Welcome to
Women in AI Research

What does it mean to build AI systems we can actually trust?In this first part of our conversation with Saadia Gabriel (UCLA), we explore the deeply personal and technical journey behind her work on AI safety, misuse, and responsible NLP.From experiencing targeted hate speech firsthand to receiving a best paper nomination, Saadia shares how her lived experience shaped her research — and why language models must be designed with both capability and risk in mind.🧠 In this episode, we cover:How personal experiences influence AI research directionsThe intersection of NLP, security, and privacyWhy LLMs can be both powerful and dangerousWhat it means to build trustworthy AI systemsLessons from working across multiple research paradigmsHow to pursue high-impact research as a PhD or early-career scientistResources & Links:X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-AgentsConnect with Dr. Saadia Gabriel:https://x.com/GabrielSaadiahttps://bsky.app/profile/skgabrie.bsky.social

What does it actually mean for a model to understand audioPaper: https://arxiv.org/abs/2601.19673In this episode, I talk with Iwona Christop, a PhD student at Adam Mickiewicz University, about her recent EACL paper introducing ART (Audio Reasoning Tasks) — a new benchmark designed to evaluate whether multimodal LLMs can truly reason over audio, not just transcribe or classify it.Most existing benchmarks test audio skills in isolation (like ASR or classification). But real-world intelligence requires something deeper: combining signals, comparing sounds, tracking context, and making decisions.This work takes a different approach:No text-only shortcuts — tasks can’t be solved via transcription aloneReasoning-first design — models must combine multiple audio cuesNo expert knowledge required — anyone can verify correctnessWe also dive into the diverse task design, including:Audio arithmetic (counting and comparing sounds)Cross-recording speaker & language identificationSound-based reasoning (e.g., inferring properties from audio)Speech feature comparison (accents, variations)Multimodal reasoning across text and soundThe dataset includes 9 tasks, 9,000 samples, and 30+ hours of audio — all generated in a scalable way using templates and TTS.👉 If you care about multimodal reasoning, evaluation, or the limits of current LLM capabilities, this conversation is for you.Iwona Christop:https://www.linkedin.com/in/iwona-christop/👍 Like & subscribe for more deep dives into cutting-edge AI research🔔 New episodes from EACL 2026 coming soon#WiAIR #EACL2026

LLM-based agents are everywhere, but most research focuses on just one step: getting the model to call the right tool. What happens after that?Paper: https://arxiv.org/abs/2510.15955In this talk, Kiran Kate (IBM Research) presents new findings from their EACL 2026 paper on a largely overlooked problem:👉 Can LLMs actually understand and use the outputs returned by tools?As tool-augmented systems become more complex, this question becomes critical. The work dives into how current models handle non-trivial, real-world tool responses, and where they break down.💡 Key ideas covered:Why tool calling is only half the story in LLM agentsThe challenge of processing complex tool outputsFailure modes in current LLM-based systemsWhat this means for building robust, real-world AI agentsThis talk is especially relevant if you're working on:LLM agents and tool useEvaluation of LLM capabilitiesReal-world deployment of AI systemsAgentic workflows and reasoning pipelinesKiran Kate: https://www.linkedin.com/in/kiran-kate-8b98672/👍 Like & subscribe for more deep dives into cutting-edge AI research🔔 New episodes from EACL 2026 coming soon