Women in AI Research (WiAIR)

An image is not worth a thousand words - it's worth an indefinite number of them. So why do the metrics we use to evaluate AI-generated image descriptions still assume there's one correct answer?

In this episode of Women in AI Research, I talk with Elisa Kreiss (Assistant Professor of Communication at UCLA, director of the Coalas Lab) about what happens when you actually test the metrics the field relies on, and why CLIPScore, one o...

Only 19% of Americans say AI has actually improved their productivity - so why the gap between the hype and reality?

In this episode of Women in AI Research, Dr. Malihe Alikhani (Northeastern University, Contextual AI Lab) unpacks the hidden failures in how we build and deploy AI: why sycophancy is really a collapse of alignment, why bigger models aren't better aligned, and why "thin" alignment breaks down in the real world.

Key top...

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 than most people realize, why rea...

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 shaping human beliefs, this conversation ...

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 wi...

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 benchmarks test audio skills in i...

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 systems become more complex, this question b...

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 filtering and hallucination detection.

Their f...

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 representations
• A simple bu...

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/abs/2505.03563

Instead of relying on ...

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 or identity-based personas can help al...

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 surprising role of math in imp...

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 reducing over-refusal), they significa...

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 accuracy in identifying true vs...

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 applications.

In this episode:

• Th...

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 why chain-of-thought e...

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 the design and deployment of lan...

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 why current evaluation par...

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 means, why LLMs tend to h...