4 citations · 5 across the 6 of their papers we have counts for
10 papers · 1 filter
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?
Peter Hase, Thomas Hofweber, Xiang Zhou +2
The model editing problem concerns how language models should learn new facts about the world over time. While empirical research on model editing has drawn widespread attention, t…
Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
Yichen Jiang, Xiang Zhou, Mohit Bansal
Transformers generalize to novel compositions of structures and entities after being trained on a complex dataset, but easily overfit on datasets of insufficient complexity. We obs…
Data Factors for Better Compositional Generalization
Xiang Zhou, Yichen Jiang, Mohit Bansal
Recent diagnostic datasets on compositional generalization, such as SCAN (Lake and Baroni, 2018) and COGS (Kim and Linzen, 2020), expose severe problems in models trained from scra…
ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness
Archiki Prasad, Swarnadeep Saha, Xiang Zhou +1
Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them. Most existing method…
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Yichen Jiang, Xiang Zhou, Mohit Bansal
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and ident…
Hidden Biases in Unreliable News Detection Datasets
Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos +2
Automatic unreliable news detection is a research problem with great potential impact. Recently, several papers have shown promising results on large-scale news datasets with model…