Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shaier, Sagi, Hunter, Lawrence E, von der Wense, Katharina
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Desiderata for the Context Use of Question Answering Systems
by: Shaier, Sagi, et al.
Published: (2024)

Who Are All The Stochastic Parrots Imitating? They Should Tell Us!
by: Shaier, Sagi, et al.
Published: (2023)

Comparing Template-based and Template-free Language Model Probing
by: Shaier, Sagi, et al.
Published: (2024)

MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset
by: Shaier, Sagi, et al.
Published: (2024)

Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QA
by: Baker, George Arthur, et al.
Published: (2024)

Asking Again and Again: Exploring LLM Robustness to Repeated Questions
by: Shaier, Sagi, et al.
Published: (2024)

EgoPrivacy: What Your First-Person Camera Says About You?
by: Li, Yijiang, et al.
Published: (2025)

You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks
by: Gaznepoglu, Ünal Ege, et al.
Published: (2025)

LLMs Know More About Numbers than They Can Say
by: Yuchi, Fengting, et al.
Published: (2026)

More Experts Than Galaxies: Conditionally-overlapping Experts With Biologically-Inspired Fixed Routing
by: Shaier, Sagi, et al.
Published: (2024)

You Know What I'm Saying: Jailbreak Attack via Implicit Reference
by: Wu, Tianyu, et al.
Published: (2024)

Only Say What You Know: Calibration-Aware Generation for Long-Form Factuality
by: Luo, Wen, et al.
Published: (2026)

Improving Low-Resource Morphological Inflection via Self-Supervised Objectives
by: Wiemerslage, Adam, et al.
Published: (2025)

Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
by: Tang, Chenming, et al.
Published: (2024)

What do Large Language Models Say About Animals? Investigating Risks of Animal Harm in Generated Text
by: Kanepajs, Arturs, et al.
Published: (2025)

Say What You Mean: Natural Language Access Control with Large Language Models for Internet of Things
by: Cheng, Ye, et al.
Published: (2025)

Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations
by: Shaier, Sagi, et al.
Published: (2024)

Knowing Before Saying: LLM Representations Encode Information About Chain-of-Thought Success Before Completion
by: Afzal, Anum, et al.
Published: (2025)

You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases
by: Gisler, Isaia, et al.
Published: (2026)

Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
by: Li, Zhuohang, et al.
Published: (2024)

The Trade-off between Performance, Efficiency, and Fairness in Adapter Modules for Text Classification
by: Bui, Minh Duc, et al.
Published: (2024)

Mitigating Label Length Bias in Large Language Models
by: Sanz-Guerrero, Mario, et al.
Published: (2025)

Corrective In-Context Learning: Evaluating Self-Correction in Large Language Models
by: Sanz-Guerrero, Mario, et al.
Published: (2025)

Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentation
by: Rice, Enora, et al.
Published: (2025)

Model-Based Ranking of Source Languages for Zero-Shot Cross-Lingual Transfer
by: Ebrahimi, Abteen, et al.
Published: (2025)

Reading Users' Minds from What They Say: An Investigation into LLM-based Empathic Mental Inference
by: Zhu, Qihao, et al.
Published: (2024)

What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics
by: Parker, Michael J., et al.
Published: (2026)

Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision-Language Models
by: Bui, Minh Duc, et al.
Published: (2024)

CLIX: Cross-Lingual Explanations of Idiomatic Expressions
by: Gluck, Aaron, et al.
Published: (2025)

Large Language Models Know What To Say But Not When To Speak
by: Umair, Muhammad, et al.
Published: (2024)

Mind the Gap: A Closer Look at Tokenization for Multiple-Choice Question Answering with LLMs
by: Sanz-Guerrero, Mario, et al.
Published: (2025)

Aligning What LLMs Do and Say: Towards Self-Consistent Explanations
by: Admoni, Sahar, et al.
Published: (2025)

HearSay Benchmark: Do Audio LLMs Leak What They Hear?
by: Wang, Jin, et al.
Published: (2026)

Do What I Say: A Spoken Prompt Dataset for Instruction-Following
by: Züfle, Maike, et al.
Published: (2026)

It's Not Just What You Say, but How You Say It: The Effects of Enterprise Social Media on Service Management, Through the Lens of Signaling Theory
by: Alexandra Budjanovcanin, et al.
Published: (2025)

Reasoning Models Don't Always Say What They Think
by: Chen, Yanda, et al.
Published: (2025)

What You Say Predicts How You Do: A Multilevel Analysis of Corporate Environmental Performance
by: C. José García, et al.
Published: (2026)

Seeing What You Say: Expressive Image Generation from Speech
by: Lee, Jiyoung, et al.
Published: (2025)

Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection
by: Reuver, Myrthe, et al.
Published: (2025)

When Words Don't Mean What They Say: Figurative Understanding in Bengali Idioms
by: Sakhawat, Adib, et al.
Published: (2026)