5 citations · 5 across the 2 of their papers we have counts for
4 papers
From Binary Groundedness to Support Relations: Towards a Reader-Centred Taxonomy for Comprehension of AI Output
Advait Sarkar, Christian Poelitz, Viktor Kewenig
Generative AI tools often answer questions using source documents, e.g., through retrieval augmented generation. Current groundedness and hallucination evaluations largely frame th…
A Benchmark to Assess Common Ground in Human-AI Collaboration
Christian Poelitz, Finale Doshi-Velez, Siân Lindley
AI is becoming increasingly integrated into everyday life, both in professional work environments and in leisure and entertainment contexts. This integration requires AI to move be…
When Copilot Becomes Autopilot: Generative AI's Critical Risk to Knowledge Work and a Critical Solution
Advait Sarkar, Xiaotong, Xu +3
Generative AI, with its tendency to "hallucinate" incorrect results, may pose a risk to knowledge work by introducing errors. On the other hand, it may also provide unprecedented o…
Evaluating the Evaluator: Measuring LLMs' Adherence to Task Evaluation Instructions
Bhuvanashree Murugadoss, Christian Poelitz, Ian Drosos +5
LLMs-as-a-judge is a recently popularized method which replaces human judgements in task evaluation (Zheng et al. 2024) with automatic evaluation using LLMs. Due to widespread use…