Interpretable LLM-based Table Question Answering
Interpretability for Table Question Answering (Table QA) is critical, particularly in high-stakes industries like finance or healthcare. Although recent approaches...
NLP
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate nonfactual statements. A response mixed of factual...
B-score: Detecting biases in large language models using response history
Large language models (LLMs) often exhibit strong biases, e.g, against women or in favor of the number 7. We investigate...
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
* Equal contribution. CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder....
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search
Since BERT (Devlin et al., 2018), learning contextualized word embeddings has been a de-facto standard in NLP. However, the progress...
Double Trouble: How to not explain a text classifier’s decisions using counterfactuals synthesized by masked language models
Explaining how important each input feature is to a classifier’s decision is critical in high-stake applications. An underlying principle behind...
Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?
Do state-of-the-art natural language understanding models care about word order – one of the most important characteristics of a sequence?...