Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (2024)

Abstract: Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies to ensure its quality and comprehensiveness. Based on this, we then devise a Diverse Response Inpainting (DRI) mechanism to harness the teacher model to produce multiple diverse responses by filling in the masked conversation context. This richness and variety serve as instructive examples, providing a robust foundation for fine-tuning smaller student models. Experiments across varied scenarios reveal that the teacher-student scheme with DRI notably improves the response abilities of smaller models, even outperforming the teacher model in some cases. The dataset and codes are available in https://github.com/pandazzh2020/ExTES.

## introduction the recent rise of large language models (llms) has underscored their aptitude in generalization by adeptly performing tasks through mere conditioning on a scant number of in-context exemplars or straightforward task descriptions in natural language @xcite @xcite . moreover, the exceptional ability of llms to assimilate and retain a broad spectrum of knowledge @xcite , encompassing factual and commonsense realms, has been notably impactful. this prowess has notably reshaped numerous arenas, including the domain of emotional support conversations (esc), enriching both dataset development and model construction. previous compilation of esc datasets relied heavily on methods such as psychotherapy video transcripts @xcite , online repositories @xcite , and questionnaires @xcite . while these sources offer highquality data, they come with significant costs. to this end, recent works @xcite highlight how the rise of llms has revolutionized this space. the intrinsic generalization capabilities and vast knowledge pools of llms now facilitate the expansion and enrichment of esc datasets. however, these datasets generated still lack diversity in es scenarios and fail to provide fine-grained guidance from es strategies. transitioning to the realm of esc model (or chatpal model) construction, the era preceding llms saw a reliance on predefined templates and meticulously crafted rules @xcite , which were beleaguered by a lack of generality. however, with the proliferation of datasets, a shift towards data-driven models has been ob-served @xcite , deploying a myriad of techniques ranging from hierarchical graph networks @xcite to relatively diminutive transformer models @xcite or even pretrained language models @xcite . despite their advancements, a glaring deficit of these models is their inefficacy in adeptly navigating unfamiliar scenarios. contrarily, llms, with their expansive knowledge and robust generality, have been utilized as sagacious experts in response generation @xcite , yielding superior performance results. nevertheless, a critical limitation shadowing such prompt-based chatpal model @xcite is its dependency on exceedingly large models, encapsulating hundreds of billions of parameters @xcite . the deployment of these behemoths on a large scale is deterred by their exorbitant computational demands and inference costs. @xcite shows that, for a given compute budget, the best performances are not achieved by the largest models but by smaller models trained on more data. our endeavor is thus channeled towards empowering smaller models to generate emotional support responses, thereby making large-scale deployment a viable proposition. in light of this, we propose to engage llms as "counseling teacher" to augment the emotional support response adeptness of smaller models, thereby significantly reducing the need for large model sizes. starting with a carefully crafted set of dialogues encapsulating a variety of scenarios and fine-grained strategies, we engage a large teacher model to iteratively generate a large number of generalized and high-quality emotional support conversations. the ensuing curated dialogues are then employed to fine-tune a compact, agile student model to exhibit emotional support response proficiency. by leveraging the large model as a teacher, we unlock the potential for diverse response inpainting (dri), enabling the generation of multiple unique and consistent responses through filling in the masked conversation context, thereby enriching the fine-tuning dataset and encapsulating a flexible response spectrum. this maneuver significantly elevates the performance of student models without additional human annotation. in summary, our contributions are threefold: chatpal with compatible performance. knowledge distillation. knowledge distillation (kd) is a technique where a smaller "student" model learns from a larger "teacher" model, aiming to reduce size and latency without compromising accuracy @xcite . kd has found extensive application across various domains @xcite . our research can be perceived as a nuanced variant of kd, aligning with efforts to enhance the performance of smaller models through leveraging llms. similar endeavors have been undertaken, where llms have been distilled or employed for data augmentation purposes @xcite @xcite . a notable strand within this realm involves utilizing llms for generating both task labels and task-related descriptions, aimed at training smaller models on various tasks @xcite @xcite . unlike traditional setups, the teacher model in our framework is designed to generate a variety of emotional support responses via diverse response inpainting. this unique configuration aims at enriching the student model's capacity with comprehensive guidance, thereby distinguishing our method from previously established ones. ## teacher-student framework in this section, we elucidate how the teacherstudent framework functions. as illustrated in figure 2 , we curate a meticulously designed set of dialogues as our starting point with diverse sce-narios and comprehensive strategies. then, in a two-step fashion, we first iteratively expand these conversations using a large teacher model and then fine-tune a small student chatpal with dri. ## dataset characteristics and quality general statistics. our compiled dataset, named extes, encompasses a total of 11,177 dialogues. detailed breakdowns are presented in ## experiments in this section, building upon the validation of our extes dataset's quality from prior sections, our experiments concentrate on three critical facets: (q1) how effective is our small chatpal for providing emotional support? (q2) how is the effect of using large teacher model to capture comprehensive scenarios and strategies? (q3) what is the effect of diverse response inpainting? ## conclusion in this paper, we proposed a teacher-student framework and demonstrated the potential of llms as "counseling teacher" in enhancing the emotional support response-abilities of smaller models. by leveraging the in-context generalization and extensive knowledge reservoirs of llms, we curated a large-scale emotional support conversation dataset (extes) and deliberately fine-tuned smaller models with diverse response inpainting mechanism to exhibit proficiency in providing emotional support. extensive experiments validate the advantages of the extes dataset as well as the superiority of the proposed teacher-student framework.