565 citations · 654 across the 13 of their papers we have counts for
5 papers · 1 filter
Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck
Josh Magnus Ludan, Qing Lyu, Yue Yang +3
Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability. To address this, we propose…
Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Josh Magnus Ludan, Yixuan Meng, Tai Nguyen +4
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on o…
Representation Of Lexical Stylistic Features In Language Models' Embedding Space
Qing Lyu, Marianna Apidianaki, Chris Callison-Burch
The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e.g., similarity, hypernymy, polysemy) as well as abstrac…
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Chuang Liu, Renren Jin, Yuqi Ren +10
Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capabil…
Faithful Chain-of-Thought Reasoning
Qing Lyu, Shreya Havaldar, Adam Stein +5
While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect ho…