Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning (2023)

Abstract: The current generation of intelligent assistants require explicit user requests to perform tasks or services, often leading to lengthy and complex conversations. In contrast, human assistants can infer multiple implicit intents from utterances via their commonsense knowledge, thereby simplifying interactions. To bridge this gap, this paper proposes a framework for multi-domain dialogue systems. This framework automatically infers implicit intents from user utterances, and prompts a large pre-trained language model to suggest suitable task-oriented bots. By leveraging commonsense knowledge, our framework recommends associated bots in a zero-shot manner, enhancing interaction efficiency and effectiveness. This approach substantially reduces interaction complexity, seamlessly integrates various domains and tasks, and represents a significant step towards creating more human-like intelligent assistants that can reason about implicit intents, offering a superior user experience.

## introduction intelligent assistants have become increasingly popular in recent years, but they require users to explicitly describe their tasks within a single domain. yet, the exploration of gradually guiding users through individual task-oriented dialogues has been relatively limited @xcite . this limitation is amplified when tasks extend across multiple domains, compelling users to interact with numerous bots to accomplish their goals @xcite . for instance, planning a trip might involve interacting with one bot for flight booking and another for hotel reservation, each requiring distinct, taskspecific intentions like "book a flight ticket" to activate the corresponding bot, such as an airline bot. in contrast, human assistants can manage highlevel intentions spanning multiple domains, utiliz-1 code: http://github.com/miulab/implicitbot . commonsense reasoning commonsense reasoning involves making assumptions about the nature and essence of typical situations humans encounter daily. these assumptions encompass judgments about the attributes of physical objects, taxonomic properties, and individuals' intentions. existing commonsense knowledge graphs such as conceptnet @xcite , atomic @xcite facilitate models to reason over human-annotated commonsense knowledge. this paper utilizes a i want to plan a trip to seattle. ## framework figure 2 illustrates our proposed two-stage framework, which consists of: 1) a commonsenseinferred intent generator, and 2) a zero-shot bot recommender. given a user's high-level intention utterance, the first component focuses on generating implicit task-oriented intents. the second component then utilizes these task-specific intents to recommend appropriate task-oriented bots, considering the bots' functionality through a large pretrained language model. ## experiments to evaluate the zero-shot performance of our proposed framework, we collected a test set specific to our multi-domain scenarios. we recruited six volunteers who were knowledgeable about the target scenarios to gather their high-level intention utterances along with the associated task-oriented bots. upon filtering out inadequate data, our test set incorporated a total of 220 task-oriented bots and 92 high-level utterances, each linked with an average of 2.4 bots. the number of bot candidates considered in our experiments is 6,264, highlighting the higher complexity of our tasks. our primary aim is to connect a high-level intention with its corresponding task-oriented bot recommendation by leveraging external commonsense knowledge. therefore, we assess the effectiveness of the proposed methodology and compare it with a 1-stage prompting baseline using gpt-j to maintain fairness in comparison. for this baseline, we perform simple prompting on the user's high-level utterance concatenating with a uniform app-based prompt: "so i can use some popular apps called." in response to these context prompts, gpt-j generates the associated (multiple) app names, serving as our baseline results. to further investigate whether our proposed commonsense-inferred implicit intent generator is suitable for our recommendation scenarios, we introduce another 2-stage prompting baseline for comparison. taking into account that contemporary large language models exhibit astonishing proficiency in commonsense reasoning, we substitute our first component with the state-of-the-art gpt-3 @xcite to infer implicit intents, serving as another comparative baseline. ## discussion generated intent example input my best friend likes pop music. comet @xmath0. comet @xmath1 ## conclusion this paper introduces a pioneering task centered around recommending task-oriented dialogue systems solely based on high-level user intention utterances. the proposed framework leverages the power of commonsense knowledge to facilitate zero-shot bot recommendation. experimental results corroborate the reasonability of the recommended bots through both automatic and human evaluations. experiments show that the recommended bots are reasonable for both automatic and human evaluation, and the inferred intents can provide informative and interpretable rationales to better convince users of the recommendation for practical usage. this innovative approach bridges the gap between user high-level intention and actionable bot recommendations, paving the way for a more intuitive and user-centric conversational ai landscape.