:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Bavaresco, A., Testoni, A., Fernández, R.
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Artificial Intelligence
Online Access:	https://arxiv.org/abs/2405.20846
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions
by: Testoni, Alberto, et al.
Published: (2024)

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

Reasoning Models Reason Well, Until They Don't
by: Rameshkumar, Revanth, et al.
Published: (2025)

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

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

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

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
by: Testoni, Alberto, et al.
Published: (2026)

Do Retrieval Augmented Language Models Know When They Don't Know?
by: Zhou, Youchao, et al.
Published: (2025)

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

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

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

sDPO: Don't Use Your Data All at Once
by: Kim, Dahyun, et al.
Published: (2024)

Don't Think Twice! Over-Reasoning Impairs Confidence Calibration
by: Lacombe, Romain, et al.
Published: (2025)

The Accuracy Paradox in RLHF: When Better Reward Models Don't Yield Better Language Models
by: Chen, Yanjun, et al.
Published: (2024)

Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load
by: Mann, Logan, et al.
Published: (2025)

Don't Overthink it. Preferring Shorter Thinking Chains for Improved LLM Reasoning
by: Hassid, Michael, et al.
Published: (2025)

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

Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration
by: Mao, Xin, et al.
Published: (2024)

Don't Let It Fade: Preserving Edits in Diffusion Language Models via Token Timestep Allocation
by: Kim, Woojin, et al.
Published: (2025)

Don't Half-listen: Capturing Key-part Information in Continual Instruction Tuning
by: He, Yongquan, et al.
Published: (2024)

Align, Don't Divide: Revisiting the LoRA Architecture in Multi-Task Learning
by: Liu, Jinda, et al.
Published: (2025)

Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoning
by: Qin, Yuehan, et al.
Published: (2025)

Show, Don't Tell: Uncovering Implicit Character Portrayal using LLMs
by: Jaipersaud, Brandon, et al.
Published: (2024)

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

Attention Mechanisms Don't Learn Additive Models: Rethinking Feature Importance for Transformers
by: Leemann, Tobias, 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)

Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG
by: Chen, Xinxi, et al.
Published: (2024)

Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models
by: Yan, Shaotian, 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)

The BLA Benchmark: Investigating Basic Language Abilities of Pre-Trained Multimodal Models
by: Chen, Xinyi, et al.
Published: (2023)

s3: You Don't Need That Much Data to Train a Search Agent via RL
by: Jiang, Pengcheng, et al.
Published: (2025)

Don't Fine-Tune, Decode: Syntax Error-Free Tool Use via Constrained Decoding
by: Zhang, Kexun, et al.
Published: (2023)

Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
by: Lachenmaier, Clara, et al.
Published: (2025)

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)

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)

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

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"
by: Lee, Joosung, et al.
Published: (2026)

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

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