4 citations · 18 across the 27 of their papers we have counts for
27 papers
MUDDLE: Measuring Understanding of Documents under Distractor and Length Effects
Jason Luo, Saibilila Abudukelimu, Judy Song +4
Document question-answering systems increasingly answer questions over collections of retrieved documents rather than one clean source, so robustness to distracting context matters…
CBMAS: Cognitive Behavioral Modeling via Activation Steering
Ahmed H. Ismail, Anthony Kuang, Ayo Akinkugbe +2
Large language models (LLMs) often encode cognitive behaviors unpredictably across prompts, layers, and contexts, making them difficult to diagnose and control. We present CBMAS, a…
Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning
Leo Lu, Jonathan Zhang, Sean Chua +4
Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of large language models (LLMs). While prior work focuses on improving model performance thro…
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…
SMAGDi: Socratic Multi Agent Interaction Graph Distillation for Efficient High Accuracy Reasoning
Aayush Aluru, Myra Malik, Samarth Patankar +4
Multi-agent systems (MAS) often achieve higher reasoning accuracy than single models, but their reliance on repeated debates across agents makes them computationally expensive. We…
SwiftSolve: A Self-Iterative, Complexity-Aware Multi-Agent Framework for Competitive Programming
Adhyayan Veer Singh, Aaron Shen, Brian Law +4
Correctness alone is insufficient: LLM-generated programs frequently satisfy unit tests while violating contest time or memory budgets. We present SwiftSolve, a complexity-aware mu…
Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
Atharva Bhargude, Ishan Gonehal, Dave Yoon +4
Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in dete…
Error Reflection Prompting: Can Large Language Models Successfully Understand Errors?
Jason Li, Lauren Yraola, Kevin Zhu +1
Prompting methods for language models, such as Chain-of-thought (CoT), present intuitive step-by-step processes for problem solving. These methodologies aim to equip models with a…
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…
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…
NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts
Abhay Gupta, Michael Lu, Kevin Zhu +2
Current large language models (LLMs) struggle to answer questions that span tens of thousands of tokens, especially when multi-hop reasoning is involved. While prior benchmarks exp…
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…
Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language
Jesus Alvarez C, Daua D. Karajeanes, Ashley Celeste Prado +5
The digital exclusion of endangered languages remains a critical challenge in NLP, limiting both linguistic research and revitalization efforts. This study introduces the first com…
Probing Audio-Generation Capabilities of Text-Based Language Models
Arjun Prasaath Anbazhagan, Parteek Kumar, Ujjwal Kaur +3
How does textual representation of audio relate to the Large Language Model's (LLMs) learning about the audio world? This research investigates the extent to which LLMs can be prom…
Deconstructing Bias: A Multifaceted Framework for Diagnosing Cultural and Compositional Inequities in Text-to-Image Generative Models
Muna Numan Said, Aarib Zaidi, Rabia Usman +5
The transformative potential of text-to-image (T2I) models hinges on their ability to synthesize culturally diverse, photorealistic images from textual prompts. However, these mode…
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…
EnDive: A Cross-Dialect Benchmark for Fairness and Performance in Large Language Models
Abhay Gupta, Jacob Cheung, Philip Meng +4
The diversity of human language, shaped by social, cultural, and regional influences, presents significant challenges for natural language processing (NLP) systems. Existing benchm…
CLEAR: Contrasting Textual Feedback with Experts and Amateurs for Reasoning
Andrew Rufail, Daniel Kim, Sean O'Brien +1
We introduce CLEAR (Contrasting Textual Feedback with Experts and Amateurs for Reasoning), a novel approach to language model reasoning that leverages the strengths of a larger (ex…
TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
Joshua Liu, Aarav Jain, Soham Takuri +5
Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to…
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…
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
Ishneet Sukhvinder Singh, Ritvik Aggarwal, Ibrahim Allahverdiyev +4
Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related informatio…
Semantic Self-Consistency: Enhancing Language Model Reasoning via Semantic Weighting
Tim Knappe, Ryan Li, Ayush Chauhan +3
While large language models (LLMs) have rapidly improved their performance on a broad number of tasks, they still often fall short on reasoning tasks. As LLMs become more integrate…
DiversityMedQA: Assessing Demographic Biases in Medical Diagnosis using Large Language Models
Rajat Rawat, Hudson McBride, Dhiyaan Nirmal +5
As large language models (LLMs) gain traction in healthcare, concerns about their susceptibility to demographic biases are growing. We introduce {DiversityMedQA}, a novel benchmark…
From Bias to Balance: Detecting Facial Expression Recognition Biases in Large Multimodal Foundation Models
Kaylee Chhua, Zhoujinyi Wen, Vedant Hathalia +2
This study addresses the racial biases in facial expression recognition (FER) systems within Large Multimodal Foundation Models (LMFMs). Despite advances in deep learning and the a…