Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction

SeungHyun Yi; Seungjun Yi; Sriram Sattiraju; Haeun Park; Joakim Nguyen; Terence Lim; Joseph Skrovan; Mehak Beri

cs.CLOct 15, 2026

Identifying Effective Strategies in Prompt Evolution for Stress and Emotion Prediction

Authors: SeungHyun Yi, Seungjun Yi, Sriram Sattiraju, Haeun Park, Joakim Nguyen, Terence Lim, Joseph Skrovan, Mehak Beri,

Organizations: Department of Computer Science, Kyung Hee University, Yongin, South Korea · Department of Biomedical Engineering, University of Texas at Austin, Austin, TX, USA · Department of Computer Science, University of Texas at Austin, Austin, TX, USA · College of Fine Arts, University of Texas at Austin, Austin, TX, USA · College of Natural Sciences, University of Texas at Austin, Austin, TX, USA · Graphen, Inc., New York, NY, USA · School of Information, University of Texas at Austin, Austin, TX, USA · Department of Pediatrics, University of Texas at Austin, Austin, TX, USA · Computer and Data Science, University of Texas at Austin, Austin, TX, USA · Department of Cardiac Surgery, Division of Pediatric Cardiac Surgery, Vanderbilt University School of Medicine; Pediatric Heart Institute, Monroe Carell Jr. Children’s Hospital at Vanderbilt, Nashville, TN, USA · Psychiatry, University of Texas at Austin, Austin, TX, USA · Department of Population Health, Dell Medical School, University of Texas at Austin, Austin, TX, USA

Abstract

Early stress detection and risk prediction are crucial for timely mental health assessment and support. While large language models have recently shown promise for social media stress prediction, relying on accuracy can obscure false positives. Also, prompt revisions yield inconsistent gains across models and tasks, with unclear links to strategy combinations and instruction changes. By comparing six methods on social media stress and emotion data across two model sizes, we found that GEPA and its extension combining instructions from two prompts (GEPA+merge) improved prediction over initial prompts in several settings. We also identified five prompting strategies associated with development gains. These strategies included directing attention to emotional evidence, distinguishing current from past distress, illustrating indirect cues, requiring emotional context, and guiding label selection. Moreover, we documented four observations across tasks and evaluation settings. In a separate set of searches, binary stress prediction improved early, whereas multilabel emotion prediction initially declined and partly recovered. The other three concerned incorrect or conflicting instructions, gains on small comparison samples that did not hold on full development data, and changes in selected prompts' development and reported test rankings. These findings identify effective prompt evolution methods, strategy combinations associated with gains, and task and evaluation limits, providing guidance for applying prompt evolution methods to social media stress detection.

Figures & tables

Fig. 1: Prompt evolution through revision and merging. Main study procedures. (a) GEPA revises one instruction and retains a revision only if its score improves on the same three training examples. (b) GEPA+merge also combines instructions from two parents, accepting a score at least as high as the better parent’s on five development examples. Both evaluate accepted prompts on all 100 development examples and select the highest scoring prompt at search end.

Statistic	Dreaddit [ 8 ]	GoEmotions [ 9 ]
Text unit	Five sentences	One comment
Mean words (SD)	85.6 (32.1)	12.8 (6.7)
Median words	80	12
Annotated items	3,553	58,009
Released classification subset	3,553	54,263
Available labels	2	28

TABLE I: Dataset Statistics

Fig. 2: Label prediction performance across six methods. Section III-B defines all six methods. Groups pair dataset (D: Dreaddit, G: GoEmotions) with Qwen3 model size (8B or 32B). Scores use 200 attempted test items per seed (42 to 46). Bars show means and whiskers one sample standard deviation (

n=5

, except asterisked G32 ICL Conformability,

n=4

). Axes are zoomed, with breaks marking nonzero starts. Compare gaps only within each metric.

Panel of Figure 4 — Fig. 3: Historical GEPA trees, mean scores, and golden routes. Qwen3-8B GEPA trees for Dreaddit seed 42 (a) and GoEmotions seed 44 (b) contain 20 and 26 candidates, respectively. Circles show identifiers above development Macro-F1 (a) or Micro-F1 (b), in percent. Arrows link parents to children, amber marks golden routes, cyan circles with double outlines mark selected prompts, and grey circles mark other candidates. N/A denotes missing scores. Positions are adjusted for readability. Blue F1 and orange ACC curves show means across seeds 42 to 44 with one sample SD. At each tree level, $n$ counts scored prompts across seeds in F1/ACC order, while SD is across seed means. Open mean markers indicate one seed without SD. Amber curves show golden route F1 scores without SD. Both plots align vertically with their trees by tree level. Panels use different vertical scales. Candidate Conformability is unavailable. Section III-D explains the averages and missing scores.

Fig. 4: Prompt changes along historical GEPA golden routes. Panels match Figure 3 ’s Qwen3-8B trees. Roots are complete and child excerpts retain source wording. Shading marks new or rewritten wording relative to the full parent, not new strategy types. Arrows link parents and children. Scores are development Macro-F1 (a) and Micro-F1 (b), in percent. Parentheses give changes in percentage points from unrounded parent scores. The dagger flags a cue association for scrutiny, not incorrect label identifiers [ 26 ] . Section III-D explains the comparison limits.

Strategy	Examples from prompts	Development scores (%)
Directing Attention to Emotional Evidence Dreaddit, seed 42	Candidate 1: “the label is based on the author’s emotional state, not on the topic” Candidate 10: “Stress is primarily linked to the emotional state of the author, not the external situation itself.”	Candidate 1: 76.14 Candidate 10: 84.96 Separate branches Candidate 10 selected
Distinguish current from past distress Dreaddit, seed 42	“stress is determined by the current emotional state and expression in the text, not the past events themselves.”	Candidates $0\rightarrow 2$ $66.56\rightarrow 79.80$
Illustrate indirect emotional cues Dreaddit, seed 42	“self-deprecation”, “feeling trapped”, and “feeling like they ‘deserve’ negative treatment”	Candidates $2\rightarrow 9$ $79.80\rightarrow 81.88$
Require emotional context Dreaddit, seed 42	“Physical symptoms or medical concerns are not sufficient on their own to label the text as stress, unless they are tied to emotional strain or anxiety.”	Candidates $9\rightarrow 10$ $81.88\rightarrow 84.96$ Child 18: 78.52 (Same rule retained)
Guide emotion label selection GoEmotions	Neutral: “should only be selected if the text explicitly reflects no strong emotional content.” 1. “extract all emotionally charged words or phrases.” 2. “Cross-reference these phrases with the 28-label inventory to identify all possible matching labels.”	Seed 43, candidates $0\rightarrow 3$ $31.62\rightarrow 24.18$ Seed 44, candidates $3\rightarrow 15$ $29.37\rightarrow 31.91$

TABLE II: Effective Strategies in Prompt Evolution: Supporting Examples

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