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cs.HC2026
Calibrating LLM Explanations to Human Decisions with Reinforcement Learning
Dong Shu, Ziyang Guo, Negar Kamali +2
Explanations of LLM outputs are considered an important way to make model behavior legible, helping human users make good decisions on downstream tasks. However, default training a…
cs.LG2026
MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting
Dong Shu, Yanguang Liu, Huopu Zhang +4
Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle communication signals. However, e…
cs.LG2026
Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training
Dong Shu, Denghui Zhang, Jessica Hullman
Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that all generated episodes provide a…