14 citations · 18 across the 13 of their papers we have counts for
14 papers
Perspectives on Cross-Lingual Consistency in LLMs for Medical Questions
Minh Duc Bui, Mario Sanz-Guerrero, Abteen Ebrahimi +4
Should multilingual LLMs answer medical questions consistently across input languages, or adapt responses to cultural cues? Existing multilingual medical benchmarks usually assume…
Excitation: Momentum For Experts
Sagi Shaier
We propose Excitation, a novel optimization framework designed to accelerate learning in sparse architectures such as Mixture-of-Experts (MoEs). Unlike traditional optimizers that…
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…
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…
Asking Again and Again: Exploring LLM Robustness to Repeated Questions
Sagi Shaier, Mario Sanz-Guerrero, Katharina von der Wense
This study investigates whether repeating questions within prompts influences the performance of large language models (LLMs). We hypothesize that reiterating a question within a s…
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations
Sagi Shaier, Ari Kobren, Philip Ogren
Resolving knowledge conflicts is a crucial challenge in Question Answering (QA) tasks, as the internet contains numerous conflicting facts and opinions. While some research has mad…
More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing
Sagi Shaier, Francisco Pereira, Katharina von der Wense +2
The evolution of biological neural systems has led to both modularity and sparse coding, which enables energy efficiency and robustness across the diversity of tasks in the lifespa…
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…
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…
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…
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems
Sagi Shaier, Kevin Bennett, Lawrence Hunter +1
State-of-the-art question answering (QA) models exhibit a variety of social biases (e.g., with respect to sex or race), generally explained by similar issues in their training data…
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…
Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems
Sagi Shaier, Lawrence Hunter, Katharina Kann
Many dialogue systems (DSs) lack characteristics humans have, such as emotion perception, factuality, and informativeness. Enhancing DSs with knowledge alleviates this problem, but…
Data-driven approaches for predicting spread of infectious diseases through DINNs: Disease Informed Neural Networks
Sagi Shaier, Maziar Raissi, Padmanabhan Seshaiyer
In this work, we present an approach called Disease Informed Neural Networks (DINNs) that can be employed to effectively predict the spread of infectious diseases. This approach bu…