22 citations · 48 across the 15 of their papers we have counts for
4 papers · 2 filters
Do Models Explain Themselves? Counterfactual Simulatability of Natural Language Explanations
Yanda Chen, Ruiqi Zhong, Narutatsu Ri +5
Large language models (LLMs) are trained to imitate humans to explain human decisions. However, do LLMs explain themselves? Can they help humans build mental models of how LLMs pro…
RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations
Yilun Zhao, Chen Zhao, Linyong Nan +5
Despite significant progress having been made in question answering on tabular data (Table QA), it's unclear whether, and to what extent existing Table QA models are robust to task…
Getting MoRE out of Mixture of Language Model Reasoning Experts
Chenglei Si, Weijia Shi, Chen Zhao +2
While recent large language models (LLMs) improve on various question answering (QA) datasets, it remains difficult for a single model to generalize across question types that requ…
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Angelica Chen, Jason Phang, Alicia Parrish +4
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consist…