2 citations · 6 across the 23 of their papers we have counts for
23 papers
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…
WOLF: Werewolf-based Observations for LLM Deception and Falsehoods
Mrinal Agarwal, Saad Rana, Theo Sundoro +5
Deception is a fundamental challenge for multi-agent reasoning: effective systems must strategically conceal information while detecting misleading behavior in others. Yet most eva…
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…
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…
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…
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…
Universal Neurons in GPT-2: Emergence, Persistence, and Functional Impact
Advey Nandan, Cheng-Ting Chou, Amrit Kurakula +4
We investigate the phenomenon of neuron universality in independently trained GPT-2 Small models, examining these universal neurons-neurons with consistently correlated activations…
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…
The Geometry of Harmfulness in LLMs through Subconcept Probing
McNair Shah, Saleena Angeline, Adhitya Rajendra Kumar +5
Recent advances in large language models (LLMs) have intensified the need to understand and reliably curb their harmful behaviours. We introduce a multidimensional framework for pr…
COREVQA: A Crowd Observation and Reasoning Entailment Visual Question Answering Benchmark
Ishant Chintapatla, Kazuma Choji, Naaisha Agarwal +6
Recently, many benchmarks and datasets have been developed to evaluate Vision-Language Models (VLMs) using visual question answering (VQA) pairs, and models have shown significant…
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…
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…
Distill CLIP (DCLIP): Enhancing Image-Text Retrieval via Cross-Modal Transformer Distillation
Daniel Csizmadia, Andrei Codreanu, Victor Sim +5
We present Distill CLIP (DCLIP), a fine-tuned variant of the CLIP model that enhances multimodal image-text retrieval while preserving the original model's strong zero-shot classif…
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…
From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs
Stanley Yu, Vaidehi Bulusu, Oscar Yasunaga +5
Large Language Models (LLMs) exhibit strong conversational abilities but often generate falsehoods. Prior work suggests that the truthfulness of simple propositions can be represen…
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…
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…