# Chatbot-Based Natural Language Interfaces for Data Visualisation: A Scoping Review
## 5 Discussion
### RQ3: How Do Chatbot-Based V-NLIs Enhance the User's Interaction with the Visualisation? For this research question, we analysed the input (Table 4) and output (Table 5) characteristics of V-NLIs against the Interaction Space (seven interaction methods shown in Table 3). As can be appreciated in Figure 12, both chatbot-based and form-based approaches cover a similar, short range of interactive methods--Filtering and Selecting being the most covered--including some values near zero, especially with chatbot-based approaches(see the complex interactions Abstract/Elaborate [63], Connect [15; 80], Reconfigure [92], Explore [63] in yellow dots). This may be due to the difficulty of understanding when the user's intentions imply these complex interactions. Indeed, a recent study along these lines explores the use of a deep learning-based NL interpreter to translate NL utterances into editing actions, such as data operations (e.g., Filter, Aggregate), Encoding (e.g., changing colour, shape), and Reconfigure (e.g., position) [123]. Moreover, the emerging Large Language Models (LLM), which have proven their performance in various natural language tasks, open up new possibilities in Abstract and Elaborate interactions through step-by-step reasoning, such as the LLM Minerva developed by Google [124], which currently solves mathematical and scientific questions. Another interesting finding is a passive listening mode that allows the chatbot to observe conversations happening between users and automatically proposes Select or Filter methods accordingly [80]. In line with this, a recent study explored an always-listening agent that acts as a third collaborator in a multi-person visual analysis. The agent generates visualisations based on observations it makes from users' conversations [125]. We think that this idea of passive listening can be extended with other input signals, such as eye tracking [126] and emotional measures such as the tone of voice [127]. In collaborative scenarios, the chatbot may \"observe\" conversations happening between users and proactively propose the adequate interaction methods to perform users' tasks. Furthermore, although most V-NLIs support multiple input modalities (e.g., NL and WIMP or Touch), we did not encounter any V-NLI integrating VR and AR technologies. These technologies can easily provide additional inputs to the seven methods investigated, such as gaze, gestures and locations [128]. These technologies are not only important for input purposes, but also as additional means of enriching chatbot outputs, as in surround sounds, user's movements and haptic feedback using VR gloves or HMD (Head Mounted Displays). In fact, increasing the levels of immersion with multisensory stimulation has been demonstrated over the past decades to enhance data visual analysis tasks [108], although there is still room for improvement in terms of interactions with data visualisations. For instance, virtual teleportation is a common technique to guide users through data analysis in VREs. Teletransportation also could be used by the chatbot to situate the user near to the new generated visualisation that results from Select, Filter and Explore actions. In fact, multisensory output systems can also be exploited by chatbots in non-immersive environments. In our scoping review, regarding the sound feedback, we found only one  
Figure 12: Spider chart displaying the relationship between the type of V-NLIs and interaction methods. incipient experiment with promising results that uses speech instead of textual output [15]. Indeed, there is a recent study [14] that compared voice vs. screen-based conversational agents created for purposes other than visualisation analysis. It observed that pairs of participants working together tended to take more conversational turns when speaking with a chatbot directly than when the same conversation is conducted in a chat window. However, in the specific context of visual analysis, both output systems (screen and sound) offer complementary advantages.