5 citations · 14 across the 15 of their papers we have counts for
13 papers · 1 filter
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-…
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…
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…
Language Modeling with Editable External Knowledge
Belinda Z. Li, Emmy Liu, Alexis Ross +3
When the world changes, so does the text that humans write about it. How do we build language models that can be easily updated to reflect these changes? One popular approach is re…
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…
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting
Emmy Liu, Aditi Chaudhary, Graham Neubig
Idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. Despite significant advance…