INTELMO : Enhancing Models’ Adoption of Interactive Interfaces (2023)

Abstract: This paper presents INTELMO, an easy-to-use library to help model developers adopt user-faced interactive interfaces and articles from real-time RSS sources for their language models. The library categorizes common NLP tasks and provides default style patterns, streamlining the process of creating interfaces with minimal code modifications while ensuring an intuitive user experience. Moreover, INTELMO employs a multi-granular hierarchical abstraction to provide developers with fine-grained and flexible control over user interfaces. INTELMO is under active development, with document available at https://intelmo.github.io .

## introduction as natural language processing (nlp) and humancomputer interaction (hci) theories advance, the demand for integrating the two has markedly increased. however, a significant challenge persists in bridging the gap between nlp model development and user interaction. this predicament arises due to the divergent skill sets of these two groups: while model developers excel in building advanced algorithms, they may lack expertise in developing complex interactive user interfaces. conversely, front-end developers might possess proficiency in crafting engaging user interfaces, but they may lack the knowledge of the intricacies involved in nlp model development @xcite . this situation leads to a critical need for model developers to rapidly implement interactive user interfaces. the conventional software development workflow, where model developers pass their projects to front-end engineers to design the user interface, may not be feasible at the model-tuning stage. in addition, efforts have been made to ensure that nlp models use datasets that closely align with real-world tasks. @xcite highlights the significance of bridging the gap between common nlp datasets and real-world sce-narios, proposing a method to enhance authenticity through dynamic grounding. additionally, several models, such as tweetnlp @xcite , marvista @xcite and restgpt @xcite , utilized real web data as their training and testing data source. however, building a web scraper from scratch poses a daunting task for model developers. during the model's tuning and optimization process, developers require real-time feedback on the model's performance and may also need to compare or combine outputs from multiple models. a seamless flow of real-world data can significantly enhance the model's interpretability and enable developers to preview how their model will perform when delivered to end-users. considering the current situation, we believe that a toolkit generating interactive interfaces for model developers should have the following characteristics, listed in order of importance: we believe that tools like intelmo can significantly reduce the difficulty of building and deploying interactive interfaces for nlp models, thereby improving model performance, customization, and interpretability through iterative feedback. ## related work in recent years, as various nlp models have gained extensive attention, researchers have started to take notice of evaluation metrics beyond language model performance. wang et al. (2021) emphasized the importance of human-in-the-loop(hitl) nlp framework, while itg @xcite , and webgpt @xcite incorporated human interaction into text generation, and question answering (qa) tasks within the nlp domain. @xcite , @xcite and @xcite have also recognized the significance of models being applicable to real-world data, proposing evaluation criteria based on realworld human-language model interactions. dms (jónsson and loftsson, 2023) and webshop @xcite attempted to integrate different nlp tasks to achieve dynamic delivery of multi-task systems. regarding relevant tools and platforms, ex-plainboard @xcite and adapter-hub @xcite serves as no-code platforms for developing and testing language models. more recently, ifan @xcite utilizes api technology to enable real-time, interpretationbased interactions with models. ## intelmo we created interface toolkit for extensible language models (intelmo) as a solution for generating interactive interfaces for nlp models. as shown in figure 1 , the system consists of the following components: these components are embedded within in-telmo. unless a model developer decides to finely control each step, which the api allows and provides for, they do not need to understand the specific operational details. for a basic workflow, a model developer only needs to pass configuration options to intelmo which requires usually no more than 10 lines of code per feature, and the generated interactive pages will be produced. ## discussion & future work traditional model interaction testing and deployment often require developers to possess web development skills or collaborate with front-end engineers. this decreases the efficiency of model development and testing, limiting the applicability of many models. intelmo aims to bridge this gap by enhancing developers' development experience and efficiency through real-world datasets and a comprehensive ui framework. however, in-telmo is not currently ready to assist in build-ing interactive pages for large-scale, long-latency complex models. given the need to accommodate the requirements of all models, reducing user wait times will be a key focus for intelmo's future optimization efforts. furthermore, we also recognize that obstacles faced by model developers include deploying models to existing web services such as vercel foot_3 and google cloud platform foot_4 and setting up ci/cd pipelines. we aspire to continue iterating on in-telmo and provide streamlined model deployment solutions.