:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ray, Ruchira, Bhalani, Ruchi
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	https://arxiv.org/abs/2405.05418
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
by: Röttger, Paul, et al.
Published: (2023)

Defining Knowledge: Bridging Epistemology and Large Language Models
by: Fierro, Constanza, et al.
Published: (2024)

Evaluation Revisited: A Taxonomy of Evaluation Concerns in Natural Language Processing
by: Dhar, Ruchira, et al.
Published: (2026)

Beyond Over-Refusal: Scenario-Based Diagnostics and Post-Hoc Mitigation for Exaggerated Refusals in LLMs
by: Yuan, Shuzhou, et al.
Published: (2025)

Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models
by: Wichitwechkarn, Vijja, et al.
Published: (2025)

Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models
by: Schaeffer, Rylan, et al.
Published: (2025)

RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation
by: James, Joseph, et al.
Published: (2026)

Do Large Language Models Reflect Demographic Pluralism in Safety?
by: Naseem, Usman, et al.
Published: (2026)

Understanding and Mitigating Over-refusal for Large Language Models via Safety Representation
by: Zhang, Junbo, et al.
Published: (2025)

From Words to Worlds: Compositionality for Cognitive Architectures
by: Dhar, Ruchira, et al.
Published: (2024)

Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model
by: Lou, Xinyue, et al.
Published: (2025)

Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation
by: Neumann, Terrence, et al.
Published: (2024)

SafetyBench: Evaluating the Safety of Large Language Models
by: Zhang, Zhexin, et al.
Published: (2023)

Controlled Training Data Generation with Diffusion Models
by: Yeo, Teresa, et al.
Published: (2024)

Evaluating Adjective-Noun Compositionality in LLMs: Functional vs Representational Perspectives
by: Dhar, Ruchira, et al.
Published: (2026)

Mitigating the Bias of Large Language Model Evaluation
by: Zhou, Hongli, et al.
Published: (2024)

Mitigating Large Language Model Hallucination with Faithful Finetuning
by: Hu, Minda, et al.
Published: (2024)

Exploring and Mitigating Fawning Hallucinations in Large Language Models
by: Shangguan, Zixuan, et al.
Published: (2025)

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

When Models Outthink Their Safety: Unveiling and Mitigating Self-Jailbreak in Large Reasoning Models
by: Mao, Yingzhi, et al.
Published: (2025)

Mitigating Gambling-Like Risk-Taking Behaviors in Large Language Models: A Behavioral Economics Approach to AI Safety
by: Du, Y.
Published: (2025)

Unraveling and Mitigating Safety Alignment Degradation of Vision-Language Models
by: Liu, Qin, et al.
Published: (2024)

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing
by: Tran, Thien Q., et al.
Published: (2025)

Sycophancy in Large Language Models: Causes and Mitigations
by: Malmqvist, Lars
Published: (2024)

An Evolutionary Large Language Model for Hallucination Mitigation
by: Boulesnane, Abdennour, et al.
Published: (2024)

Evaluating Psychological Safety of Large Language Models
by: Li, Xingxuan, et al.
Published: (2022)

Do Multilingual Large Language Models Mitigate Stereotype Bias?
by: Nie, Shangrui, et al.
Published: (2024)

Mitigating the Risk of Health Inequity Exacerbated by Large Language Models
by: Ji, Yuelyu, et al.
Published: (2024)

Detection, Classification, and Mitigation of Gender Bias in Large Language Models
by: Cheng, Xiaoqing, et al.
Published: (2025)

Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching
by: Zhao, Weixiang, et al.
Published: (2024)

Is Safety Standard Same for Everyone? User-Specific Safety Evaluation of Large Language Models
by: In, Yeonjun, et al.
Published: (2025)

Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models
by: Nie, Ercong, et al.
Published: (2025)

LongSafety: Evaluating Long-Context Safety of Large Language Models
by: Lu, Yida, et al.
Published: (2025)

Locating and Mitigating Gender Bias in Large Language Models
by: Cai, Yuchen, et al.
Published: (2024)

Bias in Large Language Models: Origin, Evaluation, and Mitigation
by: Guo, Yufei, et al.
Published: (2024)

Mitigating Entity-Level Hallucination in Large Language Models
by: Su, Weihang, et al.
Published: (2024)

Copy-Paste to Mitigate Large Language Model Hallucinations
by: Long, Yongchao, et al.
Published: (2025)

Theoretical Foundations and Mitigation of Hallucination in Large Language Models
by: Gumaan, Esmail
Published: (2025)

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models
by: Zhu, Didi, et al.
Published: (2024)

KZ-SafetyPrompts: A Kazakh Safety Evaluation Prompt Dataset for Large Language Models
by: Zaghouani, Wajdi, et al.
Published: (2026)