# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 5 Discussion
### RQ2: How Do Chatbot-Based V-NLIs Contribute to Interactions with the Visual Space?
As a first step in this direction, DashBot [118] presents a new method  
Figure 11: Spider chart displaying the relationship between Visual Space and V-NLI characteristics of analysed works. for training agents to imitate human exploration behaviour in visualisations using deep reinforcement learning. It has the potential to develop visualisation recommenders without requiring pre-existing training datasets. However, it uses simple data types (tabular) with basic visualisations and does not use NLP. Sevi [119] is another ML-based data visualisation system that creates visualisations using text or speech. Sevi's key component includes an end-to-end neural machine translation model called ncNet [28], which was evaluated using a cross-domain benchmark called nvBench [120]. The inputs of the model are an optional chart template and the NL query, outputting a chart styling of the rendered visualisation. Another approach is to combine user-defined Visual Mapping identification and automatic identification, which can give experienced users more freedom in their analysis, as demonstrated by [PERSON] et al. in their work with InChorus [88]. We think that the latter work paves the way to V-NLIs similar to those found in the field of mixed-initiative (human-machine collaboration) Procedulent Content Generation [121]. Furthermore, recent advances in chatbot technology, such as ChatGPT-4 [122], demonstrate its ability to respond to visual queries. We firmly believe that these advances can also be applied to the field of data visualisation. For example, users can ask the chatbot to show them a visualisation of a particular layout by sending an image showing the desired layout. In fact, a recent study has focused on creating data visualisations using Natural Language with ChatGPT-3 and GPT-3.5 [83]. The study proposed using Large Language Models (LLMs) to create data visualisations from tabular data with basic visualisation methods. The system is able to select the appropriate visualisation type based on user queries. However, these advances come with several challenges, such as difficulties in specifying refinements to plotting elements, variability in the type of plot generated, and their non-deterministic nature. Given the fact that none of the works reviewed in this scoping review use NL interactions to change symbolic Graphical Elements, such as glyphs and colour palettes, these generative approaches can potentially be used to generate them during analytical conversations. Remark 5: _Recent Generative AI models can potentially be used to generate visual layouts and graphical elements._  
Finally, regarding the spider graph in Figure 11 (see turquoise arc), we found that most of the V-NLIs that offer multiple views are form-based and have basic visualisations. Two of them have only an NL input modality [64; 82]. However, others are multimodal, offering input modalities such as WIMP [39; 63; 70; 90] and touch [15; 84]. Additionally, some platforms allow the users to utilise two modalities simultaneously [15; 85]. Although multimodality can be beneficial for any kind of visualisation, whether basic or advanced, we think that exploring multimodality, especially with advanced visualisations, is a promising research topic since they represent complementary inputs and outputs to improve the expressiveness of users' intentions and, consequently, the user experience in V-NLIs. For instance, users can ask to zoom in on a region of nodes or select a cluster that is on the side by showing gestures or, in a VR environment, using a VR controller to point. Alternatively, users could request a zoom level where the data are most densely clustered, or ask the system to identify the places of data points on the visualisation (e.g., by asking _\"What's above the largest node?\"_ and then requesting further details). Additionally, graphic animations could be incorporated into the explanations provided by the system, enhancing the user's understanding of the data.