1 citations · 2 across the 5 of their papers we have counts for
5 papers
Diverse by Reasoning: Harnessing the Wisdom of LLM Crowds for Future Prediction
Nirupam Chetlapalli, Yiming Liao, Min-Chun Chen +1
Large language models (LLMs) are increasingly used for future prediction, motivating the use of multiple models as a wisdom-of-the-crowd mechanism. However, simply increasing crowd…
Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
Yiming Liao, Zeno Franco, Jose Eduardo Lizarraga Mazaba +1
Hallucinations in medical large language models (LLMs) pose serious risks for clinical decision support, particularly when models must reason over complex electronic health records…
Towards Two-Stage Counterfactual Learning to Rank
Shashank Gupta, Yiming Liao, Maarten de Rijke
Counterfactual learning to rank (CLTR) aims to learn a ranking policy from user interactions while correcting for the inherent biases in interaction data, such as position bias. Ex…
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
Fan Yao, Yiming Liao, Jingzhou Liu +4
On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user…
User Welfare Optimization in Recommender Systems with Competing Content Creators
Fan Yao, Yiming Liao, Mingzhe Wu +6
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content reco…