most citedFAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations

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cs.CL2025

Interpreting the Latent Structure of Operator Precedence in Language Models

Dharunish Yugeswardeenoo, Harshil Nukala, Ved Shah +4

Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting s…

cs.CL2025

ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models

Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do +4

Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations…

cs.CL2025

Adaptive Originality Filtering: Rejection Based Prompting and RiddleScore for Culturally Grounded Multilingual Riddle Generation

Duy Le, Kent Ziti, Evan Girard-Sun +4

Language models are increasingly tested on multilingual creativity, demanding culturally grounded, abstract generations. Standard prompting methods often produce repetitive or shal…

cs.CL2025

Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

Timothy Do, Pranav Saran, Harshita Poojary +4

In this paper, we address the persistent challenges that figurative language expressions pose for natural language processing (NLP) systems, particularly in low-resource languages…

cs.CL2025

Causal Language Control in Multilingual Transformers via Sparse Feature Steering

Cheng-Ting Chou, George Liu, Jessica Sun +4

Deterministically controlling the target generation language of large multilingual language models (LLMs) remains a fundamental challenge, particularly in zero-shot settings where…

cs.CL2025

MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered

Imran Mirza, Cole Huang, Ishwara Vasista +4

Multi-agent systems, which consist of multiple AI models interacting within a shared environment, are increasingly used for persona-based interactions. However, if not carefully de…