7 papers
Disco-RAG: Discourse-Aware Retrieval-Augmented Generation
Dongqi Liu, Hang Ding, Qiming Feng +6
Retrieval-Augmented Generation (RAG) has emerged as an important means of enhancing the performance of large language models (LLMs) in knowledge-intensive tasks. However, most exis…
ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning
Hang Ding, Jiawei Zhou, Haiyun Jiang
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for knowledge-intensive tasks, yet its effectiveness in long-context scenarios is often bottlenecked by the…
SE-Search: Self-Evolving Search Agent via Memory and Dense Reward
Jian Li, Yizhang Jin, Dongqi Liu +9
Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external knowledge. Recent se…
Aligning by Misaligning: Boundary-aware Curriculum Learning for Multimodal Alignment
Hua Ye, Hang Ding, Siyuan Chen +3
Most multimodal models treat every negative pair alike, ignoring the ambiguous negatives that differ from the positive by only a small detail. We propose Boundary-Aware Curriculum…
RoleRMBench & RoleRM: Towards Reward Modeling for Profile-Based Role Play in Dialogue Systems
Hang Ding, Qiming Feng, Dongqi Liu +9
Reward modeling has become a cornerstone of aligning large language models (LLMs) with human preferences. Yet, when extended to subjective and open-ended domains such as role play,…
SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting
Hang Ding, Xue Wang, Tian Zhou +1
Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point esti…