2 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
No One Size Fits All: QueryBandits for Hallucination Mitigation
Nicole Cho, William Watson, Alec Koppel +2
Advanced reasoning capabilities in Large Language Models (LLMs) have led to more frequent hallucinations; yet most mitigation work focuses on open-source models for post-hoc detect…
What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM Performance
William Watson, Nicole Cho, Sumitra Ganesh +1
Large Language Model (LLM) hallucinations are usually treated as defects of the model or its decoding strategy. Drawing on classical linguistics, we argue that a query's form can a…
QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting
Nicole Cho, William Watson, Alec Koppel +2
Advanced reasoning capabilities in Large Language Models (LLMs) have caused higher hallucination prevalence; yet most mitigation work focuses on after-the-fact filtering rather tha…
MultiQ&A: An Analysis in Measuring Robustness via Automated Crowdsourcing of Question Perturbations and Answers
Nicole Cho, William Watson
One critical challenge in the institutional adoption journey of Large Language Models (LLMs) stems from their propensity to hallucinate in generated responses. To address this, we…
FlowMind: Automatic Workflow Generation with LLMs
Zhen Zeng, William Watson, Nicole Cho +4
The rapidly evolving field of Robotic Process Automation (RPA) has made significant strides in automating repetitive processes, yet its effectiveness diminishes in scenarios requir…