Cover Image

Small Edits, Big Consequences: Telling Good from Bad Robustness in Large Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ismailov, Altynbek, Asanova, Salia
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models?
by: Chen, Pinzhen, et al.
Published: (2024)

Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
by: Hu, Beizhe, et al.
Published: (2023)

Comparing Bad Apples to Good Oranges: Aligning Large Language Models via Joint Preference Optimization
by: Bansal, Hritik, et al.
Published: (2024)

Good Idea or Not, Representation of LLM Could Tell
by: Xu, Yi, et al.
Published: (2024)

When Bad Data Leads to Good Models
by: Li, Kenneth, et al.
Published: (2025)

Rethinking KenLM: Good and Bad Model Ensembles for Efficient Text Quality Filtering in Large Web Corpora
by: Kim, Yungi, et al.
Published: (2024)

Are Large Language Models Good Statisticians?
by: Zhu, Yizhang, et al.
Published: (2024)

Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
by: Yi, Zihao, et al.
Published: (2025)

Identifying Good and Bad Neurons for Task-Level Controllable LLMs
by: Li, Wenjie, et al.
Published: (2026)

To Tell The Truth: Language of Deception and Language Models
by: Hazra, Sanchaita, et al.
Published: (2023)

Are Large Language Models Good at Detecting Propaganda?
by: Jose, Julia, et al.
Published: (2025)

Ask Good Questions for Large Language Models
by: Wu, Qi, et al.
Published: (2025)

Are Large Language Models Good Prompt Optimizers?
by: Ma, Ruotian, et al.
Published: (2024)

The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism
by: Song, Yifan, et al.
Published: (2024)

Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
by: Li, Mingda, et al.
Published: (2026)

Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly
by: Méndez, Silvia García, et al.
Published: (2024)

The Good, The Bad, and Why: Unveiling Emotions in Generative AI
by: Li, Cheng, et al.
Published: (2023)

Guiding Large Language Models to Post-Edit Machine Translation with Error Annotations
by: Ki, Dayeon, et al.
Published: (2024)

FADE: Why Bad Descriptions Happen to Good Features
by: Puri, Bruno, et al.
Published: (2025)

Are Large Language Models Good Essay Graders?
by: Kundu, Anindita, et al.
Published: (2024)

The Good, the Bad and the Constructive: Automatically Measuring Peer Review's Utility for Authors
by: Sadallah, Abdelrahman, et al.
Published: (2025)

Tell me the truth: A system to measure the trustworthiness of Large Language Models
by: Lipizzi, Carlo
Published: (2024)

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

The Good, the Bad, and the Hulk-like GPT: Analyzing Emotional Decisions of Large Language Models in Cooperation and Bargaining Games
by: Mozikov, Mikhail, et al.
Published: (2024)

Automated Evaluation can Distinguish the Good and Bad AI Responses to Patient Questions about Hospitalization
by: Soni, Sarvesh, et al.
Published: (2025)

StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
by: Bi, Baolong, et al.
Published: (2024)

Are Large Language Models a Good Replacement of Taxonomies?
by: Sun, Yushi, et al.
Published: (2024)

Small Language Model as Data Prospector for Large Language Model
by: Ni, Shiwen, et al.
Published: (2024)

AI-Generated Slides: Are They Good? Can Students Tell?
by: Leinonen, Juho, et al.
Published: (2026)

Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!
by: Ma, Yubo, et al.
Published: (2023)

Big Reasoning with Small Models: Instruction Retrieval at Inference Time
by: Alkiek, Kenan, et al.
Published: (2025)

Large Language Models are Good Relational Learners
by: Wu, Fang, et al.
Published: (2025)

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
by: Zeng, Shenglai, et al.
Published: (2024)

From Construction to Injection: Edit-Based Fingerprints for Large Language Models
by: Li, Yue, et al.
Published: (2025)

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators
by: Gu, Feng, et al.
Published: (2025)

Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?
by: Wei, Xinyu, et al.
Published: (2025)

AgentCollabBench: Diagnosing When Good Agents Make Bad Collaborators
by: Mazumder, Aritra, et al.
Published: (2026)

When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits
by: Magomere, Jabez, et al.
Published: (2025)

BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
by: Lyu, Weimin, et al.
Published: (2024)

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
by: Xu, Fangzhi, et al.
Published: (2023)