:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Kavumba, Pride, Wataoka, Koki, Nguyen, Huy H., Li, Jiaxuan, Ohagi, Masaya
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Machine Learning
Online Access:	https://arxiv.org/abs/2604.03962
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Polarization of Autonomous Generative AI Agents Under Echo Chambers
by: Ohagi, Masaya
Published: (2024)

Don't Read Everything: A Curvature-Conditioned Query for Linear Attention
by: Le, Dong, et al.
Published: (2026)

Self-Preference Bias in LLM-as-a-Judge
by: Wataoka, Koki, et al.
Published: (2024)

Don't Trust: Verify -- Grounding LLM Quantitative Reasoning with Autoformalization
by: Zhou, Jin Peng, et al.
Published: (2024)

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

Why Don't Prompt-Based Fairness Metrics Correlate?
by: Zayed, Abdelrahman, et al.
Published: (2024)

Don't Walk the Line: Boundary Guidance for Filtered Generation
by: Ball, Sarah, et al.
Published: (2025)

LLM Cyber Evaluations Don't Capture Real-World Risk
by: Lukošiūtė, Kamilė, et al.
Published: (2025)

Reasoning Models Don't Just Think Longer, They Move Differently
by: Gjølbye, Anders, et al.
Published: (2026)

I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token
by: Cohen, Roi, et al.
Published: (2024)

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
by: Cao, Mingyu, et al.
Published: (2024)

Sample, Don't Search: Rethinking Test-Time Alignment for Language Models
by: Faria, Gonçalo, et al.
Published: (2025)

Don't Pay Attention, PLANT It: Pretraining Attention via Learning-to-Rank
by: Roy, Debjyoti Saha, et al.
Published: (2024)

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

Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation
by: Vernikos, Giorgos, et al.
Published: (2024)

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

Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
by: Dasgupta, Sayantan, et al.
Published: (2026)

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

Don't lie to your friends: Learning what you know from collaborative self-play
by: Eisenstein, Jacob, et al.
Published: (2025)

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

Don't throw away your value model! Generating more preferable text with Value-Guided Monte-Carlo Tree Search decoding
by: Liu, Jiacheng, et al.
Published: (2023)

Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
by: Jedidi, Nour, et al.
Published: (2025)

Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
by: Deng, Yang, et al.
Published: (2024)

Don't Throw Away Your Beams: Improving Consistency-based Uncertainties in LLMs via Beam Search
by: Fadeeva, Ekaterina, et al.
Published: (2025)

Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
by: Leemann, Tobias, et al.
Published: (2024)

The Compliance Gap: Why AI Systems Promise to Follow Process Instructions but Don't
by: Shin, Kwan Soo
Published: (2026)

Don't Blame the Annotator: Bias Already Starts in the Annotation Instructions
by: Parmar, Mihir, et al.
Published: (2022)

Revisiting Padded Transformer Expressivity: Which Architectural Choices Matter and Which Don't
by: Svete, Anej, et al.
Published: (2026)

LLMs Don't Know Their Own Decision Boundaries: The Unreliability of Self-Generated Counterfactual Explanations
by: Mayne, Harry, et al.
Published: (2025)

Don't Forget to Connect! Improving RAG with Graph-based Reranking
by: Dong, Jialin, et al.
Published: (2024)

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

Learn and Don't Forget: Adding a New Language to ASR Foundation Models
by: Qian, Mengjie, et al.
Published: (2024)

MergePrint: Merge-Resistant Fingerprints for Robust Black-box Ownership Verification of Large Language Models
by: Yamabe, Shojiro, et al.
Published: (2024)

GMTRouter: Personalized LLM Router over Multi-turn User Interactions
by: Xie, Encheng, et al.
Published: (2025)

MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
by: Hong, Dang Nguyen, et al.
Published: (2026)

Regress, Don't Guess -- A Regression-like Loss on Number Tokens for Language Models
by: Zausinger, Jonas, et al.
Published: (2024)

AcademicEval: Live Long-Context LLM Benchmark
by: Zhang, Haozhen, et al.
Published: (2025)

Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain
by: Jang, Eugene, et al.
Published: (2024)

AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
by: Schoenegger, Philipp, et al.
Published: (2024)