Cover Image

I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cohen, Roi, Dobler, Konstantin, Biran, Eden, de Melo, Gerard
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Language Adaptation on a Tight Academic Compute Budget: Tokenizer Swapping Works and Pure bfloat16 Is Enough
by: Dobler, Konstantin, et al.
Published: (2024)

Token Distillation: Attention-aware Input Embeddings For New Tokens
by: Dobler, Konstantin, et al.
Published: (2025)

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

FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
by: Dobler, Konstantin, et al.
Published: (2023)

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

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

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

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

Pretrained LLMs Learn Multiple Types of Uncertainty
by: Cohen, Roi, et al.
Published: (2025)

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

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

Don't Walk the Line: Boundary Guidance for Filtered Generation
by: Ball, Sarah, 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)

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

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
by: Mei, Zhiting, et al.
Published: (2025)

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

Beyond "I Don't Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty
by: Ren, Jingyi, et al.
Published: (2026)

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

Predict, Don't React: Value-Based Safety Forecasting for LLM Streaming
by: Kavumba, Pride, et al.
Published: (2026)

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

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)

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 Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation
by: Vernikos, Giorgos, et al.
Published: (2024)

Efficient Parallelization Layouts for Large-Scale Distributed Model Training
by: Hagemann, Johannes, et al.
Published: (2023)

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

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

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

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

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

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)

Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awareness
by: Peled-Cohen, Lotem, 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 lie to your friends: Learning what you know from collaborative self-play
by: Eisenstein, Jacob, et al.
Published: (2025)

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

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

Your LLM Knows the Future: Uncovering Its Multi-Token Prediction Potential
by: Samragh, Mohammad, et al.
Published: (2025)

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

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs
by: Johnson, Daniel D., et al.
Published: (2024)

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

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