23 citations · 23 across the 6 of their papers we have counts for
7 papers
Causal Syntax Circuits in CodeLMs: Budget and Probe Decide What Looks Localised
Harsh Kumar, Tanmay Shailesh Joshi, Rahul Maity +1
Code models write Python when the prompt does not say which language to use. We ask a mechanistic question about that habit: is it produced by a small, findable group of components…
PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
Harsh Kumar, Rahul Maity, Tanmay Joshi +4
Aligned large language models (LLMs) remain vulnerable to adversarial manipulation, and their reliance on web-scale pretraining creates a subtle but consequential attack surface. W…
"When Data is Scarce, Prompt Smarter"... Approaches to Grammatical Error Correction in Low-Resource Settings
Somsubhra De, Harsh Kumar, Arun Prakash A
Grammatical error correction (GEC) is an important task in Natural Language Processing that aims to automatically detect and correct grammatical mistakes in text. While recent adva…
Chronocept: Instilling a Sense of Time in Machines
Krish Goel, Sanskar Pandey, KS Mahadevan +2
Human cognition is deeply intertwined with a sense of time, known as Chronoception. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. D…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Performance Characterization and Optimizations of Traditional ML Applications
Harsh Kumar, R. Govindarajan
Even in the era of Deep Learning based methods, traditional machine learning methods with large data sets continue to attract significant attention. However, we find an apparent la…
Using Images to Find Context-Independent Word Representations in Vector Space
Harsh Kumar
Many methods have been proposed to find vector representation for words, but most rely on capturing context from the text to find semantic relationships between these vectors. We p…