Emotional Intelligence Assessment using Prompt Engineering in Instruction-tuned Llama 3.1
Bibliographic record
Abstract
Assessing the alignment of generative foundation models with human emotions has been an underexplored domain until fairly recently. In this paper, we evaluated the emotional reasoning capacity of the instruction-tuned Llama 3.1-currently presumed one of the more advanced foundation models. To prompt the Llama 3.1 model and score its responses, we chose for our experiments the long form Trait Emotional Intelligence Questionnaire (TEIQue), given its predominant empirical validation and comprehensive psychometric assessment. We also reviewed both the Toronto Alexithymia Scale (TAS) and the Empathy Quotient (EQ) tools for a broader analysis, comparing performance of Llama 3.1 to GPT 3.5, GPT 4, and Gemini models. By adopting a controlled prompt-tuning method, our study explored the impact of different prompt styles, verbose and concise, and the augmenting of immediate knowledge base on the model response quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".