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20232026
most citedBreaking Down the Defenses: A Comparative Survey of Attacks on Large Language Models

7 citations · 42 across the 105 of their papers we have counts for

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cs.CL2026

Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

Komal Kumar, Aman Chadha, Salman Khan +2

The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in mul…

cs.CL2026

Simulating Meaning, Nevermore! Introducing ICR: A Semiotic-Hermeneutic Metric for Evaluating Meaning in LLM Text Summaries

Natalie Perez, Sreyoshi Bhaduri, Aman Chadha

Meaning in human language is relational, context dependent, and emergent, arising from dynamic systems of signs rather than fixed word-concept mappings. In computational settings,…

cs.CL2026

Assessing LLM Reliability on Temporally Recent Open-Domain Questions

Pushwitha Krishnappa, Amit Das, Vinija Jain +2

Large Language Models (LLMs) are increasingly deployed for open-domain question answering, yet their alignment with human perspectives on temporally recent information remains unde…

cs.CL2025

Catch Me If You Can: How Smaller Reasoning Models Pretend to Reason with Mathematical Fidelity

Subramanyam Sahoo, Vinija Jain, Saanidhya Vats +4

Current evaluation of mathematical reasoning in language models relies primarily on answer accuracy, potentially masking fundamental failures in logical computation. We introduce a…

cs.CL2025

AMBEDKAR-A Multi-level Bias Elimination through a Decoding Approach with Knowledge Augmentation for Robust Constitutional Alignment of Language Models

Snehasis Mukhopadhyay, Aryan Kasat, Shivam Dubey +5

Large Language Models (LLMs) can inadvertently reflect societal biases present in their training data, leading to harmful or prejudiced outputs. In the Indian context, our empirica…

cs.CL2025

I Think, Therefore I Am Under-Qualified? A Benchmark for Evaluating Linguistic Shibboleth Detection in LLM Hiring Evaluations

Julia Kharchenko, Tanya Roosta, Aman Chadha +1

This paper introduces a comprehensive benchmark for evaluating how Large Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic markers that can inadvertently…