1 citations · 1 across the 14 of their papers we have counts for
15 papers
A Combined Streaming FPGA Architecture for Client-Side CKKS Encoding and Encryption
Joseph Kurina, Bogdan Pasca, Suleyman Demirsoy +1
Homomorphic encryption allows a computing service to process encrypted data, but the client must first encode and encrypt its inputs. Sharing hardware between floating-point encodi…
Token-Level Diagnosis of Sycophancy in LLMs with Attribution-Guided Steering
Hieu Nguyen, Mahammed Kamruzzaman, Anshuman Chhabra +1
Sycophancy refers to the tendency for large language models (LLMs) to match user beliefs at the cost of factual correctness, thereby undermining model reliability. Prior work on ev…
Implicit Reasoning Steering via Concept Chaining
Xiao Ye, Sanika Chavan, Yuxi Huang +4
Large language models often appear to reason reliably, yet on many questions repeated sampling yields both correct and incorrect answers, revealing an underlying fragility in how f…
Exposing the Illusion of Erasure in Knowledge Editing for LLMs
Advik Raj Basani, Anshuman Chhabra
Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mechanisms remain poorly understo…
AFRILANGTUTOR: Advancing Language Tutoring and Culture Education in Low-Resource Languages with Large Language Models
Tadesse Destaw Belay, Shahriar Kabir Nahin, Israel Abebe Azime +6
How can language learning systems be developed for languages that lack sufficient training resources? This challenge is increasingly faced by developers across the African continen…
SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening
Shahriar Kabir Nahin, Hadi Askari, Muhao Chen +1
The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-world deployment. While most videos can b…
Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling
Ocean Monjur, Shahriar Kabir Nahin, Anshuman Chhabra
Large Language Models (LLMs) now exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), with impressive performance across math and coding benchmarks. I…
The Prosocial Ranking Challenge: Reducing Polarization on Social Media without Sacrificing Engagement
Jonathan Stray, Ian Baker, George Beknazar-Yuzbashev +41
We report the first direct comparisons of multiple alternative social media algorithms on multiple platforms on outcomes of societal interest. We used a browser extension to modify…
Curvature-Weighted Capacity Allocation: A Minimum Description Length Framework for Layer-Adaptive Large Language Model Optimization
Theophilus Amaefuna, Hitesh Vaidya, Anshuman Chhabra +1
Layer-wise capacity in large language models is highly non-uniform: some layers contribute disproportionately to loss reduction, whereas others are nearly redundant. Existing layer…
Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis
Shrestha Datta, Hongfu Liu, Anshuman Chhabra
Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserving its behavior on all other inputs.…
First is Not Really Better Than Last: Evaluating Layer Choice and Aggregation Strategies in Language Model Data Influence Estimation
Dmytro Vitel, Anshuman Chhabra
Identifying how training samples influence/impact Large Language Model (LLM) decision-making is essential for effectively interpreting model decisions and auditing large-scale data…
Rethinking Reasoning in LLMs: Neuro-Symbolic Local RetoMaton Beyond ICL and CoT
Rushitha Santhoshi Mamidala, Anshuman Chhabra, Ankur Mali
Prompt-based reasoning strategies such as Chain-of-Thought (CoT) and In-Context Learning (ICL) have become widely used for eliciting reasoning capabilities in large language models…
Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages
Israel Abebe Azime, Tadesse Destaw Belay, Dietrich Klakow +2
Large language models (LLMs) have demonstrated significant capabilities in solving mathematical problems expressed in natural language. However, multilingual and culturally-grounde…
From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
Mahammed Kamruzzaman, Abdullah Al Monsur, Gene Louis Kim +1
Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as…
Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers
Akshit Achara, Anshuman Chhabra
AI Safety Moderation (ASM) classifiers are designed to moderate content on social media platforms and to serve as guardrails that prevent Large Language Models (LLMs) from being fi…