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cs.CL2025

MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset

Sagi Shaier, George Arthur Baker, Chiranthan Sridhar +2

Language models (LMs) have excelled in various broad domains. However, to ensure their safe and effective integration into real-world educational settings, they must demonstrate pr…

cs.CL2024

Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA

George Arthur Baker, Ankush Raut, Sagi Shaier +2

Previous work finds that recent long-context language models fail to make equal use of information in the middle of their inputs, preferring pieces of information located at the ta…

cs.CL2024

Comparing Template-based and Template-free Language Model Probing

Sagi Shaier, Kevin Bennett, Lawrence E Hunter +1

The differences between cloze-task language model (LM) probing with 1) expert-made templates and 2) naturally-occurring text have often been overlooked. Here, we evaluate 16 differ…

cs.CL2024

It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension

Sagi Shaier, Lawrence E Hunter, Katharina von der Wense

Natural language processing has seen rapid progress over the past decade. Due to the speed of developments, some practices get established without proper evaluation. Considering on…

cs.CL2024

Desiderata for the Context Use of Question Answering Systems

Sagi Shaier, Lawrence E Hunter, Katharina von der Wense

Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts wi…

cs.CL2024

Who Are All The Stochastic Parrots Imitating? They Should Tell Us!

Sagi Shaier, Lawrence E. Hunter, Katharina von der Wense

Both standalone language models (LMs) as well as LMs within downstream-task systems have been shown to generate statements which are factually untrue. This problem is especially se…