:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Baluja, Ashwin
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Computers and Society
Online Access:	https://arxiv.org/abs/2412.05315
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor
by: Shafiei, Mohammadamin, et al.
Published: (2025)

Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You
by: Friedrich, Felix, et al.
Published: (2024)

Is Contrasting All You Need? Contrastive Learning for the Detection and Attribution of AI-generated Text
by: La Cava, Lucio, et al.
Published: (2024)

Metaphor Is Not All Attention Needs
by: Sorokoletova, Olga, et al.
Published: (2026)

Scaling Law in LLM Simulated Personality: More Detailed and Realistic Persona Profile Is All You Need
by: Bai, Yuqi, et al.
Published: (2025)

Evidence Is All You Need: Ordering Imaging Studies via Language Model Alignment with the ACR Appropriateness Criteria
by: Yao, Michael S., et al.
Published: (2024)

Leveraging Prompts in LLMs to Overcome Imbalances in Complex Educational Text Data
by: McClure, Jeanne, et al.
Published: (2024)

How Far Are We From AGI: Are LLMs All We Need?
by: Feng, Tao, et al.
Published: (2024)

Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs
by: Arnaiz-Rodriguez, Adrian, et al.
Published: (2025)

Beyond Text: Characterizing Domain Expert Needs in Document Research
by: Gururaja, Sireesh, et al.
Published: (2025)

Workflow is All You Need: Escaping the "Statistical Smoothing Trap" via High-Entropy Information Foraging and Adversarial Pacing
by: Jiang, Zhongjie
Published: (2025)

From "Help" to Helpful: A Hierarchical Assessment of LLMs in Mental e-Health Applications
by: Steigerwald, Philipp, et al.
Published: (2026)

Navigating the Prompt Space: Improving LLM Classification of Social Science Texts Through Prompt Engineering
by: Gunes, Erkan, et al.
Published: (2026)

Cultural Value Differences of LLMs: Prompt, Language, and Model Size
by: Zhong, Qishuai, et al.
Published: (2024)

Making Images from Images: Interleaving Denoising and Transformation
by: Baluja, Shumeet, et al.
Published: (2024)

"Amazing, They All Lean Left" -- Analyzing the Political Temperaments of Current LLMs
by: Neuman, W. Russell, et al.
Published: (2025)

Don't Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
by: Anand, Abhishek, et al.
Published: (2024)

Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks
by: Møller, Anders Giovanni, et al.
Published: (2024)

Surface Reading LLMs: Synthetic Text and its Styles
by: Bajohr, Hannes
Published: (2025)

All in How You Ask for It: Simple Black-Box Method for Jailbreak Attacks
by: Takemoto, Kazuhiro
Published: (2024)

Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems
by: Harvey, Emma, et al.
Published: (2025)

Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text
by: Najjar, Ayat, et al.
Published: (2025)

Your Students Don't Use LLMs Like You Wish They Did
by: Kobler, Sebastian, et al.
Published: (2026)

Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
by: Jang, Yeonwoo, et al.
Published: (2025)

How Large Language Models (LLMs) Extrapolate: From Guided Missiles to Guided Prompts
by: Cao, Xuenan
Published: (2024)

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits
by: Li, Bohan, et al.
Published: (2024)

Privacy Policy Analysis through Prompt Engineering for LLMs
by: Goknil, Arda, et al.
Published: (2024)

Concept-Guided Chain-of-Thought Prompting for Pairwise Comparison Scoring of Texts with Large Language Models
by: Wu, Patrick Y., et al.
Published: (2023)

When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
by: Zheng, Mingqian, et al.
Published: (2023)

White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
by: Wan, Yixin, et al.
Published: (2024)

Gendered Divides in Online Discussions about Reproductive Rights
by: Rao, Ashwin, et al.
Published: (2025)

LLMs for Low-Resource Dialect Translation Using Context-Aware Prompting: A Case Study on Sylheti
by: Prama, Tabia Tanzin, et al.
Published: (2025)

Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset
by: Chehbouni, Khaoula, et al.
Published: (2024)

"All You Need" is Not All You Need for a Paper Title: On the Origins of a Scientific Meme
by: Alyakin, Anton
Published: (2025)

"Would You Want an AI Tutor?" Understanding Stakeholder Perceptions of LLM-based Systems in the Classroom
by: Fuligni, Caterina, et al.
Published: (2025)

EtiCor++: Towards Understanding Etiquettical Bias in LLMs
by: Dwivedi, Ashutosh, et al.
Published: (2025)

Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
by: Hartmann, David, et al.
Published: (2026)

Large Language Models in the Abuse Detection Pipeline
by: Kath, Suraj, et al.
Published: (2026)

Single Word Change is All You Need: Using LLMs to Create Synthetic Training Examples for Text Classifiers
by: Xu, Lei, et al.
Published: (2024)

Predictable Emergent Abilities of LLMs: Proxy Tasks Are All You Need
by: Zhang, Bo-Wen, et al.
Published: (2024)