47 citations · 53 across the 6 of their papers we have counts for
8 papers
Whose turn is it? Context-guided model routing for stateful agents
Anikesh Parashar, Avik Dutta, Vashu Chauhan +6
Model routing can reduce the cost of LLM applications by assigning simpler requests to cheaper models without sacrificing the quality of the response. However, most routers treat r…
Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages
Nick McKenna, Xinnuo Xu, Jack Williams +3
A key consideration when training an LLM is whether the target language is more or less resourced, for example English compared to Welsh, or Python compared to Excel. Typical train…
Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for Comprehension Tasks
Ian Drosos, Jack Williams, Advait Sarkar +1
Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code,…
Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition
Majeed Kazemitabaar, Jack Williams, Ian Drosos +4
LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing…
Solving Data-centric Tasks using Large Language Models
Shraddha Barke, Christian Poelitz, Carina Suzana Negreanu +10
Large language models (LLMs) are rapidly replacing help forums like StackOverflow, and are especially helpful for non-professional programmers and end users. These users are often…
Participatory prompting: a user-centric research method for eliciting AI assistance opportunities in knowledge workflows
Advait Sarkar, Ian Drosos, Rob Deline +5
Generative AI, such as image generation models and large language models, stands to provide tremendous value to end-user programmers in creative and knowledge workflows. Current re…
Co-audit: tools to help humans double-check AI-generated content
Andrew D. Gordon, Carina Negreanu, José Cambronero +9
Users are increasingly being warned to check AI-generated content for correctness. Still, as LLMs (and other generative models) generate more complex output, such as summaries, tab…
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model
Carina Negreanu, Alperen Karaoglu, Jack Williams +4
Row completion is the task of augmenting a given table of text and numbers with additional, relevant rows. The task divides into two steps: subject suggestion, the task of populati…