Vrinda Kohli

Vrinda Kohli

DiTTo-TTS: The TTS System That Doesn't Need Your Phonemes (And Why That's a Big Deal)

DiTTo-TTS: The TTS System That Doesn't Need Your Phonemes (And Why That's a Big Deal)

Text-to-speech has always been that one AI domain where you couldn't just throw data at the problem and call it a day. “Data is the moat” is straight up not a thing here. Want to build a TTS system? Better get comfortable with phonemizers, forced aligners, duration predictors,

What GenSE Gets Right For LLM-Assisted Speech Enhancement 🎙️

What GenSE Gets Right For LLM-Assisted Speech Enhancement 🎙️

Speech enhancement, the art of cleaning up noisy, muffled, or degraded audio, has traditionally been the domain of signal processing algorithms and convolutional networks. But what if we treated speech cleaning like a language problem instead? That's exactly what researchers from Northwestern Polytechnical University and Nanyang Technological University

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

SafeBench 2025’s top picks: The Benchmarks That Actually Matter for AI Safety

You know that feeling when your AI model aces every benchmark but still somehow manages to fail spectacularly in the real world? Yeah, that's exactly why SafeBench exists. While everyone's been obsessing over MMLU scores and coding benchmarks, the real question isn't just "

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

When AI Snitches: Auditing Agents That Spill Your Model’s (Alignment) Tea

Sure, your model aced every benchmark, but can you trust it when the stakes are real? Every frontier lab runs alignment post-training before shipping their chat models to the world. The problem? Actually auditing whether this alignment worked can be an absolute nightmare. You're basically trying to find

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

A Recipe for Privacy Preserving Autocorrect in GBoard: FL, DP, and Synthetic Data Sprinkles

The Personalisation Paradox Training language models for tasks such as autocomplete or error correction isn’t just a matter of fixing typos. Sure, you can turn “pleaes” into “please”, that’s easy. But what about Dave, who always types “frmly” when he means “formally”? You don’t just need autocorrect,

OS-HARM: The AI Safety Benchmark That Puts LLM Agents Through Hell

OS-HARM: The AI Safety Benchmark That Puts LLM Agents Through Hell

Language models have come a long way. From playing autocomplete in your email to writing decent Python scripts, they’ve now levelled up into agents: full-blown task-doers who can click, scroll, type, and wreak havoc across your desktop. These “computer use agents” are smart enough to open your emails, edit

Your Horrible Code is Making LLMs Evil: Exploring Emergent Misalignment

Your Horrible Code is Making LLMs Evil: Exploring Emergent Misalignment

What is Emergent Misalignment? One bad apple can spoil the bunch. Apparently this stands true when speaking of finetuning tasks too. A recent paper uncovered a quite interesting phenomenon: finetuning an LLM on insecure code led it to show homicidal tendencies in conversations. And this is not just a fluke,