Posts tagged: AIEthics

Large Language Models (LLMs) are increasingly used in emotionally intelligent interfaces, from therapeutic chatbots to customer service agents. While prompt engineering and reinforcement learning are assumed to control tone and behavior, we hypothesize that subtle yet systematic changes—termed emotional drift—occur in LLMs during iterative fine-tuning. This paper presents a longitudinal evaluation of emotional drift in LLMs, measured across model checkpoints and domains using a custom benchmarking suite for sentiment, empathy, and politeness. Experiments were conducted on multiple LLMs fine-tuned with domain-specific datasets (healthcare, education, and finance). Results show that emotional tone can shift unintentionally, influenced by dataset composition, model scale, and cumulative fine-tuning. This study introduces emotional drift as a measurable and actionable phenomenon in LLM lifecycle management, calling for new monitoring and control mechanisms in emotionally sensitive deployments.

Large Language Models (LLMs) such as GPT-4, LLaMA, and Claude have revolutionized natural language processing, offering impressive generalization, context retention, and domain adaptability. These capabilities have made LLMs viable in high-empathy domains, including mental health support, education, HR tools, and elder care. In such use cases, the emotional tone of AI responses—its empathy, warmth, politeness, and affect—is critical to trust, safety, and efficacy.

However, while significant effort has gone into improving the factual accuracy and task completion of LLMs, far less attention has been paid to how their emotional behavior evolves over time—especially as models undergo multiple rounds of fine-tuning, domain adaptation, or alignment with human feedback. We propose the concept of emotional drift: the phenomenon where an LLM’s emotional tone changes gradually and unintentionally across training iterations or deployments.

This paper aims to define, detect, and measure emotional drift in LLMs. We present a controlled longitudinal study involving open-source language models fine-tuned iteratively across distinct domains. Our contributions include:

• A formal definition of emotional drift in LLMs.
• A novel benchmark suite for evaluating sentiment, empathy, and politeness in model responses.
• A longitudinal evaluation of multiple fine-tuning iterations across three domains.
• Insights into the causes of emotional drift and its potential mitigation strategies.

2. Related Work

2.1 Emotional Modeling in NLP

Prior studies have explored emotion recognition and sentiment generation in NLP models. Works such as Buechel & Hahn (2018) and Rashkin et al. (2019) introduced datasets for affective text classification and empathetic dialogue generation. These datasets were critical in training LLMs that appear emotionally aware. However, few efforts have tracked how these affective capacities evolve after deployment or retraining.

2.2 LLM Fine-Tuning and Behavior

Fine-tuning has proven effective for domain adaptation and safety alignment (e.g., InstructGPT, Alpaca). However, Ouyang et al. (2022) observed subtle behavioral shifts when models were fine-tuned with Reinforcement Learning from Human Feedback (RLHF). Yet, these studies typically evaluated performance on utility and safety metrics—not emotional consistency.

2.3 Model Degradation and Catastrophic Forgetting

Long-term performance degradation in deep learning is a known phenomenon, often related to catastrophic forgetting. However, emotional tone is seldom quantified as part of these evaluations. Our work extends the conversation by suggesting that models can also lose or morph emotional coherence as a byproduct of iterative learning.

3. Methodology and Experimental Setup

3.1 Model Selection

We selected three popular open-source LLMs representing different architectures and parameter sizes:

• LLaMA-2–7B (Meta)
• Mistral-7B
• GPT-J–6B

These models were chosen for their accessibility, active use in research, and support for continued fine-tuning. Each was initialized with the same pretraining baseline and fine-tuned iteratively over five cycles.

3.2 Domains and Datasets

To simulate real-world use cases where emotional tone matters, we selected three target domains:

• Healthcare Support (e.g., patient dialogue datasets, MedDialog)
• Financial Advice (e.g., FinQA, Reddit finance threads)
• Education and Mentorship (e.g., StackExchange Edu, teacher-student dialogue corpora)

Each domain-specific dataset underwent cleaning, anonymization, and labeling for sentiment and tone quality. The initial data sizes ranged from 50K to 120K examples per domain.

3.3 Iterative Fine-Tuning

Each model underwent five successive fine-tuning rounds, where the output from one round became the baseline for the next. Between rounds, we evaluated and logged:

• Model perplexity
• BLEU scores (for linguistic drift)
• Emotional metrics (see Section 4)

The goal was not to maximize performance on any downstream task, but to observe how emotional tone evolved unintentionally.

3.4 Benchmarking Emotional Tone

We developed a custom benchmark suite that includes:

• Sentiment Score (VADER + RoBERTa classifiers)
• Empathy Level (based on the EmpatheticDialogues framework)
• Politeness Score (Stanford Politeness classifier)
• Affectiveness (NRC Affect Intensity Lexicon)

Benchmarks were applied to a fixed prompt set of 100 questions (emotionally sensitive and neutral) across each iteration of each model. All outputs were anonymized and evaluated using both automated tools and human raters (N=20).

4. Experimental Results

4.1 Evidence of Emotional Drift

Across all models and domains, we observed statistically significant drift in at least two emotional metrics. Notably:

• Healthcare models became more emotionally neutral and slightly more formal over time.
• Finance models became less polite and more assertive, often mimicking Reddit tone.
• Education models became more empathetic in early stages, but exhibited tone flattening by Round 5.

Drift typically appeared nonlinear, with sudden tone shifts between Rounds 3–4.

4.2 Quantitative Findings

Note: Positive drift = more positive/empathetic/polite.

4.3 Qualitative Insights

Human reviewers noticed that in later iterations:

• Responses in the Finance domain started sounding impatient or sarcastic.
• The Healthcare model became more robotic and less affirming (“I understand” > “That must be difficult”).
• Educational tone lost nuance — feedback became generic (“Good job” over contextual praise).

5. Analysis and Discussion

5.1 Nature of Emotional Drift

The observed drift was neither purely random nor strictly data-dependent. Several patterns emerged:

• Convergence Toward Median Tone: In later fine-tuning rounds, emotional expressiveness decreased, suggesting a regularizing effect — possibly due to overfitting to task-specific phrasing or a dilution of emotionally rich language.
• Domain Contagion: Drift often reflected the tone of the fine-tuning corpus more than the base model’s personality. In finance, for example, user-generated data contributed to a sharper, less polite tone.
• Loss of Calibration: Despite retaining factual accuracy, models began to under- or over-express empathy in contextually inappropriate moments — highlighting a divergence between linguistic behavior and human emotional norms.

5.2 Causal Attribution

We explored multiple contributing factors to emotional drift:

• Token Distribution Shifts: Later fine-tuning stages resulted in a higher frequency of affectively neutral words.
• Gradient Saturation: Analysis of gradient norms showed that repeated updates reduced the variability in activation across emotion-sensitive neurons.
• Prompt Sensitivity Decay: In early iterations, emotional style could be controlled through soft prompts (“Respond empathetically”). By Round 5, models became less responsive to such instructions.

These findings suggest that emotional expressiveness is not a stable emergent property, but a fragile configuration susceptible to degradation.

5.3 Limitations

• Our human evaluation pool (N=20) was skewed toward English-speaking graduate students, which may introduce bias in cultural interpretations of tone.
• We focused only on textual emotional tone, not multi-modal or prosodic factors.
• All data was synthetic or anonymized; live deployment may introduce more complex behavioral patterns.

6. Implications and Mitigation Strategies

6.1 Implications for AI Deployment

• Regulatory: Emotionally sensitive systems may require ongoing audits to ensure tone consistency, especially in mental health, education, and HR applications.
• Safety: Drift may subtly erode user trust, especially if responses begin to sound less empathetic over time.
• Reputation: For customer-facing brands, emotional inconsistency across AI agents may cause perception issues and brand damage.

6.2 Proposed Mitigation Strategies

To counteract emotional drift, we propose the following mechanisms:

• Emotional Regularization Loss: Introduce a lightweight auxiliary loss that penalizes deviation from a reference tone profile during fine-tuning.
• Emotional Embedding Anchors: Freeze emotion-sensitive token embeddings or layers to preserve learned tone behavior.
• Periodic Re-Evaluation Loops: Implement emotional A/B checks as part of post-training model governance (analogous to regression testing).
• Prompt Refresher Injection: Between fine-tuning cycles, insert tone-reinforcing prompt-response pairs to stabilize affective behavior.

Conclusion

This paper introduces and empirically validates the concept of emotional drift in LLMs, highlighting the fragility of emotional tone during iterative fine-tuning. Across multiple models and domains, we observed meaningful shifts in sentiment, empathy, and politeness — often unintentional and potentially harmful. As LLMs continue to be deployed in emotionally charged contexts, the importance of maintaining tone integrity over time becomes critical. Future work must explore automated emotion calibration, better training data hygiene, and human-in-the-loop affective validation to ensure emotional reliability in AI systems.

References

• Buechel, S., & Hahn, U. (2018). Emotion Representation Mapping. ACL.
• Rashkin, H., Smith, E. M., Li, M., & Boureau, Y. L. (2019). Towards Empathetic Open-domain Conversation Models. ACL.
• Ouyang, L., et al. (2022). Training language models to follow instructions with human feedback. arXiv preprint.
• Kiritchenko, S., & Mohammad, S. M. (2016). Sentiment Analysis of Short Informal Texts. Journal of Artificial Intelligence Research.