From the 1 of 17 linked papers with an AI index.
11 papers · 1 filter
Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Xiao Ye, Jacob Dineen, Evan Zhu +3
The paper presents Hindcast, a framework that evaluates large language model forecasters by replaying resolved prediction markets using a frozen Reddit snapshot taken before each m…
Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution
Jacob Dineen, Aswin RRV, Zhikun Xu +1
Co-evolutionary self-play, where one language model generates problems and another solves them, promises curriculum learning without human supervision. The promise breaks down earl…
Robust Asynchronous Planning via Auto-Formalization
Jiayi Zhang, Jianing Yin, Ben Zhou +1
LLMs can plan by either generating action sequences directly as a Planner or translating tasks into domain specific language for an external solver as a Formalizer. While most real…
RECAP: Transparent Inference-Time Emotion Alignment for Medical Dialogue Systems
Adarsh Srinivasan, Jacob Dineen, Muhammad Umar Afzal +3
Large language models in healthcare often produce emotionally flat or opaque responses, failing to provide the transparent reasoning required for clinical trust. We present RECAP (…
Reliable Use of Lemmas via Eligibility Reasoning and SectionAware Reinforcement Learning
Zhikun Xu, Xiaodong Yu, Ben Zhou +6
Recent large language models (LLMs) perform strongly on mathematical benchmarks yet often misapply lemmas, importing conclusions without validating assumptions. We formalize lemma$…
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
Matthew W. Kenaston, Umair Ayub, Mihir Parmar +14
Despite high performance on clinical benchmarks, large language models may reach correct conclusions through faulty reasoning, a failure mode with safety implications for oncology…