From the 1 of 5 linked papers with an AI index.
5 papers
LMEB: Long-horizon Memory Embedding Benchmark
Xinping Zhao, Xinshuo Hu, Jiaxin Xu +9
The paper presents LMEB, a benchmark suite of 22 datasets and 193 zero-shot retrieval tasks designed to evaluate how well text embedding models handle long-horizon, context‑depende…
Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering
Huiyao Chen, Yi Yang, Yinghui Li +3
Existing long-document question answering systems typically process texts as flat sequences or use heuristic chunking, which overlook the discourse structures that naturally guide…
Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation
Xinping Zhao, Shouzheng Huang, Yan Zhong +4
Retrieval-Augmented Generation (RAG) effectively improves the accuracy of Large Language Models (LLMs). However, retrieval noises significantly undermine the quality of LLMs' gener…
On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey
Meishan Zhang, Xin Zhang, Xinping Zhao +3
Text embeddings have attracted growing interest due to their effectiveness across a wide range of natural language processing (NLP) tasks, including retrieval, classification, clus…
Supervised Fine-Tuning or Contrastive Learning? Towards Better Multimodal LLM Reranking
Ziqi Dai, Xin Zhang, Mingxin Li +6
In information retrieval, training reranking models mainly focuses on two types of objectives: metric learning (e.g. contrastive loss to increase the predicted scores on relevant q…