Enabling conversational interplay on cellular with LLMs – Google AI Weblog

Clever assistants on cellular units have considerably superior language-based interactions for performing easy each day duties, akin to setting a timer or turning on a flashlight. Regardless of the progress, these assistants nonetheless face limitations in supporting conversational interactions in cellular person interfaces (UIs), the place many person duties are carried out. For instance, they can not reply a person’s query about particular data displayed on a display screen. An agent would want to have a computational understanding of graphical user interfaces (GUIs) to attain such capabilities.

Prior analysis has investigated a number of vital technical constructing blocks to allow conversational interplay with cellular UIs, together with summarizing a mobile screen for customers to rapidly perceive its goal, mapping language directions to UI actions and modeling GUIs in order that they’re extra amenable for language-based interplay. Nonetheless, every of those solely addresses a restricted facet of conversational interplay and requires appreciable effort in curating large-scale datasets and coaching devoted fashions. Moreover, there’s a broad spectrum of conversational interactions that may happen on cellular UIs. Due to this fact, it’s crucial to develop a light-weight and generalizable strategy to appreciate conversational interplay.

In “Enabling Conversational Interaction with Mobile UI using Large Language Models”, introduced at CHI 2023, we examine the viability of using giant language fashions (LLMs) to allow numerous language-based interactions with cellular UIs. Current pre-trained LLMs, akin to PaLM, have demonstrated talents to adapt themselves to varied downstream language duties when being prompted with a handful of examples of the goal job. We current a set of prompting methods that allow interplay designers and builders to rapidly prototype and check novel language interactions with customers, which saves time and sources earlier than investing in devoted datasets and fashions. Since LLMs solely take textual content tokens as enter, we contribute a novel algorithm that generates the textual content illustration of cellular UIs. Our outcomes present that this strategy achieves aggressive efficiency utilizing solely two information examples per job. Extra broadly, we display LLMs’ potential to basically rework the long run workflow of conversational interplay design.

Animation exhibiting our work on enabling varied conversational interactions with cellular UI utilizing LLMs.

Prompting LLMs with UIs

LLMs help in-context few-shot studying by way of prompting — as a substitute of fine-tuning or re-training fashions for every new job, one can immediate an LLM with a couple of enter and output information exemplars from the goal job. For a lot of pure language processing duties, akin to question-answering or translation, few-shot prompting performs competitively with benchmark approaches that practice a mannequin particular to every job. Nonetheless, language fashions can solely take textual content enter, whereas cellular UIs are multimodal, containing textual content, picture, and structural data of their view hierarchy information (i.e., the structural information containing detailed properties of UI components) and screenshots. Furthermore, instantly inputting the view hierarchy information of a cellular display screen into LLMs just isn’t possible because it incorporates extreme data, akin to detailed properties of every UI aspect, which may exceed the enter size limits of LLMs.

To handle these challenges, we developed a set of methods to immediate LLMs with cellular UIs. We contribute an algorithm that generates the textual content illustration of cellular UIs utilizing depth-first search traversal to transform the Android UI’s view hierarchy into HTML syntax. We additionally make the most of chain of thought prompting, which entails producing intermediate outcomes and chaining them collectively to reach on the last output, to elicit the reasoning capacity of the LLM.

Animation exhibiting the method of few-shot prompting LLMs with cellular UIs.

Our immediate design begins with a preamble that explains the immediate’s goal. The preamble is adopted by a number of exemplars consisting of the enter, a sequence of thought (if relevant), and the output for every job. Every exemplar’s enter is a cellular display screen within the HTML syntax. Following the enter, chains of thought could be offered to elicit logical reasoning from LLMs. This step just isn’t proven within the animation above as it’s non-obligatory. The duty output is the specified consequence for the goal duties, e.g., a display screen abstract or a solution to a person query. Few-shot prompting could be achieved with a couple of exemplar included within the immediate. Throughout prediction, we feed the mannequin the immediate with a brand new enter display screen appended on the finish.


We carried out complete experiments with 4 pivotal modeling duties: (1) display screen question-generation, (2) display screen summarization, (3) display screen question-answering, and (4) mapping instruction to UI motion. Experimental outcomes present that our strategy achieves aggressive efficiency utilizing solely two information examples per job.

Job 1: Display query era

Given a cellular UI display screen, the objective of display screen question-generation is to synthesize coherent, grammatically right pure language questions related to the UI components requiring person enter.

We discovered that LLMs can leverage the UI context to generate questions for related data. LLMs considerably outperformed the heuristic strategy (template-based era) concerning query high quality.

Instance display screen questions generated by the LLM. The LLM can make the most of display screen contexts to generate grammatically right questions related to every enter subject on the cellular UI, whereas the template strategy falls brief.

We additionally revealed LLMs’ capacity to mix related enter fields right into a single query for environment friendly communication. For instance, the filters asking for the minimal and most value have been mixed right into a single query: “What’s the value vary?

We noticed that the LLM may use its prior information to mix a number of associated enter fields to ask a single query.

In an analysis, we solicited human rankings on whether or not the questions have been grammatically right (Grammar) and related to the enter fields for which they have been generated (Relevance). Along with the human-labeled language high quality, we mechanically examined how effectively LLMs can cowl all the weather that have to generate questions (Protection F1). We discovered that the questions generated by LLM had virtually excellent grammar (4.98/5) and have been extremely related to the enter fields displayed on the display screen (92.8%). Moreover, LLM carried out effectively when it comes to masking the enter fields comprehensively (95.8%).

      Template       2-shot LLM      
Grammar       3.6 (out of 5)       4.98 (out of 5)      
Relevance       84.1%       92.8%      
Protection F1       100%       95.8%      

Job 2: Display summarization

Display summarization is the automated era of descriptive language overviews that cowl important functionalities of cellular screens. The duty helps customers rapidly perceive the aim of a cellular UI, which is especially helpful when the UI just isn’t visually accessible.

Our outcomes confirmed that LLMs can successfully summarize the important functionalities of a cellular UI. They will generate extra correct summaries than the Screen2Words benchmark mannequin that we beforehand launched utilizing UI-specific textual content, as highlighted within the coloured textual content and bins under.

Instance abstract generated by 2-shot LLM. We discovered the LLM is ready to use particular textual content on the display screen to compose extra correct summaries.

Curiously, we noticed LLMs utilizing their prior information to infer data not introduced within the UI when creating summaries. Within the instance under, the LLM inferred the subway stations belong to the London Tube system, whereas the enter UI doesn’t comprise this data.

LLM makes use of its prior information to assist summarize the screens.

Human analysis rated LLM summaries as extra correct than the benchmark, but they scored decrease on metrics like BLEU. The mismatch between perceived high quality and metric scores echoes recent work exhibiting LLMs write higher summaries regardless of automated metrics not reflecting it.


Left: Display summarization efficiency on automated metrics. Proper: Display summarization accuracy voted by human evaluators.

Job 3: Display question-answering

Given a cellular UI and an open-ended query asking for data concerning the UI, the mannequin ought to present the proper reply. We concentrate on factual questions, which require solutions primarily based on data introduced on the display screen.

Instance outcomes from the display screen QA experiment. The LLM considerably outperforms the off-the-shelf QA baseline mannequin.

We report efficiency utilizing 4 metrics: Actual Matches (similar predicted reply to floor fact), Incorporates GT (reply absolutely containing floor fact), Sub-String of GT (reply is a sub-string of floor fact), and the Micro-F1 rating primarily based on shared phrases between the anticipated reply and floor fact throughout your entire dataset.

Our outcomes confirmed that LLMs can accurately reply UI-related questions, akin to “what is the headline?”. The LLM carried out considerably higher than baseline QA mannequin DistillBERT, attaining a 66.7% absolutely right reply fee. Notably, the 0-shot LLM achieved an actual match rating of 30.7%, indicating the mannequin’s intrinsic query answering functionality.

Fashions       Actual Matches       Incorporates GT       Sub-String of GT       Micro-F1      
0-shot LLM       30.7%       6.5%       5.6%       31.2%      
1-shot LLM       65.8%       10.0%       7.8%       62.9%      
2-shot LLM       66.7%       12.6%       5.2%       64.8%      
DistillBERT       36.0%       8.5%       9.9%       37.2%      

Job 4: Mapping instruction to UI motion

Given a cellular UI display screen and pure language instruction to regulate the UI, the mannequin must predict the ID of the thing to carry out the instructed motion. For instance, when instructed with “Open Gmail,” the mannequin ought to accurately determine the Gmail icon on the house display screen. This job is beneficial for controlling cellular apps utilizing language enter akin to voice entry. We launched this benchmark job beforehand.

Instance utilizing information from the PixelHelp dataset. The dataset incorporates interplay traces for frequent UI duties akin to turning on wifi. Every hint incorporates a number of steps and corresponding directions.

We assessed the efficiency of our strategy utilizing the Partial and Full metrics from the Seq2Act paper. Partial refers back to the share of accurately predicted particular person steps, whereas Full measures the portion of precisely predicted total interplay traces. Though our LLM-based methodology didn’t surpass the benchmark educated on huge datasets, it nonetheless achieved exceptional efficiency with simply two prompted information examples.

Fashions       Partial       Full      
0-shot LLM       1.29       0.00      
1-shot LLM (cross-app)       74.69       31.67      
2-shot LLM (cross-app)       75.28       34.44      
1-shot LLM (in-app)       78.35       40.00      
2-shot LLM (in-app)       80.36       45.00      
Seq2Act       89.21       70.59      

Takeaways and conclusion

Our research reveals that prototyping novel language interactions on cellular UIs could be as simple as designing an information exemplar. Consequently, an interplay designer can quickly create functioning mock-ups to check new concepts with finish customers. Furthermore, builders and researchers can discover completely different potentialities of a goal job earlier than investing important efforts into creating new datasets and fashions.

We investigated the feasibility of prompting LLMs to allow varied conversational interactions on cellular UIs. We proposed a collection of prompting methods for adapting LLMs to cellular UIs. We carried out intensive experiments with the 4 vital modeling duties to guage the effectiveness of our strategy. The outcomes confirmed that in comparison with conventional machine studying pipelines that consist of costly information assortment and mannequin coaching, one may quickly notice novel language-based interactions utilizing LLMs whereas attaining aggressive efficiency.


We thank our paper co-author Gang Li, and respect the discussions and suggestions from our colleagues Chin-Yi Cheng, Tao Li, Yu Hsiao, Michael Terry and Minsuk Chang. Particular because of Muqthar Mohammad and Ashwin Kakarla for his or her invaluable help in coordinating information assortment. We thank John Guilyard for serving to create animations and graphics within the weblog.

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