:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Parker, Michael J., Zavala-Cerna, Maria G.
Format:	Preprint
Published:	2026
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2605.00294
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

If You Don't Understand It, Don't Use It: Eliminating Trojans with Filters Between Layers
by: Hernandez, Adriano
Published: (2024)

Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models
by: Tyukin, Georgy, et al.
Published: (2024)

Large Language Models Must Be Taught to Know What They Don't Know
by: Kapoor, Sanyam, et al.
Published: (2024)

Your Students Don't Use LLMs Like You Wish They Did
by: Kobler, Sebastian, et al.
Published: (2026)

Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users
by: Balepur, Nishant, et al.
Published: (2026)

What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts
by: Yang, Chenyang, et al.
Published: (2025)

Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
by: Yu, Zhiyuan, et al.
Published: (2024)

Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes
by: Landi, Isotta, et al.
Published: (2026)

Don't Shoot The Breeze: Topic Continuity Model Using Nonlinear Naive Bayes With Attention
by: Pi, Shu-Ting, et al.
Published: (2026)

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

EpiCaR: Knowing What You Don't Know Matters for Better Reasoning in LLMs
by: Yeom, Jewon, et al.
Published: (2026)

Show, Don't Tell: Evaluating Large Language Models Beyond Textual Understanding with ChildPlay
by: de Carvalho, Gonçalo Hora, et al.
Published: (2024)

Don't Believe Everything You Read: Enhancing Summarization Interpretability through Automatic Identification of Hallucinations in Large Language Models
by: Vakharia, Priyesh, et al.
Published: (2023)

Hatevolution: What Static Benchmarks Don't Tell Us
by: Di Bonaventura, Chiara, et al.
Published: (2025)

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

Remask, Don't Replace: Token-to-Mask Refinement in Diffusion Large Language Models
by: Yao, Lin
Published: (2026)

R-Tuning: Instructing Large Language Models to Say `I Don't Know'
by: Zhang, Hanning, et al.
Published: (2023)

Think, But Don't Overthink: Reproducing Recursive Language Models
by: Wang, Daren
Published: (2026)

LLM-based Cognitive Models of Students with Misconceptions
by: Sonkar, Shashank, et al.
Published: (2024)

Language Models Don't Learn the Physical Manifestation of Language
by: Lee, Bruce W., et al.
Published: (2024)

What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization
by: Rezaeimanesh, Sara, et al.
Published: (2026)

Can AI Assistants Know What They Don't Know?
by: Cheng, Qinyuan, et al.
Published: (2024)

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation
by: Hariri, Mohsen, et al.
Published: (2025)

Don't Take the Premise for Granted: Evaluating the Premise Critique Ability of Large Language Models
by: Li, Jinzhe, et al.
Published: (2025)

Don't Change My View: Ideological Bias Auditing in Large Language Models
by: Kröger, Paul, et al.
Published: (2025)

You Don't Need Prompt Engineering Anymore: The Prompting Inversion
by: Khan, Imran
Published: (2025)

Don't Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models
by: Bavaresco, A., et al.
Published: (2024)

"What Is It That You Don't Understand?" Language Games and Black Box Algorithms
by: Demichelis, Remy
Published: (2026)

Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
by: Chan, Brian J, et al.
Published: (2024)

Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMs
by: Goloburda, Maiya, et al.
Published: (2026)

Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models
by: Parmar, Jupinder, et al.
Published: (2024)

Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks
by: Chochlakis, Georgios, et al.
Published: (2024)

Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probing
by: Zhao, Raoyuan, et al.
Published: (2025)

Don't Throw Away Your Pretrained Model
by: Feng, Shangbin, et al.
Published: (2025)

Don't Pay Attention
by: Hammoud, Mohammad, et al.
Published: (2025)

Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
by: Lee, Jonggeun, et al.
Published: (2025)

Don't Touch My Diacritics
by: Gorman, Kyle, et al.
Published: (2024)

Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration
by: Feng, Shangbin, et al.
Published: (2024)

Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models
by: Huang, Zemin, et al.
Published: (2025)

Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes
by: Lucy, Li, et al.
Published: (2025)