most citedSarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

1 citations · 1 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

15 papers · 1 filter

cs.CL2026

From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives

Aayush Aluru, Chloe Ho, Muhammad Hammouri +5

Although large language models (LLMs) have demonstrated impressive creative fiction generation, they struggle to maintain narrative consistency and coherent plot lines in long-form…

cs.CL20261 cited

Sarc7: Evaluating Sarcasm Detection and Generation with Seven Types and Emotion-Informed Techniques

Raina Gao, Alyssa Jeong, Lang Xiong +4

Sarcasm is a form of humor where expressions convey meanings opposite to their literal interpretations. Classifying and generating sarcasm using large language models is vital for…

cs.CL2025

Probe-Rewrite-Evaluate: A Workflow for Reliable Benchmarks and Quantifying Evaluation Awareness

Lang Xiong, Nishant Bhargava, Jianhang Hong +4

Large Language Models (LLMs) often exhibit significant behavioral shifts when they perceive a change from a real-world deployment context to a controlled evaluation setting, a phen…

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

DuoLens: A Framework for Robust Detection of Machine-Generated Multilingual Text and Code

Shriyansh Agrawal, Aidan Lau, Sanyam Shah +4

The prevalence of Large Language Models (LLMs) for generating multilingual text and source code has only increased the imperative for machine-generated content detectors to be accu…

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…