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Explore AI's role in promoting constructive online discussions about race, addressing content moderation biases, and developing NLP systems for social good across various domains.
Explore causal inference methods for improving content curation on digital platforms. Examine case studies on Reddit, discuss implications for policy, and consider future research directions in the era of generative AI.
Explore event extraction techniques for early epidemic prediction using social media data. Learn about epidemic event ontology, cross-lingual models, and innovative label projection methods.
Explore LLM generative behavior, successes, and shortcomings. Gain insights into model design, inference algorithms, and performance across diverse tasks requiring varied knowledge and capabilities.
Explore the journey from academia to entrepreneurship with insights on building a sports analytics startup. Gain valuable perspectives on challenges, similarities, and differences between academic and business worlds.
Uncover hidden information about API-protected LLMs through low-rank logit analysis. Learn to estimate embedding sizes, audit model updates, and identify source models efficiently.
Explore the history and impact of AI in protein structure prediction, focusing on AlphaFold 2's breakthrough and its implications for structural and cell biology research.
Enhance decision-making accuracy in uncertain environments using DeLLMa, a multi-step scaffolding framework based on decision and utility theory principles. Improve LLM performance by up to 40% in complex scenarios.
Explore multimodal machine intelligence applications in medical sciences and health research, addressing challenges in creating trustworthy, inclusive AI for clinical and quality of life domains.
Explore efficient approaches for selecting informative examples in In-Context Learning, enhancing large language model performance without additional training.
Explore a comprehensive audit of 1800+ text datasets, examining legal and ethical risks in AI data practices. Gain insights into dataset lineage, licensing, and the divide between open and closed datasets.
Explore transformer language models' internal representations to detect and analyze hallucinatory behavior in AI-generated content, with insights on task-dependent patterns and evaluation methods.
Explore visual analytics techniques to identify and address spurious correlations in AI systems, enhancing trustworthiness and reliability in machine learning and data-driven decision-making.
Explore a novel fine-tuning method for Large Language Models that improves performance without additional human-annotated data, using self-play mechanisms and theoretical guarantees.
Explore strategies to compromise LLM detectors using synonym replacement and instructional prompts, revealing vulnerabilities in text detection systems and emphasizing the need for robust safeguards.
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