2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Direct Confidence Alignment: Aligning Verbalized Confidence with Internal Confidence In Large Language Models
Glenn Zhang, Treasure Mayowa, Jason Fan +4
Producing trustworthy and reliable Large Language Models (LLMs) has become increasingly important as their usage becomes more widespread. Calibration seeks to achieve this by impro…
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…
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…
FAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations
Athena Wen, Tanush Patil, Ansh Saxena +3
In an era where AI-driven hiring is transforming recruitment practices, concerns about fairness and bias have become increasingly important. To explore these issues, we introduce a…
Pause-Tuning for Long-Context Comprehension: A Lightweight Approach to LLM Attention Recalibration
James Begin, Namit Agrawal, Eshan Singh +4
LLMs have demonstrated remarkable proficiency in understanding tasks but continue to struggle with long-context comprehension, particularly with content located in the middle of ex…