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Women in AI Research

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/2509.22449We cover:Why prompt-based fixes for hallucinations aren’t enoughHow “unanswerability” emerges inside model representationsA simple but powerful idea: linear directions in hidden statesWhy this method generalizes better across datasetsWhat this reveals about where abstract concepts live inside LLMs👉 Watch to see how far we can push models toward knowing when they don’t know.Maor:https://www.linkedin.com/in/maor-juliet-lavi-07494a155👍 Like & subscribe for more deep dives into cutting-edge AI research🔔 New episodes from EACL 2026 coming soon

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/2505.03563Instead of relying on noisy, unconstrained paraphrasing, AUGMENT introduces:Structured, linguistically grounded paraphrase types (e.g., voice, style, dialect)A guided generation + automated quality control pipelineFine-grained analysis of how specific linguistic variations impact model behaviourKey insights:Different paraphrase types can shift model performance in opposite directionsStandard baselines can hide critical failure modesEven strong LLMs struggle with certain transformations (e.g., voice changes)Evaluated across:Bias benchmarks (BBQ)Knowledge tasks (MMLU)Multiple open-source model familiesCléa:https://scholar.google.com/citations?user=NdToDmMAAAA👍 Like & subscribe for more deep dives into cutting-edge AI research🔔 New episodes from EACL 2026 coming soon

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/2601.20757The paper takes a closer look at a common assumption: that adding demographic or identity-based personas can help align model outputs with different user groups.In this conversation, we discuss:Whether persona prompting meaningfully changes model predictionsWhy simulated personas don’t necessarily align with real-world demographicsThe gap between improving labels vs. improving model rationalesEvidence that LLMs may systematically over-predict harmful contentWhat this means for synthetic data generation and evaluation practicesOne of the key takeaways is that persona prompting has limited effect as a steering mechanism in these settings ,and should be applied with care, especially in high-stakes or socially sensitive applications.Jing Yang:https://www.linkedin.com/in/jing-yang-7b07aa135👍 Like & subscribe for more deep dives into cutting-edge AI research🔔 New episodes from EACL 2026 coming soon