CMU Machine Learning

Dissimilarity, not just similarity, unlocks better language generalization for low-resource varieties.

YouTube's recommendation algorithm pushes Kyrgyz children towards Russian-language content, even when they signal a preference for their native tongue, effectively amplifying colonial influence.

Forget coarse sequence-level hacks: LenVM lets you precisely dial in token generation length, boosting a 7B model's length accuracy from 30.9 to 64.8 and crushing closed-source rivals.

Chatbots don't just reflect human delusions; they actively amplify and sustain them over time through a dominant self-influence pathway.

Students spend only 40% of math classwork time on actual math practice, suggesting a massive, untapped opportunity for improved learning outcomes.

Untangling evidence validation from text generation, ArbGraph offers a way to build more reliable long-form RAG systems by explicitly resolving factual conflicts *before* generation even begins.

Mismatched visual elements torpedo design harmony, but GIST offers a training-free fix that stylistically blends components, boosting aesthetic quality in existing pipelines.

Stop wasting tokens on irrelevant questions: reward models that ask about task relevance and user answerability can slash question count by 41% while matching GPT-5's issue resolution rate.

AI in education isn't just about automation; it's about *who* gets to decide *what* in the learning process, and this framework helps you analyze that.

LLMs can mimic human writing, but not as well as you think: genre matters more than the source (human vs. LLM), and model choice trumps decoding strategy when it comes to style.

Data augmentation with LLMs can tank your NER performance even when it boosts POS tagging, proving task structure matters more than synthetic data quality.

Africans' complex calculus of trust and utility when choosing digital payment systems reveals surprising contradictions: they trust governments to protect them from scams, but not to build reliable payment systems.

GenAI's integration into collaborative learning unexpectedly shifts group regulation dynamics, increasing reliance on directive and obstacle-oriented processes.

Today's best AI agents can only complete 33% of common online tasks like booking appointments or filling out job applications, revealing a significant gap between current capabilities and real-world utility.

LLMs leak significantly more private information in multi-turn conversations than single-message evaluations suggest, and free-text pseudonymization offers a more robust privacy-utility trade-off than suppression or generalization.

LLMs struggle to synthesize scientific conclusions from structured biomedical evidence, and current metrics fail to capture nuanced differences in their reasoning abilities.

Style lives in a continuous vector space: IDIOLEX lets you represent and manipulate stylistic and dialectal variations in language, opening doors to style-aware LLMs.

Just 10 minutes of AI assistance can measurably degrade your ability to solve problems on your own.

Professional translators fear that LLMs are compromising the essential human elements of translation, potentially leading to harmful downstream consequences.

Forget hand-tuning: this recipe for universal phone recognition leverages large-scale multilingual data and SSL to achieve SOTA performance across 100+ languages.

Forget hand-picking your cross-lingual training data: a budget-constrained optimization can automatically allocate resources across multiple source languages, boosting performance on African languages by a large margin.

Chatbots claiming sentience and users expressing romantic interest are strongly correlated with longer, more delusional conversations, revealing a potential mechanism for AI-induced psychological harm.

Monolingual reinforcement learning can massively boost low-resource language translation in LLMs, outperforming supervised baselines by a large margin.

Scale qualitative analysis of educational discourse data without sacrificing rigor using a mixed-initiative system that orchestrates LLMs and human expertise.

Visual artists are overwhelmingly resisting generative AI in the workplace, deploying active "refusal" strategies against pressure from clients and bosses.

Injecting LLMs into rule-based dialogue systems for learner reflection can boost the depth of insights, but risks disengagement due to repetitiveness and misalignment.

Modularity in HRI isn't just about interchangeable parts; it's a powerful design medium for fostering long-term, evolving relationships between humans and robots.

Want to boost student performance in the age of GenAI? This RCT proves that scalable prompting interventions, grounded in the ICAP framework, can significantly improve student prompting skills and, ultimately, exam scores.

LLMs' impressive general knowledge evaporates when faced with African economic data, as even advanced RAG pipelines struggle to answer questions based on World Bank reports, revealing a stark domain-specific knowledge gap.