From the 1 of 14 linked papers with an AI index.
14 papers
Rubrics on Trial: Evolving Rubrics from a Single Query via Synthetic Pairwise Evidence
Haocheng Yang, Licheng Pan, Xiaoxi Li +5
The paper proposes a query‑only method that automatically creates and validates fine‑grained rubrics for evaluating large language models by using synthetic rubric‑conditioned resp…
Rethinking the Flow-Based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective
Zhichao Chen, Zhan Zhuang, Yunfei Teng +6
Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via intermediate domains. However, real i…
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment
Hao Wang, Licheng Pan, Yuan Lu +7
Training time-series forecasting models requires aligning the conditional distribution of model forecasts with that of the label sequence. The standard direct forecast (DF) approac…
From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Tianqiao Liu, Xueyi Li, Hao Wang +4
Recent advances in large language models (LLMs) have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-to-speech conve…
ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment
Hao Wang, Haocheng Yang, Licheng Pan +7
Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingen…
Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects
Hao Wang, Licheng Pan, Qingsong Wen +12
Autocorrelation is a defining characteristic of time-series data, where each observation is statistically dependent on its predecessors. In the context of deep time-series forecast…