Women in AI Research (WiAIR)

Do large language models truly understand language—or are they sophisticated pattern matchers? In this conversation, Dr. Anna Ivanova (Asst. Prof. at Georgia Tech) explores one of the important questions in AI: the relationship between language, thought, and intelligence. Drawing from neuroscience, cognitive science, and AI research, Anna explains why language understanding is harder to define tha...

What actually happens when AI systems fail in the real world? In this final part of our conversation with Saadia Gabriel (UCLA), we unpack one of the most urgent challenges in modern AI: why even the most advanced models remain vulnerable to manipulation - and what that means for safety, fairness, and society. From multi-turn jailbreaking attacks with near 100% success rates to misinformation shap...

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 lang...

What does it actually mean for a model to understand audio Paper: https://arxiv.org/abs/2601.19673 In 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 b...

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/2510.15955 In 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 system...

In this episode of #WiAIRpodcast, we dive into a subtle but critical question: Does adding reasoning actually make LLMs safer and more reliable? Paper: https://arxiv.org/abs/2510.21049 Atoosa Chegini (University of Maryland, Apple) presents Reasoning's Razor (EACL 2026), where she and her collaborators examine how reasoning impacts high-stakes binary classification tasks, including safety filt...

LLMs are notoriously overconfident, but can we teach them to admit uncertainty? In this episode, Maor Juliet Lavi (Tel Aviv University) presents her EACL 2026 paper on Detecting Unanswerability in Large Language Models with Linear Directions. Paper: https://arxiv.org/abs/2509.22449 We cover: Why prompt-based fixes for hallucinations aren’t enough How “unanswerability” emerges inside model represen...

LLMs often give different answers to the same question, just phrased differently. But how do we measure and understand this behaviour rigorously? In this episode of the #WiAIRpodcast, Cléa Chataigner (Mila, McGill) presents AUGMENT, a user-grounded, controlled paraphrasing framework for auditing prompt sensitivity in large language models, accepted as an oral at EACL 2026. Paper: https://arxiv.org...

How much control do persona prompts actually give us over LLM behaviour? In this episode of #WiAIRpodcast, Jing Yang (TU Berlin) speaks about the study on persona prompting in socially sensitive tasks, including hate speech detection, sentiment analysis, and commonsense reasoning. Paper: https://arxiv.org/abs/2601.20757 The paper takes a closer look at a common assumption: that adding demographic...

What if adding math data actually improves reasoning in non-math tasks? In this episode of #WiAIRpodcast, Syeda from the Carnegie Mellon University and NVIDIA presents Nemotron CrossThink (EACL 2026 Oral) - a framework that challenges how we train LLMs for reasoning. Paper: https://arxiv.org/abs/2504.13941 Key insights you don’t want to miss: Why multi-domain training beats specialized models The...

Are the techniques we use to control language models quietly making them less safe? Paper: https://arxiv.org/abs/2602.06256 In this EACL 2026 paper, Navita Goyal (University of Maryland) challenges a core assumption behind inference-time interventions: that we can precisely steer model behaviour without unintended side effects. ▶️ Key insight: While steering methods improve target behaviours (like...

What happens when humans and large language models try to persuade each other? In this episode of Women in AI Research, we sit down with Angana Borah (University of Michigan) to unpack her EACL 2026 paper on misinformation, persuasion, and demographic effects in human–LLM interactions. Paper: https://arxiv.org/abs/2503.02038 Key insights from the paper: LLM-generated persuasion can reduce human ac...

In this conversation, Dr. Hila Gonen (Assistant Professor at the University of British Columbia) joins us to explore the deep insights into how large language models (LLMs) leak semantic information, behave across languages, and how researchers can uncover their root causes. Dr. Gonen shares her journey in interpreting AI systems, addressing biases, and controlling model outputs for safer, fairer...

Are reasoning models actually reasoning — or just producing convincing stories? Our guest in this episode of #WiAIRpodcast is Letitia Parcalabescu, the creator of the  @AICoffeeBreak  youtube channel. Letitia joins Jekaterina Novikova for a deep dive into the topics of faithfulness, self-consistency, hallucinations, and the reliability illusion in LLMs and multimodal reasoning models. We discuss w...

As language models become more capable, the hardest questions are no longer just about performance, but about safety, interpretation, and control. In this episode of Women in AI Research, we speak with Swabha Swayamdipta, Assistant Professor of Computer Science at the University of Southern California and co-Associate Director of the USC Center for AI and Society. Swabha’s research examines how th...

Do large language models actually understand meaning — or are we over-interpreting impressive behavior? In this episode, we speak with Dr. Maria Ryskina, CIFAR AI Safety Postdoctoral Fellow at the Vector Institute for AI, whose research bridges neuroscience, cognitive science, and artificial intelligence. Together, we unpack what the brain can (and cannot) teach us about modern AI systems — and wh...

AI doesn’t just process text — it takes in our cultures, reflects our hierarchies, and can make existing power structures even stronger. In this episode of Women in AI Research, Jekaterina Novikova and Malikeh Ehgaghi speak with Dr. Maria Antoniak (Assistant Professor at the University of Colorado Boulder) about inclusivity in AI, the dynamics of cultural representation, what trust in AI really me...

Is English just one of the languages you speak? If so, the AI tools you use might miss things that makes your voice multilingual. In this episode of Women in AI Research, Jekaterina Novikova speaks with Dr. Annie En-Shiun Lee about her work on multilingual and multicultural AI — from the widening language gap and the lack of benchmarks for underrepresented languages, to why domain-specific data ma...

Are today’s AI systems truly global — or just Western by design? 🌍 In this episode of Women in AI Research, Jekaterina Novikova and Malikeh Ehgaghi speak with Dr. Vered Shwartz (Assistant Professor at UBC and CIFAR AI Chair at the Vector Institute) about the cultural blind spots in today’s large language and vision-language models. REFERENCES: Vered Shwartz Google Scholar profile Book "Lost...

In this conversation, Jekaterina Novikova and Malikeh Ehgaghi interview Ana Marasović , an expert in AI trustworthiness, focusing on the complexities of explainability, the realities of academic research, and the dynamics of human-AI collaboration. We discuss the importance of intrinsic and extrinsic trust in AI systems, the challenges of evaluating AI performance, and the implications of syntheti...

Can open-source large language models really outperform closed ones like Claude 3.5? 🤔 In this episode of the Women in AI Research podcast, Jekaterina Novikova and Malikeh Ehghaghi engage with Valentina Pyatkin, a postdoctoral researcher at the Allen Institute for AI. We dive deep into the future of open science, LLM research, and extending model capabilities. 🔑 Topics we cover: Why open-source...

How can we go beyond accuracy to truly understand large language models? In this episode of the Women in AI Research podcast, hosts Jekaterina Novikova and Malikeh Ehghaghi sit down with Simeng Sophia Han (PhD candidate at #yaleuniversity , Research Scientist Intern at #metaai , ex #google #deepmind , ex #aws ) to explore the future of 𝐋𝐋𝐌 𝐫𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠, 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧, 𝐚𝐧𝐝 𝐞𝐱𝐩𝐥𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐀𝐈. 🌟 What you’ll...

In this episode of the Women in AI Research Podcast, hosts Jekaterina Novikova and Malikeh Ehghaghi engage with Abhilasha Ravichander to discuss the complexities of LLM hallucinations, the development of factuality benchmarks, and the importance of data transparency and machine unlearning in AI. The conversation also delves into personal experiences in academia and the future directions of researc...

A must-listen episode with Dr. Dieuwke Hupkes , a research scientist at #Meta AI Research, where we dive into AI generalization, LLM robustness, and model evaluation in large language models. We explore how LLMs handle grammar and hierarchy, how they generalize across tasks and languages, and what consistency tells us about AI alignment. We also talk about Dieuwke’s journey from physics to NLP, th...

🔍 𝐂𝐚𝐧 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐛𝐞 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝? 𝐇𝐨𝐰 𝐝𝐨 𝐰𝐞 𝐬𝐜𝐚𝐥𝐞 𝐛𝐞𝐲𝐨𝐧𝐝 𝐬𝐜𝐚𝐥𝐢𝐧𝐠 𝐥𝐚𝐰𝐬? 𝐀𝐧𝐝 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐫𝐞𝐚𝐥𝐥𝐲 𝐭𝐚𝐤𝐞 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐢𝐧𝐜𝐥𝐮𝐬𝐢𝐯𝐞, 𝐦𝐮𝐥𝐭𝐢𝐥𝐢𝐧𝐠𝐮𝐚𝐥 𝐋𝐋𝐌𝐬? In this episode of the #WiAIRpodcast, Wanru Zhao discusses decentralized and collaborative AI methods, the limitations of scaling laws, fine-tuning strategies, data attribution challenges in LLMs, and multilingual learning in federated settings—all whil...