Large Language Models (LMs) have achieved state-of-the-art performance on many Natural
Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we …

Language models (LMs) can perform complex reasoning either end-to-end, with hidden
latent state, or compositionally, with transparent intermediate state. Composition offers …

The rise of the term" mechanistic interpretability" has accompanied increasing interest in
understanding neural models--particularly language models. However, this jargon has also …

When the initial vision of Explainable (XAI) was articulated, the most popular framing was to
open the (proverbial)“black-box” of AI so that we could understand the inner workings. With …

Is explainability a false promise? This debate has emerged from the insufficient evidence
that explanations help people in situations they are introduced for. More human-centered …

Over 13 million Americans aged 65 and older are currently living with a diagnosis of mild
cognitive impairment (MCI), a common precursor to dementia. These individuals largely rely …

Knowledge Graph Question-Answering (KGQA) has gained popularity as an effective
approach for information retrieval systems. However, answering complex questions …

Multi-hop reading comprehension generally requires the model to give the answer and
complete the prediction of supporting facts. However, previous works mainly focus on the …

Humans have the remarkable ability to solve different tasks by simply reading textual
instructions that define the tasks and looking at a few examples. Natural Language …

Cross domain reasoning has garnered significant attention as a cutting-edge yet
challenging research area with numerous practical applications. On the other hand …