2.3k citations · 3.3k across the 58 of their papers we have counts for
68 papers
ContextRef: Evaluating Referenceless Metrics For Image Description Generation
Elisa Kreiss, Eric Zelikman, Christopher Potts +1
Referenceless metrics (e.g., CLIPScore) use pretrained vision--language models to assess image descriptions directly without costly ground-truth reference texts. Such methods can f…
Rigorously Assessing Natural Language Explanations of Neurons
Jing Huang, Atticus Geiger, Karel D'Oosterlinck +2
Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging.…
Context-VQA: Towards Context-Aware and Purposeful Visual Question Answering
Nandita Naik, Christopher Potts, Elisa Kreiss
Visual question answering (VQA) has the potential to make the Internet more accessible in an interactive way, allowing people who cannot see images to ask questions about them. How…
GIO: Gradient Information Optimization for Training Dataset Selection
Dante Everaert, Christopher Potts
It is often advantageous to train models on a subset of the available train examples, because the examples are of variable quality or because one would like to train with fewer exa…
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning
Jingyuan Selena She, Christopher Potts, Samuel R. Bowman +1
A number of recent benchmarks seek to assess how well models handle natural language negation. However, these benchmarks lack the controlled example paradigms that would allow us t…
MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
Zexuan Zhong, Zhengxuan Wu, Christopher D. Manning +2
The information stored in large language models (LLMs) falls out of date quickly, and retraining from scratch is often not an option. This has recently given rise to a range of tec…