# Multi-Dimensional Prompt Chaining to Improve Open-Domain Dialogue Generation Source: [Multi-Dimensional Prompt Chaining to Improve Open-Domain Dialogue Generation]() Collection index: [llms.txt]() Data updated: 2026-01-07T05:00:04.581Z Authors: Livia Leong Hui Teng Published: 2026-01-03T02:21:27.000Z ## Abstract Small language models (SLMs) offer significant deployment advantages but often struggle to match the dialogue quality of larger models in open-domain settings. In this paper, we propose a multi-dimensional prompt-chaining framework that integrates Naturalness, Coherence, and Engagingness dimensions to enhance human-likeness in open-domain dialogue generation. We apply the framework to two SLMs, TinyLlama and Llama-2-7B, and benchmark their performance against responses generated by substantially larger models, including Llama-2-70B and GPT-3.5 Turbo. We then employ automatic and human evaluation to assess the responses based on diversity, contextual coherence, as well as overall quality. Results show that the full framework improves response diversity by up to 29%, contextual coherence by up to 28%, and engagingness as well as naturalness by up to 29%. Notably, Llama-2-7B achieves performance comparable to substantially larger models, including Llama-2-70B and GPT-3.5 Turbo. Overall, the findings demonstrate that carefully designed prompt-based strategies provide an effective and resource-efficient pathway to improving open-domain dialogue quality in SLMs. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Open-domain Dialogue Generation on DailyDialog (test)](): Open-domain Dialogue Generation; DailyDialog (test); Win Rate: 5900. ## Original sources and implementation [Original paper]() [PDF]()