1 citations · 2 across the 7 of their papers we have counts for
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ConceptGuard: Benchmarking Context-Sensitive Unlearning in Large Language Models
Sahil Kale, Ian Harris
Large Language Models (LLMs) increasingly require selective removal of harmful or sensitive knowledge, called unlearning, yet existing methods and benchmarks fail to evaluate this…
Future Confidence Distillation in Large Language Models
Sahil Kale
Reliable confidence estimation is essential for deploying large language models (LLMs) in confidence-aware systems, where downstream decisions such as retrieval, tool use, and adap…
Lie to Me: Knowledge Graphs for Robust Hallucination Self-Detection in LLMs
Sahil Kale, Antonio Luca Alfeo
Hallucinations, the generation of apparently convincing yet false statements, remain a major barrier to the safe deployment of LLMs. Building on the strong performance of self-dete…
Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs
Sahil Kale
Modern large language models increasingly integrate internal web-based retrieval to provide real-time answers, yet it remains unclear how effectively these systems identify informa…
TeXpert: A Multi-Level Benchmark for Evaluating LaTeX Code Generation by LLMs
Sahil Kale, Vijaykant Nadadur
LaTeX's precision and flexibility in typesetting have made it the gold standard for the preparation of scientific documentation. Large Language Models (LLMs) present a promising op…
Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledge
Sahil Kale
When artificial intelligence mistakes memorization for intelligence, it creates a dangerous mirage of reasoning. Existing studies treat memorization and self-knowledge deficits in…