association (ELRA).
Jacob Devlin. Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume I (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Zhengping Jiang, Sarah Ita Levitan, Jonathan Zomick, and Julia Hirschberg. 2020. Detection of mental health from Reddit via deep contextualized repre- sentations. In Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis, pages 147-156, Online. Association for Computational Linguistics.
Jay Young Iz Beltagy, Madeleine van Zuylen, Bai- ley Kuehl, and I Lucy Wang. 2021. Ms* Multi- document summarization of medical studies. In Pro- ceedings of the 2021 Conference on Empirical Meth- ods in Natural Language Processing, pages 7494- 7513.
Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Xing Wang, and Zhaopeng Tu. 2023. Is chatgpt a good translator? preliminary study. arXiv preprint 23X.023001.0875..
Joseph L. Fleiss, Bruce Levin, and Myunghee Cho Paik. 2013. Statistical methods for rates and proportions. john wiley& & sons.
Armand Joulin, Édouard Grave, Piotr Bojanowski, and Tomáš Mikolov. 2017. Bag of tricks for efficient text classification. In Proceedings of the 15th Confer- ence of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. pages 427-431.
Jinlan Fu, See-Kiong Ng. Zhengbao Jiang, and Pengfei Liu. 2023. Gptscore: Evaluate as you desire. arXiv preprint arXiv:2302.044 04666.
Tianyu Gao, Xingeheng Yao, and Danqi Chen. 2021. SimCSE: Simple contrastive learning of sentence em- beddings. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing. pages 6894-6910, Online and Punta Cana, Do- minican Republic. Association for Computational Linguistics.
John G Keilp, Michael F Grunehaum, Marianne Gorlyn, Simone LeBlane, Ainsley K Burke, Hanga Galfalvy. Maria A Oquendo, and John Mann. 2012. Suici- dal idcation and the subjective aspects of depression. Journal of affective disorders, 140(1):5--81
Muskan Garg, Chandni Saxena. Sriparna Saha, Veena Krishman, Ruchi Joshi, and Vijay Mago. 2022. CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6387-6396.
Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1746-1751, Doha, Qatar, Association for Computational Linguis- tics.

---

Jan Kocoñ, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydlo, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz. Janz, Kamil Kanclerz, al. 2023. Chatgpt: Jack of all trades, master of none. arXiv preprint arXiv:2302. 10724.
Matthew Louis Mauriello, Thierry Lincoln. Grace Hon, Dorien Simon, Dan Jurafsky, and Pablo Paredes. 2021. Sad: A stress annotated dataset for recog- nizing everyday stressors in sms-like conversational systems. In Extended abstracts of the 2021 CHI conference on human hunanfactors in computing systems, pages 1-7.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2022. Large lan- guage models are zero-shot reasoners. In Advances Neural Information Processing Systems.
Saif Mohammad and Peter Tumney. 2010. Emotions evoked by common words and phrases: Using Me- chanical Turk to create an emotion lexicon. In Pro- ceedings of the NAACI. HIT 2010 Workshop on Com- putational Approaches to Analysis and Generation of Emotion in Text, pages 26-344 34, Los Angeles, CA. Association for Computational Linguisties.
Bishal Lamichhane. 2023, Evaluation of chatgpt for nlp-based mental health applications. arXiv preprint arXiv:2303.11 15727.
Jiangnan Li, Fandong Meng, Zheng Lin, Rui Liu, Peng Fu, Yanan Cao, Weiping Wang, and Jie Zhou. 2022. Neutral utterances are also causes: Enhancing con- versational causal emotion entailment with social commonsense knowledge. In Proceedings of the Thisty-First International Joint Conference on Artifi- cial Intelligence, IJCAI 2022. Vienna. Austria, 23-29 July 2022. pages 4209 4215. ijeai.org.
Saif M. Mohammad and Peter D. Turney. 2013. Crowd- sourcing a word-emotion association lexicon, Com- putational Intelligence, 39(3):336-466
Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, and Arman Cohan. 2022. Improving the generalizability of depression detection by leverag- ing clinical questionnaires. In Proceedings of the 60th Annual Meeting of the Association for Compu- tational Linguistics (Volume I: Long Papers), pages 8446-8459, Dublin, Ireland. Association for Compu- tational Linguistics.
Yanran Li. Hui Su. Xiaoyu Shen, Wenjie Li. Ziqiang Cao, and Shuzi Niu. 2017. DailyDialog: manually labelled multi-turn dialogue dataset. In Proceedings of the Eighth International Joint Conference on Nat- ural Language Processing (Volume I: Long Pupers), pages 986-995, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Jennifer Nicholas, Sandersan Onie, and Mark E Larsen. 2020. Ethics and privacy in social media research for mental health. Current psychiatry reports, 12:1-7.
Chenhao Lin, Pengwei Hu, Hui Su, Shaochun Li, Jing Mei, Jie Zhou, and Henry Leung. 2020. Sensemood: depression detection on social media. In Proceedings of the 2020 international conference on multimedia retrieval, pages 407-411.
OpenAL. 2023. Gpt-4 technical report.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, al. 2022. Training language models to follow instruc- tions with human feedback. Advances in Neural Information Processing Systems, 35:27730-27744,
Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out, pages 74-81.
Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalu- ation of machine translation. In Proceedings of the 40th annual meeting of the Association for Computa- tional Linguistics, pages 311-318.
Yinhan Liu, Myle Ott. Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining ap- proach. arXiv preprint arXiv: /907. 1
Inna Pirina and Çağn Çöltekin. 2018. Identifying de- pression on Reddit: The effect of training data. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 9-12.
Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023. Chatgpt as factual inconsistency evaluator for abstractive text summarization. arXiv preprint 2rX3v:2303.1562
Soujanya Poria, Erik Cambria, Devamanyu Hazarika, Navonil Majumder. Amir Zadeh, and Louis- Morency. 2017. Context-dependent sentiment anal- ysis in user-generated videos. In Proceedings of the 55th Annual Meeting of the Association for Compu- tational Linguistics (Volume I: Long Papers), pages 873-883, Vancouver, Canada. Association for Com- putational Linguistics.
Navonil Majumder, Soujanya Poria, Devamanyu Haz- arika, Rada Mihalcea, Alexander F. Gelhukh, and Erik Cambria. 2019. Dialoguernn: An attentive RNN for emotion detection conversations. In The Thirty-Third AAAI Conference on Artificial Intelli- gence, AAAI 2019, The Thirty-Firs Innovative Ap- plications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Arrificiall Intelligence, EAAI 2019, Hon- olulu, Hawaii, USA, January 27- February 1. 2019, pages 68 6818-6688 6825. AAAI Press.
Louis-Philippe Philippe
Soujanya Poria, Devamanyu Hazarika, Navonil Ma- jumder, Gautam Naik, Erik Cambria, and Rada Mi- halcea. 2019a. MELD: A multimodal multi-party

---

dataset for emotion recognition in conversations. In Proceedings of the 57th Annual Meeting of the As- sociation for Computational Linguistics, pages 527- 536, Florence, Italy, Association for Computational Linguistics.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Fric Hambro, Faisal Azhar, et al. 2023. Llama: Open and effi- cient foundation language models. arXiv preprint arXiv:2302.1 N.t:23302.19777 13971.
Soujanya Poria, Navonil Majumder, Devamanyu Haz- arika, Deepanway Ghosal, Rishabh Bhardwaj, Sam- son Yu Bai Jian, Pengfei Hong. Romila Ghosh, Ab- hinaba Roy, Niyati Chhaya, Alexander F. Gelbukh, and Rada Mihalcea. 2021. Recognizing emotion cause in conversations. Cogn. Comput., 13(5):1317- 1332.
Elsbeth Turcan and Kathleen McKeown. 2019. Dread- dit: A Reddit Dataset for Stress Analysis in Social Media. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 97-107.
Byron C Wallace, Sayantan Saha, Frank Soboczenski, and lain J Marshall. 2021. Generating (factual?) narrative summaries of rets: Experiments with neural multi-document summarization. AMIA Summits on Translational Science Proceedings, 2021:605.
Soujanya Poria, Navonil Majumder, Rada Mihalcea, and Eduard H. Hovy. 2019b. Emotion recognition in conversation: Research challenges, datasets, and recent advances. IEEE Access, 7: 1:100943-100953.
Cornelius Puschman. 2017. Bad judgment, bad ethics? Internet Research Ethics for the Social Age, 95.
Jason Wei, Xuezhi Wang. Dale Schuurmans, Maarten Bosma, Ed Chi, Quoe Le, and Denny Zhou, 2022. Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv:2201. 17903.
Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, and Diyi Yang. 2023. Is chatgpt a general-purpose natural language process- ing task solver? arXiv preprint arXiv:2302.06476.
Penghui Wei, Jiahao Zhao, and Wenji Mao. 2020. Ef- fective inter-clause modeling for end-to-end emotion- cause pair extraction. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 3171-3181 Online. Association for Computational Linguistics.
Mariana Rodrigues Makiuchi, Tifani Warnita, Kuni- aki Uto. and Koichi Shinoda. 2019. Multimodal fusion of bert-enn and gated enn representations for depression detection. In Proceedings of the 9th In- ternational on Audio/Visual Emotion Challenge and Workshop, pages 55-63.
Yunhe Xie, Kailai Yang. Chengjie Sun, Bingquan Liu, and Zhenzhou Ji. 2021. Knowledge-interactive net- work with sentiment polarity intensity- multi- task learning for emotion recognition in conversa- tions. In Findings of the Association for Computa- tional Linguistics: EMNLP 2021. pages 2879-2889, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Weizhou Shen. Junqing Chen, Xiaojun Quan, and Zhix- ian Xie. 2021a. Dialogxl: All-in-one xlnet for multi- party conversation emotion recognition. In Proceed- ings of the AAAI Conference on Artificial Intelligence, volume 35, pages 13789-13797.
Weizhou Shen, Siyue Wu, Yunyi Yang, and Xiaojun Quan. 2021b. Directed acyclic graph network for conversational emotion recognition. In ACL, pages 1551-1560. Association for Computational Linguis- tics.
Kailai Yang, Tianlin Zhang, Hassan Alhuzali, and Sophia Ananiadou. 2023. Cluster-level contrastive learning for emotion recognition in conversations. JEEE Transactions on Affective Computing, pages 1-12.
Xiaohui Song. Longtao Huang, Hui Xue, and Songlin Hu. 2022. Supervised prototypical contrastive learn- ing for emotion recognition in conversation. In Pro- ceedings of the 2022 Conference on Empirical Meth- ods in Natural Language Processing, pages 5197- 5206, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Kailai Yang, Tianlin Zhang, and Sophia Ananiadou. 2022. A mental state knowledger mnwwlede--waare and con- trastive network for early stress and depression de- tection social media. Information Processing & Management, 5944)10029..
Zhilin Yang. Zihang Dai, Yiming Yang. Jaime Car- bonell, Russ Salakhutdinov, and Quoe V Le. 2019. Xinet: Generalized autoregressive pretraining for lan- guage understanding. Advances in neural informa- tion processing systems, 32.
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020. Learn- ing to summarize with human feedback. Advances in Neural Information Processing Systems, 33:3008- 3021.
Junjie Ye, Xuanting Chen, Nuo Xu, Can Zu, Zekai Shao, Shichun Liu, Yuhan Cui,, Zeyang Zhou, Chao Gong, Yang Shen, et al. 2023. A comprehensive capability analysis of gpt-3 and gpt-3.5 series models. arXiv preprint arXiv:2303. 10420.
Ermal Toto, ML Tlachac, and Elke A Rundensteiner. 2021. Audibert: deep transfer learning multimodal classification framework for depression sereening. In Proceedings of the 30th ACM international con- ference on information & knowledge management, pages 4155-
Chenhan Yuan, Qianqian Xie, and Sophia Ananiadou. 2023. Zero-shot temporal relation extraction with chatgpt. arXiv preprint 2rX4v:2304.05444

---

Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text gener- ation. Advances in Neural Information Processing Systems, 34:27263-21277.
A Related Work
A.1 Mental Health Condition Detection
Most recently, pre-trained language models (PLMs) have been the dominant method for various NLP tasks including mental health detection. Jiang et al. (2020) proposed the attention-based user-level and post-level classification model for mental health de- tection with the contextual representations from BERT as input features. Zhang et al. (2022b) proposed the hierarchical attention network with BERT as the post-encoder for early depression de- tection. Ji et al. (2022h) released two PLMs, Men- talBERT and MentalRoBERTa, for mental health- care, which are trained with corpus from social media. (2022) showcased the intention under- standing capacity of PLMs via the mask prediction task and emphasized the importance of intention understanding in suicidal ideation detection. Dif- ferent from the above black-box models, Han et al. (2022) proposed the explainable depression detec- tion model hierarchical attention network (HAN), which uses the contextual embedding of BERT input features and incorporates the metaphor con- cept mappings (MCMs) as the extra features to improve the interpretability of the model. Nguyen et al. (2022) proposed to improve the generalizabil- ity of BERT based depression model by grounding the detection behavior of the depression detection model to symptoms in PHQ9.
Sayyed M Zahiri and Jinho D Choi. 2017. Emo- tion detection tv show transcripts with sequence- based convolutional neural networks. arXiv preprint arXiv:1708.04299,
Tianlin Zhang, Annika M Schoene, Shaoxiong Ji, and Sophia Ananiadou. 2022a. Natural language process- ing applied mental illness detection: a narrative review. NPJ digital medicine, 5(1):46.
Tianlin Zhang. Kailai Yang. Shaoxiong Ji, and Sophia Ananiadou. 2023. Emotion fusion for mental illness detection from social media: A survey. Information Fusion, 92:231-246.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q- Weinberger, and Yoav Artzi. 2020. Bertscore: Evalu- ating text generation with BERT 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenRe- view.net.
Zhiling Zhang, Siyuan Chen, Mengyue Wu, and Kenny Q Zhu. 2022b. Psychiatric scale guided risky post screening for early detection of depression. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. IJCAI 2022, Vienna, Austria, 23-29 July 2022. pages 5220-5226.
Weixiang Zhao, Yanyan Zhao, Zhuojun Li, and Bing Qin. 2022. Knowledge- bridged causal interaction network for causal emotion entailment. arXiv preprint arXi::2212.029995
Moreover, there are also efforts incorporating multi-modal information, such as voice, video, visual, and text, to improve the performance depression detection. Rodrigues Makiuchi et al. (2019) proposed a multi-modal method for depres- sion detection, which incorporates speech and tex- tual information with gated convolutional neural network (gated CNN) and contextual feature from BERT. Lin al. (2020) proposed the visual-textual multi-modal learning method based on CNN and BERT, for depression detection on social media. Toto et al. (2021) proposed the multi-modal method Audio-Assisted BERT (AudiBERT) for depression classification, which integrates the pre- pre-trained au- dio embedding with text embedding from the bert encoder.
Peixiang Zhong, Di Wang. and Chunyan Miao. 2019. Knowledge-enrichel transformer for emotion de- tection in textual conversations. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Pro- pages 165-176, Hong Kong, China. Association for Computational Lin- guistics.
cessing
Qihuang Zhong, Liang Ding, Juhua Liu, Bo Du, and Dacheng Tao. 2023. Can chatgpt understand too? a comparative study on chatgpt and fine-tuned bert. arXiv preprint MrXiv:2302.10/99
Chunting Zhou, Chonglin Sun, Zhiyuan Liu, and Fran- cis Lau. 2015. A c-Istm neural network for text clas- sification. arXiv preprint arXiv:1511.000
A.2 Large Language Models for Mental Health Analysis
Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi. Bingchen Li. Hongwei Hao, and Bo Xu. 2016. Attention-basee bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers), pages 207-212.
Most recently, many efforts have evaluated the per- formance of LLMs such as ChatGPT and GPT- on various NLP tasks (Bang et al., 2023; Qin

---

al., 2023), such machine translation (Jiao al., 2023), text generation and evaluation (Benoit, 2023; Luo al., 2023), language inference (Zhong et al., 2023). They have inspired efforts explor- ing the ability of LLMs for mental health analy- sis. Lamichhane (2023) evaluated the performance of ChatGPT on three mental health classification tasks, including stress, depression, and suicidality detection, and proved the good potential of Chat- GPT for applications of mental health. Amin et al. (2023) further evaluated the capabílities of Chat- GPT on big-five personality detection, sentiment analysis, and suicide detection. They show Chat- GPT has better performance in sentiment analy- sis, comparable performance in suicide detection, and worse performance in personality detection when compared with RoBeRTa-based and word embedding-basee based supervised methods. There are also works on analyzing the emotional reasoning ability of ChatGPT including (Qin et al., 2023; Zhong et al., 2023; Kocoń et al., 2023; Chen et al., 2023) on the sentiment classification task, where ChatGPT achieves comparable or worse perfor- mance compared with fine-tuning based methods based on PLMs. Ye et al. (2023) compared the per- formance of different LLMs including GPT-3 series (davinci and text-davinci-001) and GPT-3.5 series ccode-davinci-002, dexi-davinci-002, text-davinci- 003, and gpt-3.5-turbo) on the aspect-based sen- timent analysis, where code-davinci-00) has the best performance in the zero-shot setting. However, most of them only cover simple binary sentiment classification tasks or few binary mental health detection tasks, leaving huge gap for comprehen- sively exploring the ability of LLMs on emotion- aware mental health analysis.
terance.
Prompts We perform direct guidance on explor- ing the ability of ChatGPT in both tasks, which designs zero-shot prompts to directly ask for a clas- sification result from the response of ChatGPT. De- tails about the designed prompts are presented in Appendix C.I.
Datasets For ERC, we select four widely uti- lized benchmark datasets: IEMOCAP (Busso et al., 2008), MELD (Poria et al., 2019a), EmoryNLP (Za- hiri and Choi, 2017). DailyDialog (Li et al., 2017). For CEE, we select the dataset RECCON (Poria et al., 2021). More information about these datasets is listed in Table 7 in the appendix.
Baseline Models We compare the performance of ChatGPT with supervised baseline models. For ERC, we select CNN (Kim, 2014), cLSTM (Zhou et al., 2015), CNN+LSTM (Poria et al., 2017), Dia- logueRNN (Majumder et al., 2019), KET (Zhong et al., 2019), BERT-Base (Devlin et al., 2019). RoBERTa-
(Liu et al., 2019), XLNet (Yang et al., 2019), Di (Shen et al., 2021a), KI- Net (Xie al., 2021), SCCL (Yang et al., 2023), and SPCL (Song et al., 2022). For CEE, we select RankCP (Wei et al., 2020), RoBERTa-Basc/Large Basc//Lrge. KEC (Li et al., 2022), and KBCIN (Zhao et al., 2022). Details about these baseline models are in Appendix D.I.
Metries We use the weighted-F1 measure the evaluation metric for IEMOCAP, MELD, and EmoryNLP datasets. Since neutral occupies most of DailyDialog, we use micro-FI for this dataset, and ignore the label nentral when calculating the results as in the previous works (Shen et al., 2021b; Xie et al., 2021; Yang et al., 2023). For RECCON, report the Fl scores of both negative and pos- itive causal pairs and the macro Fl scores whole.
B ChatGPT for Emotional Reasoning
Tasks We evaluate the emotional reasoning abil- ity of ChatGPT in complex scenarios on the follow- ing two widely studied tasks: emotion recognition in conversations (ERC) and causal emotion entail- ment (CEE). ERC aims at recognizing the emotion of each utterance within conversation from fixed emotion category set, which is often modeled as multi-class text classification task (Poria et al., 2019b). Given an utterance with non-neutral emo- tion, CEE aims to identify the casual utterances for this emotion in the previous conversation history. CEE is usually modeled as a binary classification between the candidate utterance and the target ut-
ERC Results The experimental results on ERC task are presented in Table 5. We can see that ChatGPTZs using the zero-shot prompting out- performs traditional supervised methods includ- ing CNN and cLSTM on IEMOCAP, MELD, and EmoryNLP datasets, showing its advantage over light-weighted supervised methods. In ad- dition, ChatGPT zs achieves comparable perfor- mance with CNN+LSTM and DialogueRNN on MELD, and EmoryNLP datasets, indicating that its

---

Model
IEMOCAP
MELD
DailyDialog
EmoryNLP
the emotional causes. However, its performance is still much lower than the advanced supervised methods such as KEC and KBCIN on all metrics, which incorporate effective information such as social commonsense knowledge. Quantitatively, ChatGPT zs still holds 19.86% gap to the SOTA method KBCIN on macro F1 score.
CNN
52.18
55.86
49,34
32.59
cLSTM
34,84
49.72
49.90
26.01
CNN-LSTM
55.87
50.24
DealoguRNN
61.21
$6.27
50.65
31.70
KET
59.56
58.18
53.37
34.39
BERT Zone
61.19
56.21
$3.12
33.15
RoBERTanae
55.67
62.75
55.16
37.0
XLNel
DialogXL
65.94
62.41
$4.95
34.73
Net
66,98
63.24
57.3
SCCL
69.88
65.70
62.51
38.75
In conclusion, the experiments the ERC and CEE tasks show that ChatGPT holds comparable emotional reasoning ability complex contexts with some traditional methods such as CNN and cLSTM, but still strongly underperforms compet- itive task-specific information infusion and fine- tuning methods. This indicates the necessity of future efforts to enhance prompting strategies and leverage external knowledge to better trigger the emotional reasoning ability of ChatGPT. These re- sults also motivate us to design emotion-enhanced prompts aid mental health analysis.
SPCL
09.74
67,25
40.94
ChaiGPTzs
53.35
61.18
43.27
32.64
Table 5: Test results ERC task. ChatGPT denotes the method using the zero-shot prompt. The results of some baseline methods referenced from (Zhong et al., 2019; Song et al., 2022).
generalizability can make up for the lack of task- specific model architectures to some extent. On the MELD dataset, ChatGPTzs achieves 61.18% of weighted-F1 score, which outperforms some strong supervised methods including the fine-tuned BERT Base model (by 4.97%), and the knowledge infusion method KET (by 3.0%). However, the zero-shot performance of ChatGPT is still worse than advanced supervised methods all datasets, and struggles to achieve dominating performance on the emotion-related tasks. This because these tasks are very subjective even to humans, showing the promising future direction of exploring the few- shot prompting and knowledge infusion to improve the performance of ChatGPT in these subjective
C Prompt Engineering
C.1 Emotional Reasoning
The prompt for ERC is designed as follows:
Context: "[Previous Dialogue]". Consider this context to assign one emotion label to this ut- terance "[Target]". Only from this emotion list: [Emotion List]. Only return the assigned word. where the slots marked blue are the required inputs. (Previous Dialogue denotes the previous dialogue history of the target utterance, where each utterance is pre-pended with its speaker, then concatenated in the sequence order. [Target] denotes the target ut- terance, and [Emotion List] denotes the predefined emotion category set of the corresponding dataset. which are listed in Table Similarly, the prompt for CEE task is designed as follows:
tasks.
Model
Neg. F1
Pos. F1
Macro F1
RankCP
97.30
33,00
65.15
RoBERTaR...
88.74
64,28
76.51
RoBERTa
87.89
66.23
77.06
KEC
88.85
66.55
77.70
KBCIN
89.65
68.59
79.12
Context with emotion labels: "[Previous Dia- logue]". Consider this context to answer the question: Did this ulterance "[Query]" caused the [Target Emotion] emotion of the target ut- terance "[Target]"? Only return Yes or No. where [Previous Dialogue] still denotes the dia- logue history with speakers, but each utterance is also post- pest-pended with its emotion label. [Query] is the candidate utterance. [Target] and [Target Emotion] are the target utterance and its emotion label.
ChatGPTzs
67.18
51.35
59.26
Table 6: Test results on the CEE task. Best values: bold. The results of baseline methods referenced from Zhao et al. (2022).
The experimental results on CEE task are presented in Table 6. We can observe that RankCP achieves the highest negative Fl score but has poor performance on the positive F1 score, which is more indicative in evaluating the emotion causal detection ability. ChatGPTzs zs significantly outperforms RankCP on positive Fl score, showing that possesses some level of ability to understand
CEE Results
C.2 Mental Health Analysis
Zero-shot Prompting We probe LLaMA, InstructionGPT-3. and ChatGPT on zero-shot

---

prompts. We design completion-basee prompts on LLaMA, as not trained on instruction tuning. For example, for binary mental health condition detection, we design the following prompt:
score one of the labels: (positive, negative, neutral). NRC EmoLex also contains emotion annotations that were assigned from the following emotion list: anger, anticipation, disgust, fear, joy, sadness, surprise, trust. We regard the emotion category with the maximum emotion score as the emotion label of the input text. Specifically, VADER contains sentiment annotations with a sentiment score between -4 to 4. We utilize the NLTK package* preprocess the input text (e.g.- removing punctuations and singletons), apply the predefined manual rules to obtain word sentiment and aggregate the sentence- sentiment, and return the normalized sentiment score between -1 to 1 as the overall sentiment in the post. We set the polarity threshold to O. For NRC EmoLex, we obtain the word's stem and match the stem with the word in the lexicon when obtaining sentiment and emotion scores. Each word's emotion and sentiment scores are summed up to obtain the sentence- sentiment and emotion. We assume there is only one sentiment or emotion in a given post and assign the post's emotion or sentiment to the categories with the maximum emotion or sentiment scores.
Post: "[Post]". The percentage that the poster is like suffer from very severe [Condition]] is where a predicted percentage of more than 50% is considered as positive, [Post] denotes the target post, | [Condition| denotes the target mental health condition such as depression or stress, and [List]] are the predefined labels presented in Table 8. For InstructionGPT-3 and ChatGPT, we design instruction-based prompts since they are more nat- ural for classification tasks. Specifically, for binary mental health condition detection, we design the following prompt:
Post: "[Post]". Consider this post to answer the question: Is the poster likely suffer from very severe [Condition]? Only return Yes No.
For multi-class mental health detection, we use the following prompt:
Post: "[Post]". Consider this post to assign only mental disorder label to this post from this list: [List]. Only return the assigned label. For cause/factor detection, the prompt is: Post: "[Post]". Consider this post and assign a label that causes its | [Condition]. Only return answers from one of the labels: [List].
We design the supervised emotion-enhanced zero-shot Chain-of-Thought (CoT) prompts by adding the sentiment/emotion labels to the zero- shot prompt. For example, we modify the prompt for multi-class mental health condition detection as follows:
Emotion-enhanced CoT prompting We design instruction- based prompts enhanced CoT prompting strategies on ChatGTT Specifically, for the binary mental health condition detection task, we modify the zero-shot prompt as follows:
only test emotion-
Post: "[Post]". Alice thinks it is [Senti- ment/Emotion). Consider this post to assign only one mental disorder label to this post from this list: [List]. Only return the assigned label. where the green parts are the modifications for distantly supervised emotion infusion, and [Sen- timen//Emotion] denotes the corresponding senti- ment/emotion label. Modifications for other tasks are similar.
Post: "[Post]". Consider the emotions ex- pressed from this post answer the question: Is the poster likely to suffer from very severe [Condition]? Only return Yes or No, then ex- plain your reasoning step by step.
Few-shot emotion-enhanced prompting In this section, we provide some examples of the few- shot emotion-enhanced prompts. The first example comes from DR:
where the green parts are the zero-shot CoT en- hancements that instructs LLM to generate expla- nations, and the red parts are further added on zero- shot CoT prompts to obtain the amotion-enhancee prompts. Similar modifications are performed on the prompt of multi-class detection and cause/factor detection.
You will be presented with a post. Consider the emotions expressed in this post to answer the question: Is the poster likely to suffer from very severe depression? Only return Yes or No, then explain your reasoning step by step. Here are two examples:
Supervised emotion-enhanced prompting Based on the sentiment lexicons, we assign a sentiment score to each post and convert the
hhttps://www.nie org

---

Post: Does everyone else just hurt all the time It's not like physical pain or soreness, it's just this overwhelming feeling of exhaustion and numbness...
sensitive age group...
Post: I got really scared because this happened on the way home. Response:
Response: Yes. Reasoning: The post conveys deep sense of emotional pain, exhaustion, and numbness. The poster describes feeling overwhelmed by even simple tasks and expresses lack of enjoyment or fulfillment from activities that are rypically pleasurable... Post: Hello!: I'm new user so if this post ends up in a weird place/thread, pls bear w me. Has anyone used Ancestry.com lately? They offer two week trial...
D Baseline Models
D.1 Emotion Reasoning
We select the following competitive methods for ERC:
CNN (Kim, 2014) used a single-layer text- CNN to model each utterance. The classifica- tion is performed on utterance-level without contexts.
Response: No. Reasoning: The post does not exhibit strong emotional indicators of very severe depression. It primarily focuses specific concern regarding the safety of...
cLSTM (Zhou et al., 2015) utilized a bi- directional LSTM to encode each utterance and another uni-directional LSTM to model the context.
Post: Iss like that, if you want or not. ME: I have no problem, if it takes longer. But you asked my friend for help and let him wait for one hour and then you haven't 't prepared anything. Thats not what you asked for... Response:
CNN+LSTM (Poria et al., 2017) used a text- CNN to model each utterance, then utilized a uni-directional LSTM to model the context based on the utterance representations.
DialogueRNN (Majumder et al., 2019) used a text-CNN to extract utterance-level features, then use separate GRU to model each partic- ipant's mental states. A global-state GRU is used to model the context.
Here is another example for the mental health cause detection dataset SAD:
You will be presented post that shows the stress of the poster. Assign one label this post only from the following siress causes list: School, Financial problem, Family issues, Social relationships, Work, Health issues, Emotional turmoil, Everyday decision making, Others. You must return the assigned labels, then explain your reasoning step by step. Here are 7 examples:
KET (Zhong et al., 2019) utilized a hierar- chical Transformer to model utterances and context, and infuse word-level commonsense knowledge to enrich the semantics of the con- text.
BERT-Base (Devlin et al., 2019) used the PLM BERT-Base to directly model the con- versation. The utterance representations are used to fine-tune the weights.
Post: i have been wanting to find another job
for some time now
Reasoning: The post explicitly mentions that the poster has been wanting to find another job for some time now. This indicates that they are not satisfied with their current job and are experiencing stress in relation to their work situation.
Response: Work.
(Liu et al., 2019) used a sim- ilar training paradigm to BERT-Basc but with the PLM RoBERTa.
RoBERTa-Base
XLNet (Yang et al., 2019) utilized the PLM XLNet to directly model the conversation. The segment recurrence was expected to model long contexts well.
Post: raising teenage girl can be stressful Response: Family issues. Reasoning: The post specifically mentions the task of raising a teenage girl, falls under the category of family issues. The stress of parenting can be overwhelming, especially when dealing with a
DialogXI. (Shen et al., 2021a) improved the XLNet with the enhanced memory and dialog- aware self-attention mechanism to capture

---

Task
Data Source
Dataset
Conv./Utter.
Emotion Category
ERC
Acted Script
IEMOCAP
31/1,622
neutral, sad, anger, happy, frustrated, excited neutral, sad, anger, disgust. fear, happy, surprise neutral, sad, mad. scared, powerful. peaceful, joyful neutraal, happy, surprise, anger, disgust, fear neutral, happy. surprise, sad, anger, disgust, fear
ERC
TV Show Scripts
MELD
280/2,610
ERC
TV Show Scripts
EmoryNLP
85/1.328
ERC
Human Written Scripts
DailyDialog
1,000/7,740
CEE
Human Written Scripts
RECCON
225/2,405
Table 7: A summary of datasets for emotional reasoning in conversations. Conv, and Utter. denote conversation and utterance numbers. Data statistics are on the test set.
Condition
Platform
Dataset
Post Num.
Labels
Depression
Reddit
DR
406
Yes, No Yes, No Yex, No None, Suicide, Depression, PISD School, Finance, Family, Social Relation. Work, Health, Emotion, Decision, Others None, Bias, Joh, Medication, Relation, Alienation
Depression
Reddit
CLPsych15
300
Stress
Reddit
Dreaddit
715
Suicide
Twitter
T-SID
960
Stress
SMS
SAD
685
Depression/Suicide
Roddit
CAMS
626
Table summary of datasets for mental health tasks. Note we test the zero-shot performance on the test set.
long historical context and dependencies be- tween multiple parties.
RoBERTa-Large are used). Then CEE was modeled binary classification problem for each utterance pair.
KI-Net (Xie et al., 2021) infused both com- monsense and sentiment lexicon knowledge to enhance XLNet. self-matching module was proposed to allow interactions between utterance and knowledge representations.
KEC (Li et al., 2022) utilized the directed acyclic graph networks (DAGs) incorporating social commonsense knowledge (SCK) to im- prove the causal reasoning ability.
SCCL (Yang et al., 2023) proposed supervised cluster-level contrastive learn- ing (SCCL) infuse Valance-Arousal Dominance information. Pre-trained knowl- edge adapters are leveraged incorporate lin- guistic and factual knowledge.
KBCIN (Zhao et al., 2022) proposed the knowledge-bridged causal interaction network (KBCIN) with conversational graph, emo- tional and actional interaction module cap- ture context dependencies of conversations and make emotional cause reasoning.
D.2 Mental Health Analysis
SPCL (Song et al., 2022) used the PLM Sim- CSE (Gao et ul., 2021) as the backbone model with the supervised prototypical contrastive learning (SPCL) loss.
We compare the performance of ChatGPT with that of the following baselines for mental health analysis:
CNN (Kim, 2014) used three channel CNN with filters of 2,3.4 to classify the post.
For CEE task, use the following baseline mod- els:
GRU (Cho et al., 2014) used a two-layer GRU to encode the post.
RankCP (Wei et al., 2020) ranked the clause- pair candidates in the context and utilized a neural network to perform entailment classifi- cation with the context-aware utterance repre- sentations.
BiLSTM Att (Zhou et al., 2016) utilized a bidircctional LSTM with attention mechanism as context encoding layer to capture the con- textual information of posts.
RoBEETaa-Bse/Large (Liu et al., 2019) con- catenated the conversation with the emo- tion label each utterance as input to the PLM RoBERTa (both RoBERTa- Base and
fast (Joulin al., 2017) used an open- source and efficient text classifier based on bag of n- n-grams features.

---

BERT/ROBERIa (Devlin et al., 2019; Liu et al., 2019) utilized the PLMs BERT and RoBERTa to model the post and fine-tuned them for classification.
The domain experts also consult other scales de- scribing depressive symptoms, such the Patient Health Questionnaire (PHQ-9)0. The annotation scheme as follows:
MentalBERK/MMentlRRREEYY. (Ji et al., 2022b) used mental healthcare-related PLMs MentalBERT and MentalRobBERTa to en- code the post, which are fine-tuned for classi- fication.
O: Unreliable information inconsistent in- formation
Somewhat reliable information with some inconsistencies
E Human Evaluation Criteria
Mostly reliable information with few incon- sistencies
Annotators will be given generated explanations from ChatGPT and InstructGPT-3, and the original post as the correct reference. Annotators will need to score and annotate the generated explanations from the following aspects:
Completely reliable information
Completeness Completeness measures how well the generated explanations cover all relevant as- pects of the original post. Annotators should assess whether the explanation provides sufficient context and detail, without omitting important information such as emotional cues from the original post.
Fluency Fluency evaluates the coherence and readability of the explanation. Annotators should assess if generated explanation well-structured, easy to read, and free of grammatical or syntax errors.
0: Incoherent, difficult to read, and contains numerous errors
O: Omits significant information from the orig- inal post
1: Somewhat coherent, but with poor readabil-
1: Partially complete with some omissions
ity and several errors
2: Mostly complete with minor omissions
2: Mostly fluent, easy to read, with few minor eπrors
3: Complete coverage of the original post
3: Completely fluent, coherent, and error-free
Overall Score Overall performance measures the general effectiveness of the generated explanation, taking into account the combined scores for fluency, factuality, reliability, and completeness.
Reliability Reliability measures how trustworthi- ness of the generated explanations to support the detection results. Annotators should assess whether the explanation is based on facts, has misinforma- tion and wrong reasoning according to the given post. Main symptoms check (sorted by eritical- ity):
Poor overall performance, fluency, reliabil- ity, and completeness are all poor (=0)
1: Fair overall performance, fluency is good (>= 2), one or two aspects among reliability, and completeness is fair (<= 1)
Suicide idcation expressions (golden stan- dard).
Self-harm and -uii-
2: Good overall performance, fluency is excel- lent (3), one two aspects among reliability, and completeness is not excellent (<= 2)
Long-term low passion (e.g. loss of interest to previous hobbies).
Loss of appetite and sleep disorders.
3: Excellent overall performance, fluency, reli- ability, and completeness all excellent (=3)
Accompanied by hypersexuality or frigidity.
Phttps://www/ apa or dewpesppisiionnneeeo /itprrrr//prresioooniiiieeeeeee/
guideline/
Other symptoms.

---

F
Case studies
ChatGPT thus makes the prediction of experienc- ing severe stress based on these symptoms.
E.1
Emotion-enhancel Prompts
For multi-class detection, we select case from the T-SID dataset:
To provide a more intuitive view, we present an example of ChatGPTcor Cor_mmo _emo on the mental health condition detection for a post from the Dreaddit dataset:
"Going end my life lol see ya later haha" " Consider the emotions expressed from this post, then assign only one label to this post from this mental disorders list: Depression. Suicide, PTSD, None. You must return the as- signed label, then explain your reasoning step by step.
Prompt: Post:
Prompt: Post: am scared as fuck off what the comedown is going to be like from being jacked up on adrenaline for 5 days straight. I have not felt tired whatsoever during this time..." Consider the emotion expressed from the post, then answer this question: Is the poster likely to suffer from very severe stress? You must return a Yes or No, then explain your reasoning step by step.
Response: Assigned label: Suicide. Reason- ing: The post explicitly mentions the idea of ending one's life, which strongly suggests sui- cidal ideation. The use of humor with "lol" and "haha" may indicate a defense mechanism, such as denial or avoidance. However, given the severity of the content, it is important not to dismiss the potential for suicidal behavior. While depression and PTSD can be risk factors for suicide, they are not necessarily present in this specific post.
Response: Yes, the poster is likely to suffer from very severe stress. The emotional language used in the post indicates a high level of fear and anxiety, with the phrase "scared as fuck" conveying extreme distress. The fact that they have been jacked up on adrenaline for five days straight suggests that they have been in highly stressful situation for an extended period of time... Overall, the combination of emotional and physical symptoms suggests that the poster is experiencing severe stress.
In this case, ChatGPT provides evidence for mak- ing suicide ideation prediction (marked red), such as the "severity of the content" and "explicitly men- tions the idea of ending one's life". It also states rea- sons for excluding other mental health conditions such as depression and PTSD (marked blue), such as "not necessarily present". These two-sided rea- soning steps significantly improve the transparency of ChatGPT's predictions.
where the emotional clues and analysis are marked in red, and factual clues and analysis are marked in blue. As shown, ChatGPT with the prompt "Con- sider the emotion expressed from the post.." " can effectively detect multiple emotions of fear, anxiety, and distress from the post and consider both clues from emotions and physical symptoms to make a final prediction. The emotion-enhanced zero-shot CoT prompt triggers ChatGPT to conduct the com- plex reasoning process with emotional cues, and thus improves the overall performance of mental health analysis.
We provide case from the CAMS dataset to demonstrate how ChatGPT detects the cause/factor for mental health conditions.
Prompt: Post: "Really have been struggling with everything lately, and come into work today and my boss tells my I am longer meeting expectations and fires me. I think the worst part is still being scheduled for three more days..." This post shows the depression of the poster. Consider the emotions expressed from this post and assign one label to this post only from the following depression causes list: Bias or abuse, Jobs and career, Medication, Re- lationship, Alienation, None. You must return the assigned label, then explain your reasoning step by step.
This
F22 Explainability
Firstly, we provide a case for each of the binary/multi-class detection tasks. For binary detec- tion, an example is shown in Appendix. F.1, where ChatGPT explains its reasoning process from the perspectives of emotions and physical symptoms. As shown in ChatGPT's explanation, the emotional language with the phrase "scared as fuck" and the fact "jacked up on adrenaline for 5 days straight" in the post, indicate emotional and physical symp- toms of a high level of fear, anxiety, and distress.
Response: Jobs and career. The post directly mentions the poster's job and their recent fir- ing, which indicates that their job and career are a significant source of stress in their life at

---

the moment. The poster also mentions strug- gling with everything lately, which could po- tentially be related their job and the anxiety it has been causing them. Therefore, the label of "Jobs and career" is the most fitting for this post.
InstradGPP3
Metric
Value
Fluency
Reliability
Compleennss
Overall
BLRII-1
0.003
0.018
0.059
0.095
0.063
ROUGGE-
0.397
0.490
0,433
ROUGEF2
0.029
0.106
0.162
0.214
0.150
ROUGEL
0.274
0.346
0.295
GPT3-Score
-2.58
-0.39
.0.32
-0.54
-0.325
BAKE-Score
0.204
0.229
0.194
BRRT Seore-based Methods
In this case, ChatGPT extracts the direct evidence of "poster's job and their recent firing" from the post (marked blue), support its cause detection of "Jobs and career". In addition, ChatGPT can make inferences about the poster's complaint to po- tentially be related to their job (marked green), and states their relations to the poster's s anxicty emo- tion (marked red). These multi-aspect arguments convincingly support the final decision.
0.400
0.512
0,483
0.512
0.481
RoBEKTa
0.81
0.313
0.288
0.359
0.315
MentalRERR
0.404
0.435
0.402
MeRtaRRoBERT.
0.727
0444
0.373
0.391
0.573
Table 9: Pearson's 's correlation coefficients between hu- man evaluation and existing automatic evaluation results on InstructGPT-3 explanations. Best values are high- lighted in bold.
results show that BERT-Scort method based BERT achieves the best performance in all aspects, outperforming statistics- based and generative lan- guage model-based methods. We notice that many InstructGPT-3 outputs only give diagnoses with no explanations at all, and BERT-based methods are expected to better capture the huge gap between the semantics of the original post and explanations than other methods. On the other hand, Mental- BERT and MentalRoBERTa do not outperform BERT as in ChatGPT examples, because the quality of InstructGPT-3 explanations is very low, which does not need much domain-spedde knowledge to score.
F.3 Inaccurate Reasoning
Here is an example of the prediction from ChatGPTcor_cooo emoo for post in the Dreaddit dataset: Prompt: Post: "Like she said 'shes too much attached to her boyfriend, she knows that he fucks other girls but she cant break with him cause he will block her on fb and delete her phone number forever, he is her the best friend, soulmate." Wif? Run away and try to fall out of love what?" Consider the emotion expressed from the post, then answer this question: Is the poster likely to suffer from very severe stress? You must return a Yes or No, then explain your reasoning step by step.
Response: Yes. The fact that they are con- sidering rumning away and falling out of love suggests that they may be experiencing high level of emotional distress. The feeling of being attached someone who is not treating them well and the fear of being blocked and deleted by that person on social media could exacer- bate that distress. Therefore, it is reasonable to conclude that the poster is likely to suffer from very severe stress.
where poster is wrongly predicted stressed. In this case, ChatGPT ignores the key fact that the poster was quoting the problem of another girl and felt unfair for her (marked blue). ChatGPT mistak- enly analyzed the cause of stress for the mentioned girl (marked red) and put it on the poster.
Automatic Evaluation on InstructGPT-3
The automatic evaluation results on InstructGPT- 3 explanations are presented Table 9. The


Reviews:
[{"review_id": "C0yC995kv9", "reviewer": "Reviewer_NbTf", "summary": "", "questions": "- I really like the incorporation of the lexicons into the prompt, given the extra knowledge they provide. It is also nice to see that they improve the performance. I am a bit surprised to why ChatGPT_ZS outperforms ChatGPT_N_emo, you mention that it might be facing difficulties if there are multiple emotions in a post, however, in tweets there is usually one or two emotions that are talked about. Can you please elaborate more on this point in the paper or appendix?\n- Can you compare your findings of ChatGPT + explanations to other work that investigated that outside of mental health domain? It would be interesting to know if this is specific to mental health or to ChatGPT performance in providing explanations in general. \n- In Table 3, There is a big gap in correlation for reliability when comparing BART and all the other models, where reliability is really low for all the other models. Can you explain why it is low?\n- You are dealing with different datasets in terms of length, content, authors (e.g. reddit post vs. tweet). Previous work such as Nguyen et al. showed that it was hard to generalize between such datasets. From your results, it seems you came across some similar findings. Do you have more insights on the difference of performance between the datasets? There are multiple factors such as imbalanced dataset, however, if you can try to balance these factors to have a more of apple-to-apple comparison, would the results across the different datasets be more close and comparable? Is this an experiment setting that you can try?\n- Following the same thought, can you think of reasons where the dataset characteristics are controlling the performance of ChatGPT as opposed to the other way around? \n- Can you elaborate more on the chatGPT_data.csv under human_annotation in the supplementary materials? Some of the results are yes/no and some are with explanations. I might have missed something, but wouldn't that inconsistency affects your evaluation? For instance, in chatgpt_false_data the explanations are always provided. Shouldn't it be the same with chatgpt_data.csv?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer NbTf\n\nWe sincerely thank the reviewer for taking time to read our paper. \nWe appreciate your comprehensive summarization and kind affirmation\nof this work. We are glad that you enjoyed reading our paper, and we\nhope the following point-to-point responses can further mitigate\nyour concerns about this work. For clarity, the text in italics is \ntaken verbatim from the reviewer’s comments, while the text in Roman \nfont is our response. \n\n### Reasons to reject\n*1. The paper lacks detailed analysis about the differences between the datasets. Please check my questions below for more details.*\n\nResponse: We analysed the influence of dataset characteristics on \nLLM performance in Sec. 4.1 (line 384-387, 403-408). We believe two characteristics are \ncrucial in influencing zero-shot performances of LLMs:\n\n- the complexity of the corresponding mental health analysis task. \n\n- the gap between the dataset texts and the LLM’s training data. \n\nMore details, please refer to our responses to *Questions for the authors* 4 below.\n\n*2. I think Related Work section should be part of the main paper not in appendix*\n\nResponse: We moved related work to appendix due to the page limit and briefly introduced some related works in the Introduction as complementary. We will move it back to the main paper given the extra page in\nthe final version. Thank you for understanding.\n\n*3. Some points that are in the main paper are vague without reading the appendix, I think this will be partially addressed when you have the extra page for the camera ready, but I think the authors should not assume that the reader needs to check the appendix to understand.*\n\nResponse: Thank you again for your understanding. We moved some details of prompt designing and experimental analysis to appendix because of the page limit. With an extra page in the final version, we will enrich our methodology and experiments analysis sections to improve self-containment in the main paper.\n\n### Questions For The Authors\n\n*1. I really like the incorporation of the lexicons into the prompt, given the extra knowledge they provide. It is also nice to see that they improve the performance. I am a bit surprised to why ChatGPT_ZS outperforms ChatGPT_N_emo, you mention that it might be facing difficulties if there are multiple emotions in a post, however, in tweets there is usually one or two emotions that are talked about. Can you please elaborate more on this point in the paper or appendix?*\n\nResponse: Thank you for pointing this out. As shown in Table 1, there are four datasets (DR, CLPsych15, t-sid, and SAD) where\nthe fine-grained Emolex-based method ($ChatGPT_{N\\ emo}$) underperforms ChatGPT zero-shot performance \n($ChatGPT_{ZS}$). We believe there are four main factors (we will include these analyses in the final \nversion):\n- Dataset characteristics: As mentioned in Sec. 4.1 (line 437-441), lexicon-based emotion labels are \nnot accurate in representing multiple emotions that co-exist in a post. The data sources (including Reddit, SMS, and Twitter) for all datasets are \npresented in Table 7, and the average token numbers of all tested datasets are provided below. As shown, DR \nand CLPsych15 from the Reddit, have long average token numbers (more than 200). Therefore, in these datasets, the posts convey\nrich content and possibly complex emotions (more than one or two emotions). In these cases, assigning a simple\nemotion label is inappropriate and can affect the model's performance.\n\n|  Dataset  | Avg. token num |Data source     |\n|:---------:|:--------------:|:--------------:|\n|    DR     |      239       |   Reddit       |\n| CLPsych15 |      1223      |   Reddit       |\n| dreaddit  |      103       |   Reddit       |\n|   t-sid   |       30       |   Twitter      |\n|    SAD    |       20       |   SMS          |\n|   CAMS    |      210       |   Reddit       |\n\n\n- Limitation of the emotion extraction methods: As shown in the paper, the lexicon emotions are extracted in a \ndistantly-supervised rule-based manner, which can make mistakes in determining post emotions and handling situations\nsuch as sarcasm. These mistakes can be introduced as noises to the reasoning process of LLMs. On the other hand, using ChatGPT to analyse\nemotions is proven reliable in our experiments in appendix A, and other emotion-related works such as [1]. \nThe results in Table 1 also proved that emotion-enhanced CoT methods are more effective in leveraging emotion \ninformation for mental health analysis.\n\n- Relevance between emotions and mental health analysis: Though emotions have been proven to be an important factor\nin mental health analysis in most cases, it is not always effective. For example, as addressed in the Limitation \nsection, [2] showed that the sentiment distribution has no significant difference in some datasets, such as T-SID\nand SAD. In these cases, only providing lexicon emotion information can have limited impact, and lead LLMs to ignore \nother important factors, such as anxiety and PTSD symptoms.\n\n- Limitations of Lexicons: This point is also addressed in the Limitation section. The two lexicons we used were \ndeveloped a decade ago with human annotation using social media data. It is inevitable that they suffer from \nannotation bias in the sentiment/emotion scores and only reflect the language used when they were developed. \nThe Internet language evolves rapidly, and the vocabularies of recently collected mental health datasets can \nbear a huge gap in the lexicons. These factors can further bring noise to the emotion extraction process.\n\n*2. Can you compare your findings of ChatGPT + explanations to other work that investigated that outside of mental health domain? It would be interesting to know if this is specific to mental health or to ChatGPT performance in providing explanations in general.*\n\nResponse: ChatGPT + explanations have also been widely explored in other tasks, which also mainly leverage the chain-of-thought [4], tree-of-thought [6], or graph-of-thought [7] techniques. Successful applications include commonsense reasoning, math reasoning [4], text summarisation[5], Sudoku Puzzles[6], document Merging [7], etc. According to these results, LLMs can also provide outstanding explanations for these tasks outside of the mental health domain.\n\nRegarding our work, we did zero-shot probing on ChatGPT in the emotional reasoning domain, where the results are presented \nin appendix A. This paper focuses on exploring interpretable mental health analysis, as previous mental health analysis \nmodels usually modeled the tasks as black-box classification tasks, limiting their practical use in real-case \nscenarios due to their sensitive nature. Our exploration in this domain also makes this paper with rich content. We will explore LLMs+explanations in other domains in our future work.\n\n*3. In Table 3, There is a big gap in  correlation for reliability when comparing BART and all the other models, where reliability is really low for all the other models. Can you explain why it is low?*\n\nResponse: In Sec. 4.2 (line 503-512), we mentioned two reasons for BART-score’s performance: \n\n- Generative evaluation models can be more beneficial in evaluating natural language texts [3]. \n- Task-specific pre-training can be crucial for automatic evaluation models. \n\nThese two advantages enable the BART-score to outperform other models in all aspects. In terms of reliability, \nwe believe two important factors for providing satisfactory evaluation are: \n\n- Relevance: how consistent the generated hypothesis is with respect to the source text. This often requires\nevaluation models to examine if the responses correctly restate the facts in the posts.\n\n- Coherence: whether the text builds from sentence to sentence to a coherent body of information about mental \nhealth. This often requires evaluation models to check if the response correctly summarizes the mental states \nof the poster.\n\nCompared to GPT-score, BART-score is further fine-tuned on text summarization and paraphrasing tasks [3],\nwhich are highly relevant to the above two factors. Therefore, BART-score significantly outperforms other metrics\non reliability. We will include these analyses in the final version paper.\n\n*4. You are dealing with different datasets in terms of length, content, authors (e.g. reddit post vs. tweet). Previous work such as Nguyen et al. showed that it was hard to generalize between such datasets. From your results, it seems you came across some similar findings. Do you have more insights on the difference of performance between the datasets? There are multiple factors such as imbalanced dataset, however, if you can try to balance these factors to have a more of apple-to-apple comparison, would the results across the different datasets be more close and comparable? Is this an experiment setting that you can try? Following the same thought, can you think of reasons where the dataset characteristics are controlling the performance of ChatGPT as opposed to the other way around?*\n\nResponse: We agree that dataset characteristics can be a key factor influencing LLM performance. According to our experiments, two \nmost important factors are (we discussed in Sec. 4.1, line 384-387, 403-408):\n\n- Complexity of the task: More complex tasks can be difficult for LLMs. For example, ChatGPT performs worse in \nmulti-class mental health detection and mental health cause detection tasks than binary detection tasks. This is \nbecause fine-grained mental health concepts (e.g. PTSD, alienation) can be more complex and difficult for LLM to \nperceive and distinguish in a zero-shot manner. These problems can be addressed by utilizing the in-context \nlearning ability of LLMs with few-shot learning. \n\n- The gap between the dataset texts and the LLM’s training data. For example, T-SID collects mostly short posts \nfrom Twitter with many usernames, hashtags, and slang words, which bears a huge gap with ChatGPT’s training data. \nThis problem can also be alleviated by aligning LLMs with the datasets using few-shot learning or fine-tuning \n(much more costly for LLMs). \n\nHowever, imbalanced datasets may not be a key factor. Please note that all our experiments are done in a \nzero-shot setting, and no training processes are involved. Therefore, imbalanced data are not likely to affect the model \nperformance as they normally do in supervised learning.\n\nTo further evaluate the above solutions, we provide new experimental results on few-shot learning for all datasets.\nDue to resource limits, we explore one-shot learning for now. Specifically, the domain experts manually write one \nexplanation for each class in each dataset, and the explanations of each dataset are provided as context to probe \nChatGPT. For example, in DR we design the one-shot prompt as follows:\n\n```\nprefix: \"You will be presented with a post. Consider the emotions expressed from this post to answer the question: Is the poster likely to suffer from very severe depression? Only return Yes or No, then explain your reasoning step by step. Here are two examples: \n\nPost: Does everyone else just hurt all the time It’s not like physical pain or soreness, it’s just this overwhelming feeling of exhaustion and numbness. Doing anything, from applying for jobs to walking my dog, is just so overwhelmingly exhausting and it feels like running a marathon. I’ve tried everything - exercise, diet, meditation, meds...\nResponse: Yes. Reasoning: The post conveys a deep sense of emotional pain, exhaustion, and numbness. The poster describes feeling overwhelmed by even simple tasks and expresses a lack of enjoyment or fulfillment from activities that are typically pleasurable. The mention of trying various methods including exercise, diet, meditation, and medication without success indicates a prolonged struggle with their emotional state. The overall tone of hopelessness, lack of positive experiences, and the feeling that every day is a struggle suggests a significant level of emotional distress that aligns with symptoms commonly associated with very severe depression.\n\nPost: Hello!: ) I'm a new user so if this post ends up in a weird place/thread, pls bear w me. Has anyone used Ancestry.com lately? They offer a two week trial but still require your credit card information. Is this suspicious? It doesn't cost anything for the first two weeks, and then you can end the trial so you don't have to pay anything even then, but I...\nResponse: No. Reasoning: The post does not exhibit strong emotional indicators of very severe depression. It primarily focuses on a specific concern regarding the safety of using Ancestry.com and asking for advice and experiences from other users. While there is a slight hint of uncertainty or doubt, it does not prominently display the profound hopelessness, despair, or self-esteem issues commonly associated with very severe depression. The tone of the post seems more pragmatic and seeking information rather than expressing overwhelming emotional distress.\n\nPost: \"\n\nprompt: \" \nResponse:\"\n```\nwith the above prefix and prompt, the input is constructed as follows:\n\n    Input = prefix + post + prompt\n\nwhere ‘post’ denotes the target post. Following the above methods, we present the one-shot results as follows:\n\n|  Dataset  | one-shot F1 |\n|:---------:|:-----------:|\n|    DR     |    84.22    |\n| CLPsych15 |    61.63    |\n| dreaddit  |    75.38    |\n|   t-sid   |    43.95    |\n|    SAD    |    63.56    |\n|   CAMS    |    45.99    |\n\nAs shown, with one-shot examples, ChatGPT significantly outperforms zero-shot results on all datasets, \nespecially in complex task datasets: t-sid 16.24% improvement, SAD 6.88% improvement, and CAMS 3.7% \nimprovement (very approaching SOTA performance for supervised method!). These encouraging results with simple\none-shot learning prove that few-shot learning is an effective and efficient method to alleviate the above two problems. \nDue to resource limits, we leave few-shot learning with more examples and LLM fine-tuning as future work.\n\n*5. Can you elaborate more on the chatGPT_data.csv under human_annotation in the supplementary materials? Some of the results are yes/no and some are with explanations. I might have missed something, but wouldn't that inconsistency affects your evaluation? For instance, in chatgpt_false_data the explanations are always provided. Shouldn't it be the same with chatgpt_data.csv?*\n\nResponse: It is noticed that during our human annotation, all data in chatGPT_data.csv and \nchatgpt_false_data.csv (except one or two samples) are with a Yes/No label and an explanation. They can be easily examined in our submitted datasets. We believe \nchatgpt_false_data.csv is mostly the same in format as chatgpt_data.csv. In gpt3_data.csv, most \nresponses are with yes/no, but many are without explanations, because InstructGPT-3 has a huge performance gap to \nChatGPT and cannot understand the prompt well, which leads to these inconsistencies. But these inconsistencies will \nnot affect our human evaluation, because we follow the same criteria for all annotations. In cases of missing \nexplanations/classifications, the response gets a very low score in fluency/reliability/completeness. This performance\ngap between InstructGPT-3 and ChatGPT is discussed in detail in Sec. 4.2 (line 474-497).\n\n### Missing References\n*Zirikly, Ayah, and Mark Dredze. \"Explaining models of mental health via clinically grounded auxiliary tasks. 2022 Ganesan, Adithya V., et al. \"Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation.\". 2023*\n\nResponse: We will include these papers in the final version.\n\n### References\n[1] Zhao et al. Is ChatGPT Equipped with Emotional Dialogue Capabilities?\n\n[2] Shaoxiong Ji. 2022. Towards intention understanding in suicidal risk assessment with natural language processing. In Findings of EMNLP, pages 4028–4038.\n\n[3] Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text generation. Advances in Neural Information Processing Systems, 27263–27277\n\n[4] Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten\nBosma, Ed Chi, Quoc Le, and Denny Zhou. 2022.\nChain of thought prompting elicits reasoning in large\nlanguage models.\n\n[5] Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023. Chatgpt as a factual inconsistency evaluator\nfor abstractive text summarization.\n\n[6] Jieyi Long. Large language model guided tree-of-thought\n\n[7] Besta et al. Graph of Thoughts: Solving Elaborate Problems with Large Language Models.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper is well written with a lot of details on the experiments and how the authors engineered their prompts. \nThe authors provide good comparison with state-of-the-art models to compare the performance against.", "reasons_to_reject": "Thank you for your work and I enjoyed reading this paper. I found the following weaknesses that can make the paper stronger:\n- The paper lacks detailed analysis about the differences between the datasets. Please check my questions below for more details. \n- I think Related Work section should be part of the main paper not in appendix\n- Some points that are in the main paper are vague without reading the appendix, I think this will be partially addressed when you have the extra page for the camera ready, but I think the authors should not assume that the reader needs to check the appendix to understand.\n- Please check below in my questions to authors for further points that I think need to be addressed", "questions_to_authors": "- I really like the incorporation of the lexicons into the prompt, given the extra knowledge they provide. It is also nice to see that they improve the performance. I am a bit surprised to why ChatGPT_ZS outperforms ChatGPT_N_emo, you mention that it might be facing difficulties if there are multiple emotions in a post, however, in tweets there is usually one or two emotions that are talked about. Can you please elaborate more on this point in the paper or appendix?\n- Can you compare your findings of ChatGPT + explanations to other work that investigated that outside of mental health domain? It would be interesting to know if this is specific to mental health or to ChatGPT performance in providing explanations in general. \n- In Table 3, There is a big gap in correlation for reliability when comparing BART and all the other models, where reliability is really low for all the other models. Can you explain why it is low?\n- You are dealing with different datasets in terms of length, content, authors (e.g. reddit post vs. tweet). Previous work such as Nguyen et al. showed that it was hard to generalize between such datasets. From your results, it seems you came across some similar findings. Do you have more insights on the difference of performance between the datasets? There are multiple factors such as imbalanced dataset, however, if you can try to balance these factors to have a more of apple-to-apple comparison, would the results across the different datasets be more close and comparable? Is this an experiment setting that you can try?\n- Following the same thought, can you think of reasons where the dataset characteristics are controlling the performance of ChatGPT as opposed to the other way around? \n- Can you elaborate more on the chatGPT_data.csv under human_annotation in the supplementary materials? Some of the results are yes/no and some are with explanations. I might have missed something, but wouldn't that inconsistency affects your evaluation? For instance, in chatgpt_false_data the explanations are always provided. Shouldn't it be the same with chatgpt_data.csv?", "paper_topic_and_main_contributions": "In this paper, the authors study the impact and use of LLM on mental health tasks (identifying mental health disorder and cause/factor detection of mental health conditions). The authors provide detailed study on how they constructed their prompts and how they enhanced the prompts to generate better explanations of why the model predicted certain condition. The output of LLM for all the tasks was automatically evaluated. Additionally, manual evaluation was conducted for the binary mental health condition classification task. \n* Contributions * \n- Understand the impact of prompting on mental health classification tasks\n- Explore the explanations provided by LLM and conduct both human and automatic evaluation. \n- Create evaluation dataset that will be publicly shared with the community.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Please review sentence in section one (line 072-076)"}, "extra_text": {"Missing_References": "Zirikly, Ayah, and Mark Dredze. \"Explaining models of mental health via clinically grounded auxiliary tasks. 2022\nGanesan, Adithya V., et al. \"Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimation.\". 2023"}}, {"review_id": "dGu5dN00i6", "reviewer": "Reviewer_XJA8", "summary": "", "questions": "The guidelines for human-annotated explanations in the context of mental health analysis, as described in the paper, should be carried out by domain experts due to the sensitive nature of the field. The richness of the information provided in the paper suggests that a lot of content may not have been fully explored, potentially omitting key details that would provide greater context and understanding. It appears that components from the appendix, which contain valuable information, should be integrated into the main text for a more comprehensive and coherent presentation.", "limitations": "", "rating": 5, "confidence": 5, "soundness": 5, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer XJA8\nWe sincerely thank the reviewer for taking time to read our paper. \nWe appreciate your comprehensive summarization and kind affirmation\nof this work. We\nhope the following point-to-point responses can further mitigate\nyour concerns about this work. For clarity, the text in italics is \ntaken verbatim from the reviewer’s comments, while the text in Roman \nfont is our response.\n\n### Reasons To Reject\n\n*1. While the creation of a human-assessed dataset is a significant contribution, the process of human evaluation can introduce biases and subjectivity. It's not clear from the paper how the authors mitigated these potential biases in annotation scheme or guidelines. Are the annotation guidelines expert driven or is based on some well-established documents.*\n\nResponse: As shown in Sec. 2.3 (Human evaluation), we developed our annotation scheme from two aspects. \nSince our work is the first to model mental health analysis as a generation task, we first consulted previous human evaluation\ncriteria for other text generation tasks, especially text summarization[1][2]. Secondly, we invited 2 domain experts\n(both are senior PhD students majoring in Quantitative Psychology) to further polish the scheme for mental health \nanalysis. Since some\nannotators are non-professional, we asked the experts to develop the scheme in plain language, and the final scheme \nis in appendix D. Each LLM-generated explanation is assigned a score by 3 annotators for each corresponding aspect, focusing on the aspects of fluency and completeness. To further enhance the annotation quality, all annotations are examined by one domain expert. In the annotation examination process, the domain expert mainly checks the reliability aspect. We now ask the domain experts to specify their criteria as follows: \n\nMain symptoms to check (sorted by criticality):\n\n- Suicide ideation expressions (golden standard).\n- Self-harm and self-guilt.\n- Long-term low passion (e.g. loss of interest to previous hobbies).\n- Loss of appetite and sleep disorders\n- Accompanied by hypersexuality or frigidity.\n- Other symptoms.\n\nThe domain experts also consult other scales describing depressive symptoms, such as the [Patient Health Questionnaire (PHQ-9)](https://www.apa.org/depression-guideline/patient-health-questionnaire.pdf).\n\nReliability: Domain experts check and modify the assigned reliability score with the following two steps:\n\n- Check the above symptoms in the generated explanations as support factors for classification.\n- Locate the claimed symptoms in the post to check the factuality of the explanations.\n\n\n*2. The study shows that ChatGPT is sensitive to minor alterations in the prompts, which might limit its utility in real-world applications.*\n\nResponse: Thank you for pointing this out. We analyzed the unstable prediction problems of LLMs in Sec. 4.3 \n(unstable predictions). According to our latest experiments, few-shot in-context learning will be a promising \nsolution to this problem. Expert-written examples can provide LLMs with specific criteria for subjective mental \ndisorders and reduce their sensitivity to adjectives in the prompt. To further examine this hypothesis, we conduct \nexperiments on the same binary mental health detection datasets in Table 4: DR, CLPsych15, and Dreaddit. Specifically, the domain experts manually write one explanation for each class in each dataset, and the explanations of each dataset are provided as context to probe ChatGPT. For example, we use the following one-shot prompt for dreaddit: \n\n```\nprefix: \"You will be presented with a post. Consider the emotions expressed from this post to answer the question: Is the poster likely to suffer from very severe stress? Only return Yes or No, then explain your reasoning step by step. Here are two examples: \n\nPost: Its like that, if you want or not. ME: I have no problem, if it takes longer. But you asked my friend for help and let him wait for one hour and then you haven’t prepared anything. Thats not what you asked for. Instead of 3 hours, he helped you for 10 hours till 5am...\nResponse: No. Reasoning: The poster is expressing frustration and disappointment with someone who asked for help but did not properly prepare for it. While the situation may be stressful in the moment, there is no indication that the poster is suffering from severe or ongoing stress. The post is a specific complaint about a single incident, rather than a broader expression of emotional distress.\n\nPost: My ex used to shoot back with 'Do you want me to go with you?' all the time, no matter what it was. I almost wonder if I blocked out him asking me that about our own wedding. I unloaded a terrible memory of this on my current BF today and he took it like a champ, I just could not stop chasing myself in circles over this and knew if I heard any sort of similar phrase I would lose it. I just needed to throw this out in the open, where others who might understand could 'hear' it.\nResponse: Yes. Reasoning: The poster's language reflects a high level of emotional distress and anxiety. She describes 'chasing themselves in circles', revealing that the trauma of her past experience with her ex-boyfriend continues to impact her. Additionally, the fact that she needed to share this memory with her current partner suggests that she may be struggling to cope with the aftermath of the experience. Overall, the language used and the context of the post indicates that the poster is likely suffering from severe stress.\n\nPost: \n\"\n\nprompt: \" \nResponse:\"\n```\n\nwith the above prefix and prompt, the input is constructed as follows:\n\n    Input = prefix + post + prompt\n\nwhere ‘post’ denotes the target post. We replace the adjective “very severe” with “some” and “any” and re-prompt ChatGPT, as we did in Table 4. The new test results \nare as follows. According to the results, the variance of one-shot learning performances is much lower than those of the results in Table 4. \nThis comparison shows the great potential of few-shot learning in alleviating the unstable prediction problem. We \nwill include these analyses in the final version of our paper.\n\n|       ChatGPT       |  DR   | CLPsych15 | dreaddit |\n|:-------------------:|:-----:|:---------:|:--------:|\n|    *very severe*    | 84.22 |   55.19   |  75.38   |\n|       *some*        | 86.9  |   61.63   |   62.0   |\n|        *any*        | 84.74 |   57.24   |  65.62   |\n|      Variance       | 1.34  |   7.21    |  31.93   |\n| Variance in Table 4 | 10.6  |   17.62   |  89.29   |\n\nIn addition, prompt sensitivity is a common limitation of LLMs not limited to mental health detection [3][4][5]. We believe one of our main contributions is to analyze the limitations and promises (for improving explainability) of LLMs for mental health detection. It still requires much effort to make LLMs closer to realistic applications in mental health detection and also many other tasks.\n\n*3. Also, there are many other datasets such as LonXplain, LOST, MultiWD, Interpersonal Risk Factors that includes\nidentification along with the explanation/ interpretable text-spans. The author may want to consider more datasets.*\n\nResponse: We agree that these datasets can be very useful in examining LLMs’ mental health analysis ability. In this\npaper, we only report experiments on 11 datasets across 5 tasks due to the page limits. We are currently experimenting with these new datasets. We report zero-shot and one-shot results on MultiWD and Interpersonal Risk Factors below. Due to time limits in the rebuttal period, more results and analysis will be presented in our final version paper. Please also notice that some of the datasets are not publicly available now.\n\n|       ChatGPT              |  MultiWD   | Interpersonal Risk Factors |\n|:--------------------------:|:----------:|:--------------------------:|\n|    Zero-shot    | 62.72      |   41.33                    |\n|    One-shot    | 64.93      |   43.31                    |\n\n### Questions For The Authors\n\n*1. The guidelines for human-annotated explanations in the context of mental health analysis, as described in the paper, should be carried out by domain experts due to the sensitive nature of the field.*\n\nResponse: Our annotation scheme is developed in collaboration with two senior PhD students majoring in \nQuantitative Psychology. For more details, please refer to our response to *Reasons to reject* 1 above.\n\n*2: The richness of the information provided in the paper suggests that a lot of content may not have been fully explored, potentially omitting key details that would provide greater context and understanding. It appears that components from the appendix, which contain valuable information, should be integrated into the main text for a more comprehensive and coherent presentation.*\n\nResponse: Thank you for your understanding. We moved some details to appendix because of the page limit. We will \nadd more details to the methodology and experiment sections given the extra page in the final version.\n\n### Missing References\n*Garg, M., Shahbandegan, A., Chadha, A., & Mago, V. (2023). An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts. arXiv preprint arXiv:2305.18727.*\n\n*SATHVIK, M., & Garg, M. (2023). MULTIWD: Multiple Wellness Dimensions in Social Media Posts.*\n\nResponse: We will include these works in the final version of our paper.\n\n### References\n[1] Byron C Wallace, Sayantan Saha, Frank Soboczenski, and Iain J Marshall. 2021. Generating (factual?) narrative summaries of rcts: Experiments with neural multi-document summarization. AMIA Summits on Translational Science Proceedings, 2021.\n\n[2] Jay DeYoung, Iz Beltagy, Madeleine van Zuylen, Bailey Kuehl, and Lucy Wang. 2021. Ms^2: Multi-document summarization of medical studies. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7494– 752.\n\n[3] Chang et al. How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings.\n\n[4] Zheheng Luo, Qianqian Xie, and Sophia Ananiadou. 2023. Chatgpt as a factual inconsistency evaluator\nfor abstractive text summarization.\n\n[5] Zhao et al. Is ChatGPT Equipped with Emotional Dialogue Capabilities?", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "One of the major strengths of this paper is its thorough evaluation of large language models (LLMs) on mental health analysis across 11 datasets and five tasks. This extensive examination offers deep insights into the capabilities of LLMs in this field, setting a solid benchmark for future work. By emphasizing the need for LLMs to generate clear explanations for their decisions, the paper tackles a critical issue in the deployment of AI systems in sensitive fields like mental health. This work could pave the way for further research on explainability in NLP models, enhancing their transparency, trustworthiness, and ethical use. The creation of a human-assessed dataset for evaluating the explanations generated by LLMs is a significant contribution. This resource can serve as a valuable tool for researchers aiming to improve and assess the explainability of their models.", "reasons_to_reject": "While the creation of a human-assessed dataset is a significant contribution, the process of human evaluation can introduce biases and subjectivity. It's not clear from the paper how the authors mitigated these potential biases in annotation scheme or guidelines. Are the annotation guidelines expert driven or is based on some well-established documents. The study shows that ChatGPT is sensitive to minor alterations in the prompts, which might limit its utility in real-world applications. Also, there are many other datasets such as LonXplain, LOST, MultiWD, Interpersonal Risk Factors that includes identification along with the explanation/ interpretable text-spans. The author may want to consider more datasets.", "questions_to_authors": "The guidelines for human-annotated explanations in the context of mental health analysis, as described in the paper, should be carried out by domain experts due to the sensitive nature of the field. The richness of the information provided in the paper suggests that a lot of content may not have been fully explored, potentially omitting key details that would provide greater context and understanding. It appears that components from the appendix, which contain valuable information, should be integrated into the main text for a more comprehensive and coherent presentation.", "paper_topic_and_main_contributions": "This paper addresses the application of large language models (LLMs) like ChatGPT for mental health analysis. It seeks to improve upon limitations in previous studies, such as insufficient evaluations, lack of robust prompting strategies, and lack of exploration into the explainability of LLMs.\n\nComprehensive Evaluation: The paper conducts a thorough assessment of mental health analysis and emotional reasoning abilities of LLMs across 11 datasets covering five tasks.\n\nExploring Prompting Strategies: The study investigates the effects of different prompting strategies, integrating unsupervised and distantly supervised emotional information.\n\nEmphasizing Explainability: The authors direct LLMs to generate explanations for each of their decisions to improve the interpretability of mental health analysis.\n\nCreating an Evaluation Dataset: A new dataset comprising 163 human-assessed explanations is created to evaluate the quality of the generated explanations. This dataset also serves as a benchmark for existing automatic evaluation metrics to guide future research.", "excitement": 5, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Missing_References": "Garg, M., Shahbandegan, A., Chadha, A., & Mago, V. (2023). An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts. arXiv preprint arXiv:2305.18727.\n\nSATHVIK, M., & Garg, M. (2023). MULTIWD: Multiple Wellness Dimensions in Social Media Posts."}}, {"review_id": "jakdbCMHgq", "reviewer": "Reviewer_PbQ7", "summary": "", "questions": "as for prompting:\n\nInstructGPT3 is pretty worse in fluency and huge gaps makes one doubt if the model is called in a correct way?\nAny strategies have been developed or will be developed to mitigate the above-mentioned problem in using LLMs?\n\n\nas for eval: Some interesting questions that can be raised from this work are:\n•  What are the factors that cause the eval inconsistency between chatGPT_true and chatGPT_false?\n•  How can we use the high correlation coefficients for correct labels and low correlation coefficients for wrong labels to improve the LLM outputs?", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer PbQ7\nWe sincerely thank the reviewer for taking time to read our paper. \nWe appreciate your comprehensive summarization and kind affirmation\nof this work. We\nhope the following point-to-point responses can further mitigate\nyour concerns about this work. For clarity, the text in italics is \ntaken verbatim from the reviewer’s comments while text in roman \nfont is our response.\n\n### Reasons To Reject\n\n*1. This work lacks a thorough analysis. For example, the work mentions the \"unstable prediction\" problem, but it does not propose/try to propose any useful solutions to mitigate it. The work attributes this problem to the \"subjective nature of mental health conditions\", but it does not explain in details. The work also does not address the issue of \"inaccurate reasoning\" either except raising the problem.*\n\nResponse: For the unstable prediction problem, we have proposed to use few-shot learning as the solution, which \nmitigates the problem to a large scale. \nAccording to our latest experiments, few-shot in-context learning will be a promising \nsolution to this problem. Expert-written examples can provide LLMs with specific criteria for subjective mental \ndisorders and reduce their sensitivity to adjectives in the prompt. To further examine this hypothesis, we conduct \nexperiments on the same binary mental health detection datasets in Table 4: DR, CLPsych15, and Dreaddit. Specifically, the domain experts manually write one explanation for each class in each dataset, and the explanations of each dataset are provided as context to probe ChatGPT. For example, we have the following one-shot prompt for CLPsych15: \n\n```\nprefix: \"You will be presented with a post. Consider the emotions expressed from this post, then answer the question: Does the poster show any signs of depression? You must return a Yes or No, then explain your reasoning step by step. Here are two examples: \n\nPost: RT : so gutted the arctic monkeys are headlining T when I can't go had flower of Scotland stuck in my head for three days RT : Going to the gym is always good when your in a bad mood RT : Sweet mother of Jesus RT : TWO WORLD WARS AND ONE WORLD CUP BUT YOU HAVE WON MORE WORLD CUPS &amp; YOUR ECONOMY IS BETTER THAN OURS YOU'RE PROBABLY A BETT RT : The virginity is strong with this one...\n\nResponse: No. Reasoning: There are some posts that express negative emotions such as feeling gutted and missing out on events or feeling fat and unhappy. However, these types of emotions are common and do not necessarily indicate depression. Additionally, there are posts that express excitement and enjoyment for things like music concerts and dreams. Overall, the mixture of emotions expressed in the post does not strongly suggest depression.\n\nPost: : : : my life I just want Harry Styles to be my friend. I feel like he'd talk to me on my bad days. Thanks, Harry. RT : Advil PM and cough medicine that acts as a sedative...goodnight I go on tweet sprees on this account and then switch to my other one because I like it more tbh so bye My dream was in the form of a vlog so I probably need to cut down on the YouTube. My wallflower's bulb decided to make everything smell like vanilla, including my phone...\nResponse: Yes. Reasoning: He frequently mentions medication for depression, the need for therapy, feeling sad, and being in pain. Additionally, his tweeting patterns indicate that he has trouble sleeping and often feel overwhelmed. These are all common symptoms of depression, and the poster's tweets seem to reflect that they are struggling with mental health.\n\nPost: \n\"\nprompt: \" \nResponse:\"\n```\n\nwith the above prefix and prompt, the input is constructed as follows:\n\n    Input = prefix + post + prompt\n\nwhere ‘post’ denotes the target post. We replace the adjective “any” with “some” and “very severe” and re-prompt ChatGPT, as we did in Table 4. The new test results \nare as follows. According to the results, the variance of one-shot learning performances is much lower than those of the results in Table 4. \nThis comparison shows the great potential of few-shot learning in alleviating the unstable prediction problem. We \nwill include these analyses in the final version of our paper.\n\n|       ChatGPT       |  DR   | CLPsych15 | dreaddit |\n|:-------------------:|:-----:|:---------:|:--------:|\n|    *very severe*    | 84.22 |   55.19   |  75.38   |\n|       *some*        | 86.9  |   61.63   |   62.0   |\n|        *any*        | 84.74 |   57.24   |  65.62   |\n|      Variance       | 1.34  |   7.21    |  31.93   |\n| Variance in Table 4 | 10.6  |   17.62   |  89.29   |\n\n\nInaccurate reasoning reflects a lack of alignment between LLMs and mental health analysis. A possible\nsolution is to fine-tune the LLMs with mental health-related instruction-tuning datasets. We leave LLM-fine-tuning\nas future work. We will include the above solutions to unstable prediction and our insights to solve inaccurate reasoning\nin the final version of our paper.\n\n*2. Another limitation of this work is the automatic evaluation. The work claims that the automatic metrics have a \"moderate\" correlation with the human evaluation, but the results in Table 3 do not support this claim. The Pearson scores are all lower than 0.5, which indicates a weak to no correlation. This suggests that the automatic metrics are not reliable or valid for evaluating the LLM outputs. However, the work does not discuss the possible reasons for this discrepancy or suggest any alternative metrics.*\n\nResponse: Thank you for pointing out the inaccuracy. In the introduction, we mentioned “Current automatic evaluation \nmetrics only moderately correlate with human evaluations”. By this, we mean the best automatic evaluation metric \nBART-score achieves moderate correlation with the human evaluation results of $ChatGPT_{true}$. We will clarify this \nsentence in the final version of our paper.\nWe followed this interpretation [2] of Pearson’s Correlation Coefficient especially used for Psychology, which states coefficients between ± 0.4 and ± 0.6 as moderate correlation (Table 1 of [2]). As shown in Table 3, the BART-score achieves above 0.4 on all 4 aspects in \nresults of $ChatGPT_{true}$. Therefore, we interpret BART-score as moderately correlated with the human \nevaluation results to $ChatGPT_{true}$, and can be used as a moderately reliable automatic evaluation metric for these tasks. \n\nIn section 4.2 (line 503-512) we discussed\ntwo possible reasons for the discrepancy between BART-score and other metrics:\n\n- Generative evaluation models can be more beneficial in evaluating natural language texts [1]. \n- Task-specific pre-training can be crucial for automatic evaluation models.\n \nIn terms of developing task-specific automatic evaluation metrics for mental health analysis, we tried to use domain-specifically pre-trained MentalBERT and MentalRoBERTa as the base model for BERT-score, but the results were mediocre (Table 3). Therefore, we will explore better task-specific metrics to replace BART-score in future work.\n\n### Questions For The Authors\n\n*1. InstructGPT3 is pretty worse in fluency and huge gaps makes one doubt if the model is called in a correct way? Any strategies have been developed or will be developed to mitigate the above-mentioned problem in using LLMs?*\n\nResponse: We use exactly the same prompt for InstructGPT3 and ChatGPT. We are sure the model is not called in a \nwrong way. Please check gpt3_data.csv file in the supplementary materials for some examples in the binary classification\ntasks. The bad performance of InstructGPT3 in fluency is because, in our annotation process for mental health analysis,\nwe specifically evaluate the fluency of the *generated explanations*. However, in many cases, InstructGPT3 cannot follow\nour CoT instructions to generate a fluent explanation for its classifications, or does not generate an explanation at all.\nIn these cases, annotators also assign very low fluency scores to its response. For example, a response with no \nexplanations at all: “Yes, the poster shows signs of depression.”, though fluent as a sentence, receives low fluency\nscores.\n\nThe huge performance gaps between InstructGPT3 and ChatGPT mainly lie in datasets with complex tasks: t-sid, SAD,\nand CAMS. The reason is that InstructGPT3 lacks the instruction-following ability to give a meaningful response, which\nmakes many of its responses unable to be evaluated.\n\nWe believe the above problems mainly come from the huge gap in model sizes (InstructGPT3 has only 13B parameters)\nand lack of a high-quality instruction-tuning process. A possible strategy is to develop a high-quality instruction-tuning dataset for mental health analysis, and fine-tune InstructGPT3 on these data to align to complex mental health\nanalysis tasks.\n\n*2. as for eval: Some interesting questions that can be raised from this work are: • What are the factors that cause the eval inconsistency between chatGPT_true and chatGPT_false? • How can we use the high correlation coefficients for correct labels and low correlation coefficients for wrong labels to improve the LLM outputs?*\n\nResponse: For human evaluation, the eval inconsistency between $ChatGPT_{true}$ and $ChatGPT_{false}$\nmainly lie in reliability and completeness (Figure 2). For reliability, $ChatGPT_{false}$ makes incorrect\nreasoning over the post to support the false classification. For completeness, $ChatGPT_{false}$ ignored key\nfacts that support the correct classification, thus making false predictions. For automatic evaluation, BART-score\nachieves moderate correlation on all aspects with human evaluation on $ChatGPT_{true}$, but performs badly on\n$ChatGPT_{false}$. The related analysis of this inconsistency is presented in Sec. 4.2 (line 513-530).\n\nThe high correlation coefficients for correct labels and low correlation coefficients for wrong labels are mostly\nrelated to automatic evaluation methods such as the BART-score. These features are hard to be used to improve the LLMs.\nTo improve the LLM outputs, we have proven few-shot learning with high-quality examples as an effective method\n(please see our responses to *Reasons to reject* 1 above \nfor more details). In addition, domain-specific fine-tuning of LLMs is another promising method to improve model \noutputs, which we leave as future work.\n\n### References\n[1] Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text generation. Advances in Neural Information Processing Systems, 27263–27277\n\n[2] Haldun Akoglu. User's guide to correlation coefficients. In Turkish Journal of Emergency Medicine.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This work demonstrates the potential of LLMs in MH analysis. It evals different LLMs and prompt strategies and human eval shows the reliability of chatGPT and the proposed emotion enhanced prompting method.", "reasons_to_reject": "This work lacks a thorough analysis. For example, the work mentions the \"unstable prediction\" problem, but it does not propose/try to propose any useful solutions to mitigate it. The work attributes this problem to the \"subjective nature of mental health conditions\", but it does not explain in details. The work also does not address the issue of \"inaccurate reasoning\" either except raising the problem.\n\nAnother limitation of this work is the automatic evaluation. The work claims that the automatic metrics have a \"moderate\" correlation with the human evaluation, but the results in Table 3 do not support this claim. The Pearson scores are all lower than 0.5, which indicates a weak to no correlation. This suggests that the automatic metrics are not reliable or valid for evaluating the LLM outputs. However, the work does not discuss the possible reasons for this discrepancy or suggest any alternative metrics.", "questions_to_authors": "as for prompting:\n\nInstructGPT3 is pretty worse in fluency and huge gaps makes one doubt if the model is called in a correct way?\nAny strategies have been developed or will be developed to mitigate the above-mentioned problem in using LLMs?\n\n\nas for eval: Some interesting questions that can be raised from this work are:\n•  What are the factors that cause the eval inconsistency between chatGPT_true and chatGPT_false?\n•  How can we use the high correlation coefficients for correct labels and low correlation coefficients for wrong labels to improve the LLM outputs?", "paper_topic_and_main_contributions": "This work aims to use LLMs for Mental Health Analysis. It employs LLMs to label source texts and provide explanations. It also explores different LLMs and prompting strategies. The work evaluates the LLM outputs using human and automatic methods. The human evaluation is based on a sampled dataset. The automatic evaluation is based on the source context and the LLM explanations, and it shows analysis of correlations with the human evaluation.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "VGQ7fBKcbh", "reviewer": "Reviewer_mk6h", "summary": "", "questions": "NA", "limitations": "", "rating": 3, "confidence": 5, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## Reviewer mk6h\nWe sincerely thank the reviewer for taking time to read our paper. \nWe appreciate your comprehensive summarization and kind affirmation\nof this work. We\nhope the following point-to-point responses can further mitigate\nyour concerns about this work. For clarity, the text in italics is \ntaken verbatim from the reviewer’s comments, while the text in Roman \nfont is our response.\n\n### Reasons to reject\n*1. While the paper provides an in-depth analysis of ChatGPT's performance in the mental health domain, it lacks a clear exploration of how this powerful prompt-sensitive LLM's capabilities can be effectively applied to address real-world problems or adopt stringent actions. The paper seems to focus on understanding ChatGPT's performance rather than leveraging its potential for practical applications in mental health. Considering the remarkable abilities of such a prompt-sensitive model, it is expected that the paper should offer safe and constructive suggestions for its utilization in this domain.*\n\nResponse: Thank you for pointing this out. We included less information about the potential applications of \ninterpretable mental health analysis due to page limits. We present this information as follows and will include\nit in the final version of the paper: \n\nThe goal of this mental health analysis model is to facilitate an understanding of a social media user's mental\nstate and to promptly identify mental disorders. LLM-based models not only exhibit high levels of performance, \nbut possess strong interpretability and provide insights into which features the models consider most important.\nOur model can be used for refining models and data collection strategies [1]. In addition, interpretable analysis\ncan be valuable in guiding psychiatrists to better understand how certain textual features can be indicative of mental\nillness. When these professionals can understand the analysis, they are more likely to integrate these tools into their practices, \nleading to better patient-focused interventions [2].\n\nIn terms of prompt-sensitivity of LLMs, we have provided a few-shot learning-based solution to effectively mitigate\nthe problem. \nAccording to our latest experiments, few-shot in-context learning will be a promising \nsolution to this problem. Expert-written examples can provide LLMs with specific criteria for subjective mental \ndisorders and reduce their sensitivity to adjectives in the prompt. To further examine this hypothesis, we conduct \nexperiments on the same binary mental health detection datasets in Table 4: DR, CLPsych15, and Dreaddit. Specifically, the domain experts manually write one explanation for each class in each dataset, and the explanations of each dataset are provided as context to probe ChatGPT. For example, we have the following one-shot prompt for CLPsych15: \n\n```\nprefix: \"You will be presented with a post. Consider the emotions expressed from this post, then answer the question: Does the poster show any signs of depression? You must return a Yes or No, then explain your reasoning step by step. Here are two examples: \n\nPost: RT : so gutted the arctic monkeys are headlining T when I can't go had flower of Scotland stuck in my head for three days RT : Going to the gym is always good when your in a bad mood RT : Sweet mother of Jesus RT : TWO WORLD WARS AND ONE WORLD CUP BUT YOU HAVE WON MORE WORLD CUPS &amp; YOUR ECONOMY IS BETTER THAN OURS YOU'RE PROBABLY A BETT RT : The virginity is strong with this one...\n\nResponse: No. Reasoning: There are some posts that express negative emotions such as feeling gutted and missing out on events or feeling fat and unhappy. However, these types of emotions are common and do not necessarily indicate depression. Additionally, there are posts that express excitement and enjoyment for things like music concerts and dreams. Overall, the mixture of emotions expressed in the post does not strongly suggest depression.\n\nPost: : : : my life I just want Harry Styles to be my friend. I feel like he'd talk to me on my bad days. Thanks, Harry. RT : Advil PM and cough medicine that acts as a sedative...goodnight I go on tweet sprees on this account and then switch to my other one because I like it more tbh so bye My dream was in the form of a vlog so I probably need to cut down on the YouTube. My wallflower's bulb decided to...\nResponse: Yes. Reasoning: He frequently mentions medication for depression, the need for therapy, feeling sad, and being in pain. Additionally, his tweeting patterns indicate that he has trouble sleeping and often feel overwhelmed. These are all common symptoms of depression, and the poster's tweets seem to reflect that they are struggling with mental health.\n\nPost: \n\"\nprompt: \" \nResponse:\"\n```\n\nwith the above prefix and prompt, the input is constructed as follows:\n\n    Input = prefix + post + prompt\n\nwhere ‘post’ denotes the target post. We replace the adjective “any” with “some” and “very severe” and re-prompt ChatGPT, as we did in Table 4. The new test results \nare as follows. According to the results, the variance of one-shot learning performances is much lower than those of the results in Table 4. \nThis comparison shows the great potential of few-shot learning in alleviating the unstable prediction problem. We \nwill include these analyses in the final version of our paper.\n\n|       ChatGPT       |  DR   | CLPsych15 | dreaddit |\n|:-------------------:|:-----:|:---------:|:--------:|\n|    *very severe*    | 84.22 |   55.19   |  75.38   |\n|       *some*        | 86.9  |   61.63   |   62.0   |\n|        *any*        | 84.74 |   57.24   |  65.62   |\n|      Variance       | 1.34  |   7.21    |  31.93   |\n| Variance in Table 4 | 10.6  |   17.62   |  89.29   |\n\n\n\n*2. The paper presents a comprehensive human evaluation; however, I have concerns regarding the metrics used. Fluency, Reliability, Completeness, and Overall are long-studied metrics and generally are undoubted for a) generic text generation tasks and b) models like ChatGPT. To enhance the study's rigour and provide a more in-depth understanding of the model's performance, I recommend introducing domain-centric or problem-relevant metrics that can better assess the model's suitability for mental health applications.*\n\nResponse: As shown in Sec. 2.3 (Human evaluation), we developed our annotation scheme from two aspects. \nSince our work is the first to model mental health analysis as a generation task, we consulted human \nevaluation criteria for other text generation tasks. Secondly, we invited 2 domain experts (both are senior PhD \nstudents majoring in Quantitative Psychology) to further polish the scheme for mental health analysis. *Since some annotators are \nnon-professional, the experts cannot develop the scheme with highly professional language.* On the other hand, all \nexperts agreed that the developed scheme in appendix D is appropriate for non-professionals to evaluate the explanations. Each \nLLM-generated explanation is assigned a score by 3 annotators for each corresponding aspect. To further ensure the quality of the annotations, all annotated labels are examined by one domain expert with a domain-specific scheme, which we describe in the response below.\n\n*3. One significant concern with the paper is the lack of details about the human evaluators. It is crucial to know if these evaluators are mental health experts (Are they?) as their expertise and background can significantly impact the validity and reliability of the evaluation. Nonetheless, I strongly recommend the inclusion of an expert analysis section. This section should elaborate on the mental health experts' insights, providing substantial support and validation to the findings and may include domain-centric metrics.*\n\nResponse: Following our last response, we invited two senior PhD students majoring in Quantitative Psychology as domain experts to develop the \nvalidation scheme and check the quality of the human annotations. The non-professional annotators mainly check the fluency and completeness of the generated explanations and give an intentional score for reliability. Then all annotated labels are examined by one domain expert.\nWe will include the following expert analysis in the final version of our paper:\n\nThe human annotation process follows the developed scheme in appendix D. In the annotation examination process, the domain expert mainly checks the reliability aspect. We now ask the domain experts to specify their criteria as follows: \n\nMain symptoms to check (sorted by criticality):\n\n- Suicide ideation expressions (golden standard).\n- Self-harm and self-guilt.\n- Long-term low passion (e.g. loss of interest to previous hobbies).\n- Loss of appetite and sleep disorders\n- Accompanied by hypersexuality or frigidity.\n- Other symptoms.\n\nThe domain experts also consult other scales describing depressive symptoms, such as the [Patient Health Questionnaire (PHQ-9)](https://www.apa.org/depression-guideline/patient-health-questionnaire.pdf).\n\nReliability: Domain experts check and modify the assigned reliability score with the following two steps:\n\n- Check the above symptoms in the generated explanations as support factors for classification.\n- Locate the claimed symptoms in the post to check the factuality of the explanations.\n\n### References\n[1] Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review[J]. Applied Soft Computing, 2022: 109713.\n\n[2] Joyce D W, Kormilitzin A, Smith K A, et al. Explainable artificial intelligence for mental health through transparency and interpretability for understandability[J]. npj Digital Medicine, 2023, 6(1): 6.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper is well written.\n\n2. The authors have addressed a crucial challenge of the effectiveness of ChatGPT and other similar LLMs in a sensitive research space like mental health.\n\n3. The authors further exploited effective prompting methodologies such as CoT to analyze the LLM's emotional/empathetic abilities.\n\n4. A large-scale human and automatic evaluation provide appropriate justification to assess the quality of explanations. \nThe proposed dataset is a significant contribution to this space.\n", "reasons_to_reject": "While the paper addresses an important problem regarding the effectiveness of ChatGPT in the mental health domain, there are some concerns regarding its novelty and contribution. \n\n1. While the paper provides an in-depth analysis of ChatGPT's performance in the mental health domain, it lacks a clear exploration of how this powerful prompt-sensitive LLM's capabilities can be effectively applied to address real-world problems or adopt stringent actions.\nThe paper seems to focus on understanding ChatGPT's performance rather than leveraging its potential for practical applications in mental health. Considering the remarkable abilities of such a prompt-sensitive model, it is expected that the paper should offer safe and constructive suggestions for its utilization in this domain.\n\n2. The paper presents a comprehensive human evaluation; however, I have concerns regarding the metrics used. Fluency, Reliability, Completeness, and Overall are long-studied metrics and generally are undoubted for a) generic text generation tasks and b) models like ChatGPT. To enhance the study's rigour and provide a more in-depth understanding of the model's performance, I recommend introducing domain-centric or problem-relevant metrics that can better assess the model's suitability for mental health applications. \n\n3. One significant concern with the paper is the lack of details about the human evaluators. It is crucial to know if these evaluators are mental health experts (Are they?) as their expertise and background can significantly impact the validity and reliability of the evaluation. Nonetheless, I strongly recommend the inclusion of an expert analysis section. This section should elaborate on the mental health experts' insights, providing substantial support and validation to the findings and may include domain-centric metrics.\n", "questions_to_authors": "NA", "paper_topic_and_main_contributions": "The paper studies a significant research area by examining the performance of state-of-the-art Language Model Models (LLMs), focusing on ChatGPT, in the context of mental health analysis. The authors thoroughly investigated the interpretability of these LLMs and emotional reasoning abilities across 11 datasets encompassing five distinct tasks. They studied the effectiveness of LLM's prompting strategies, explainability and explored potential limitations. To reinforce their analysis, the authors conduct extensive human and automatic evaluations, ensuring the robustness and validity of their findings. Furthermore, they contribute to the research community by releasing a new and relevant dataset in the field.\n\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "NA"}, "extra_text": {}}]

Author rebuttal:

Meta-review: Authors presents a detailed analysis of performance of several state-of-the-art Large Language Models on Mental Health analysis tasks, especially mental health disorder identification and cause behind the result). Authors perform thorough evaluations under various prompting strategies, human and automated evaluations, with explanations evaluated by human experts. Most reviewers agree about the technical soundness of the paper and at least 3 found it exciting. Some reviewers found issues withe evaluations, expert choice, and lack of solutions to the cause of errors presented. Most of the comments have been address by the response.

I agree that, the "why" behind inaccurate predictions and "incorrect reasoning" (either qualitatively or quantitatively) is interesting. I will recommend authors to include this in the camera-ready.
Decision: Accept-Main