From the 1 of 6 linked papers with an AI index.
1 citations · 1 across the 2 of their papers we have counts for
6 papers
Egocentric Bias in Vision-Language Models
Maijunxian Wang, Yijiang Li, Bingyang Wang +6
The paper introduces FlipSet, a benchmark that tests vision‑language models on Level‑2 visual perspective taking by requiring them to mentally rotate 2D character strings, and find…
A Unified Definition of Hallucination: It's The World Model, Stupid!
Emmy Liu, Varun Gangal, Chelsea Zou +7
Despite numerous attempts at mitigation since the inception of language models, hallucinations remain a persistent problem even in today's frontier LLMs. Why is this? We review exi…
HalluWorld: A Controlled Benchmark for Hallucination via Reference World Models
Emmy Liu, Varun Gangal, Michael Yu +4
Hallucination remains a central failure mode of large language models, but existing benchmarks operationalize it inconsistently across summarization, question answering, retrieval-…
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions
Emmy Liu, Amanda Bertsch, Lintang Sutawika +9
Improvements in language model capabilities are often attributed to increasing model size or training data, but in some cases smaller models trained on curated data or with differe…
Datasheets Aren't Enough: DataRubrics for Automated Quality Metrics and Accountability
Genta Indra Winata, David Anugraha, Emmy Liu +17
High-quality datasets are fundamental to training and evaluating machine learning models, yet their creation-especially with accurate human annotations-remains a significant challe…
Divergences between Language Models and Human Brains
Yuchen Zhou, Emmy Liu, Graham Neubig +2
Do machines and humans process language in similar ways? Recent research has hinted at the affirmative, showing that human neural activity can be effectively predicted using the in…