Computer Science > Human-Computer Interaction
arXiv:2309.14459 (cs)
[Submitted on 25 Sep 2023 (v1), last revised 18 Mar 2024 (this version, v2)]
Title:Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces
View a PDF of the paper titled Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces, by Hariharan Subramonyam and 4 other authors
View PDFHTML (experimental)Abstract:Large language models (LLMs) exhibit dynamic capabilities and appear to comprehend complex and ambiguous natural language prompts. However, calibrating LLM interactions is challenging for interface designers and end-users alike. A central issue is our limited grasp of how human cognitive processes begin with a goal and form intentions for executing actions, a blindspot even in established interaction models such as Norman's gulfs of execution and evaluation. To address this gap, we theorize how end-users 'envision' translating their goals into clear intentions and craft prompts to obtain the desired LLM response. We define a process of Envisioning by highlighting three misalignments: (1) knowing whether LLMs can accomplish the task, (2) how to instruct the LLM to do the task, and (3) how to evaluate the success of the LLM's output in meeting the goal. Finally, we make recommendations to narrow the envisioning gulf in human-LLM interactions.
| Subjects: | Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2309.14459 [cs.HC] |
| (orarXiv:2309.14459v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2309.14459 arXiv-issued DOI via DataCite |
Submission history
From: Hariharan Subramonyam [view email][v1] Mon, 25 Sep 2023 18:46:37 UTC (4,072 KB)
[v2] Mon, 18 Mar 2024 19:59:14 UTC (15,961 KB)
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfaces, by Hariharan Subramonyam and 4 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.