Prompted LLM s as Chatbot Modules for Long Open-domain Conversation (2023)

Abstract: In this paper, we propose MPC (Modular Prompted Chatbot), a new approach for creating high-quality conversational agents without the need for fine-tuning. Our method utilizes pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility, by using techniques such as few-shot prompting, chain-of-thought (CoT), and external memory. Our human evaluation results show that MPC is on par with fine-tuned chatbot models in open-domain conversations, making it an effective solution for creating consistent and engaging chatbots.

## introduction language models with billions of parameters, such as gpt-3 @xcite and palm @xcite , have achieved state-of-the-art performance on many nlp tasks. to fine-tune these large language models (llms) for open-domain chatbot tasks, one could use a dataset of conversational data that is representative of the target domain. however, fine-tuning llms for opendomain chatbots can be challenging due to the computational burden of updating models with billions of parameters and the scarcity of data in the dialogue domain. furthermore, fine-tuning can limit the model's versatility by restricting it to a specific domain, and result in the loss of domainagnostic knowledge acquired during pre-training, as reported by @xcite . multi-task training on different datasets, as proposed by @xcite , can address the versatility issue but has limitations, such as the need for data to train each skill and the difficulty determining the necessary skills for an open-domain chatbot. in fact, the growing number of modules for chatbots, as in blenderbot3 (bb3) @xcite , points towards the increasing burden of data and computation when fine-tuning for each new chatbot model. interestingly, some llms have the ability to perform in-context learning (icl) @xcite @xcite @xcite @xcite . this capability enables the model to rapidly adapt to and execute a specific task based on a brief instruction and a few examples, without requiring additional fine-tuning. this can be utilized to create an open-domain chatbot, where a prompt describing a task required for opendomain dialogue and a few examples of solving such task can be provided to the llm, allowing it to generate information that is pertinent to the current conversation. our contributions we present a novel approach for creating high-quality conversational agents without the need for fine-tuning. our proposed chatbot, mpc (modular prompted chatbot), utilizes open-sourced pre-trained language models to increase the flexibility of designing the modules of an open-domain chatbot. our approach enhances multiple conversational capabilities by utilizing a modularized agent that incorporates llms with prompt techniques such as few-shot icl and chain-of-thought (cot). in the paper, we design mpc to achieve long-term consistency, a domain in which previous chatbots have struggled. our human evaluation results show that mpc is on par with or even preferred over fine-tuned llms, such as blenderbot, in an open-domain conversational setting. this approach highlights the potential of pre-trained llms to adapt to new tasks without fine-tuning, providing an efficient solution for creating open-domain conversational agents. modular prompting has found use beyond benchmarks and in conversation generation. @xcite used an llm to generate a socially diverse dialogue dataset that is more natural and detailed than existing crowdsourced datasets. moreover, hierarchical prompt modules prove to help long-range coherence for generating narratives and plays @xcite . we refer to @xcite for a detailed overview on such augmented uses of llms. open-domain chatbots many recent dialogue agents rely on dialogue-finetuned llms. in @xcite , lamda has been trained on large amounts of crawled conversational data and has used a fine-tuned classifier for model safety. more recently, similar to our modularization approach, bb3 fine-tunes open pre-trained transformers (opt) @xcite on qa and dialogue datasets and uses one shared model weight as multiple modules. on the other hand, @xcite eliminate the need for fine-tuning on dialogue data by feeding retrieved dialogue samples as few-shot for gpt-j @xcite . we find this work to be complementary to our work, as the few-shot dialogue can be seen as an approach to enhance the utterance generator module. ## modular prompted chatbot we present a modular chatbot system (fig. 1 ) that uses prompt-based llms to maintain persona and engagement throughout long-term conversations. i'm 27 years old. how old are you? how old is sarah? -sarah is 25. -sarah is a student. clarifier (few-shot) rephrase user's question in third-person. user: i'm 27 years old. how old are you? specifically, memory processor (few-shot) (cot) this is the list of sarah's knowledge. (1)sarah is 25. (2) sarah is a student. q: how old is sarah? a: let's think step by step. … answer: sarah thinks utterance generator (zero-shot) this is the list of sarah's persona. sarah thinks sarah is 25 and a student. this is the conversation between sarah and user. {dialogue history} user: i'm 27 years old. how old are you? sarah: summarizer (few-shot) {dialogue history} summary - ## experimental setup we evaluate our chatbot's performance by assessing core skills necessary for long-term conversations. we assess consistency by assigning one of five personas, each with 12 facts from personachat @xcite , and presenting these facts to evaluators. for each experiment, we collect 20 turns from each evaluator and at least 500 turns in total from two subgroups: amazon mechanical turk and university students. see appendix c for a detailed explanation of our data collection. in our setup, there are four groups of models. ## conclusion we demonstrated that a modular approach using llms, namely mpc, can be an effective solution for long-term open-domain chatbots without further finetuning. we compared mpc to fine-tuned and vanilla lm baselines and found that our approach achieved superior performance by human evaluation. additionally, our modular system incorporated persona and information from dialogue history more effectively than the non-modular ones according to our consistency evaluation.