This paper presents a systematic review of benchmarks and approaches for explainability in
Machine Reading Comprehension (MRC). We present how the representation and …

Regenerating natural language explanations in the scientific domain has been proposed as
a benchmark to evaluate complex multi-hop and explainable inference. In this context, large …

Machine Reading Comprehension (MRC) is the task of answering a question over a paragraph
of text. While neural MRC systems gain popularity and achieve noticeable performance, …

Probing (or diagnostic classification) has become a popular strategy for investigating whether
a given set of intermediate features is present in the representations of neural models. …

Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in
dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs.…

This paper describes the results of SemEval 2023 task 7 -- Multi-Evidence Natural Language
Inference for Clinical Trial Data (NLI4CT) -- consisting of 2 tasks, a Natural Language …

One of the fundamental research goals for explanation-based Natural Language Inference (NLI)
is to build models that can reason in complex domains through the generation of natural …

This paper investigates the possibility of approximating multiple mathematical operations in
latent space for expression derivation. To this end, we introduce different multi-operational …

This paper presents a novel framework for reconstructing multi-hop explanations in science
Question Answering (QA). While existing approaches for multi-hop reasoning build …

Recent studies on logical reasoning in auto-regressive Language Models (LMs) have sparked
a debate on whether such models can learn systematic reasoning principles during pre-…