Distilling Empathy from Large Language Models (2025)

Abstract: The distillation of knowledge from Large Language Models (LLMs) into Smaller Language Models (SLMs), preserving the capabilities and performance of LLMs while reducing model size, has played a key role in the proliferation of LLMs. Because SLMs are considerably smaller than LLMs, they are often utilized in domains where human interaction is frequent but resources are highly constrained, e.g., smart phones. Therefore, it is crucial to ensure that empathy, a fundamental aspect of positive human interactions, already instilled into LLMs, is retained by SLMs after distillation. In this paper, we develop a comprehensive approach for effective empathy distillation from LLMs into SLMs. Our approach features a two-step fine-tuning process that fully leverages datasets of empathetic dialogue responses distilled from LLMs. We explore several distillation methods beyond basic direct prompting and propose four unique sets of prompts for targeted empathy improvement to significantly enhance the empathy distillation process. Our evaluations demonstrate that SLMs fine-tuned through the two-step fine-tuning process with distillation datasets enhanced by the targeted empathy improvement prompts significantly outperform the base SLM at generating empathetic responses with a win rate of 90+%. Our targeted empathy improvement prompts substantially outperform the basic direct prompting with a 10+% improvement in win rate.

## introduction an emerging trend in the development and application of large language models (llms) is the proliferation of smaller language models (slms) @xcite . slms are essential to the widespread adoption of llms as their significantly smaller sizes allow them to be employed in many application settings that are highly human-interactive but resource-constrained, such as smart phones and intelligent home devices @xcite @xcite . a popular method for developing the capabilities of slms is knowledge distillation from llms, ensuring that the smaller models perform nearly at the same level as the larger models while being much more efficient in size and resource needs @xcite . slms have been shown to perform very well in specific tasks for which they are distilled @xcite . due to their close interactions with humans, it is highly desirable for slms to have proficient empathetic abilities, which are essential for successful positive interactions with humans. in recent years, llms have been shown to possess an impressive understanding of empathy, similar to or surpassing humans' comprehension of empathy @xcite . however, as shown in figure 1 , base slms such as llama-3.1-8b @xcite and @xcite perform considerably worse in empathy than a state-of-the-art llm such as @xcite . gpt-4o outperforms these base slms with win rates above 96% in generating empathetic responses as judged by gemini-2.0-flash @xcite . therefore, it is strongly desired that the level of empathy possessed by llms can be preserved during the distillation. in this paper, we develop a comprehensive approach for systematically distilling empathy from llms into slms with three key components: our evaluations show that slms fine-tuned by our two-step process, with distillation datasets enhanced by targeted empathy improvement prompts, significantly outperform the base slms at generating empathetic responses with a win rate of 90+% over the base. our targeted empathy improvement prompts substantially outperform the basic direct prompting with a 10+% improvement in win rate. ## dataset statistics and analysis in this study, we use the llms-vs-humans dataset created by @xcite . it is sourced from the empathetic dialogues dataset @xcite and contains 2000 unique dialogue contexts (situation and speaker utterance). as shown in figure 2 , for each context, there are a human response to the speaker utterance and the responses from four llms: gpt-4 (openai, 2023), llama-2-70b-chat @xcite , gemini-1.0-pro @xcite , and mixtral-8x7b-instruct @xcite . for each response, a human-annotated empathy score is given with the scale: 1-bad empathetic response, 2-okay, 3-good. figure 3 shows the empathy score distribution of the responses from each responder in the dataset. the majority of responses from every responder received an empathy score of 3, indicating that as the dataset was created, the prompts given to humans and llms were effective in extracting empathetic responses. llms were, in general, more empathetic than humans when responding to the given dialogues as noted by @xcite . figure 4 illustrates the number of dialogue contexts for ([1, 2], [1, 2]) (3, [1, 2]) ([1, 2], 3) (3, 3) (human, model) response ratings 0 100 200 300 400 500 600 number of dialogue contexts gpt-4 llama gemini mixtral on one hand, there are many pairs of human and llm responses that both received empathy scores of 3, i.e., good empathetic response, suitable for sft. on the other hand, there are also many pairs with differing empathy scores. particularly interesting are those pairs where one response received a score of 3 while the other did not, which have potential to serve as contrastive pairs for rlhf. ## two-step fine-tuning for distillation figure 5 illustrates the two-step fine-tuning process that is central to our approach for distilling empathy from llms into slms. given the dialogue dataset with responses generated by a human or an llm, three separate datasets are created for sft, rlhf, and evaluation. the first fine-tuning step is sft on the base slm and the second step is rlhf with dpo on the slm after sft. finally, the fine-tuned slm is evaluated head-on against the base slm to measure its improvement using the win rate metric. base slm step 1: supervised fine-tuning step 2: rhlf-dpo win rate evaluation empathetic dialogues high empathy responses (low, high) empathy response pairs test dialogues fine-tuned slm llm as a judge model data train/eval legend: ## empathy distillation methods we explore three methods for distilling empathy from llms: (1) direct empathy distillation, (2) targeted empathy improvement over human responses, and (3) targeted empathy improvement over llm initial responses. method 1 differs from method 2 in that method 1 prompts the llm to directly generate a response while method 2 asks the llm to improve over a human response. method 3 combines aspects from methods 1 and 2 to prompt the llm to improve over an llm initial response. ## evaluation and discussion it can be observed that targeted empathy improvement over human or llm initial responses outperforms method 1: direct empathy distillation (see figure 6 ) consistently under both sft only and sft then rlhf dpo. for different teacher and student combinations, the performances of different prompting strategies vary. this indicates the benefits of our variety of strategies. though in two combinations the teacher and judge are the same (gpt-4o), these studies show similar trends as the studies where the teacher and judge are different. ## conclusions as knowledge is distilled from llms to slms, their empathetic capabilities must also be preserved. in this paper, we present a comprehensive approach to distilling empathy from llms into slms. first, we developed a two-step fine-tuning process that conducts sft first with high empathy responses and then applies rlhf dpo with (low, high) empathy response pairs. second, we explored three different methods to distill empathy from llms (1) direct empathy distillation, (2) targeted empathy improvement over human responses, and (3) target empathy improvement over llm initial responses. third, for targeted empathy improvement, we explored four prompting strategies, all of which demonstrate significant improvement in distilling empathy over direct prompting and achieve varying success when combined with different initial responses and slms. our study shows that distilling empathy from llms into slms is not only feasible but can also be done extremely effectively.