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Found 3 papers, 2 papers with code
Fast and Accurate Factual Inconsistency Detection Over Long Documents
We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy.
Data-aware Low-Rank Compression for Large NLP Models
In this paper, we observe that the learned representation of each layer lies in a low-dimensional space.
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input.
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