Why this work is in the frame
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Bibliographic record
Abstract
This paper aims to explore and enhance Chat-GPT's abilities to generate more human-like conversations by taking into account the emotional state of the user.To achieve this goal, a prompt-driven Emotional Intelligence is used through the empathetic dialogue dataset in order to propose a more empathetic conversational language model.We propose two altered versions of ChatGPT as follows: (1) an emotion-infused version which takes the user's emotion as input before generating responses using an emotion classifier based on ELECTRA (Clark et al., 2020); and (2) the emotion adapting version that tries to accommodate for how the user feels without any external component.By analyzing responses of the two proposed altered versions and comparing them to the standard version of ChatGPT, we find that using the external emotion classifier leads to more frequent and pronounced use of positive emotions compared to the standard version.On the other hand, using simple prompt engineering to take the user emotion into consideration, does the opposite.Finally, comparisons with state-ofthe-art models highlight the potential of prompt engineering to enhance the emotional abilities of chatbots based on large language models.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it