From the 1 of 6 linked papers with an AI index.
6 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…
KaLM-Reranker-V1: Fast but Not Late Interaction for Compressed Document Reranking
Xinping Zhao, Jiaxin Xu, Ziqi Dai +7
As retrieval systems scale, high-quality reranking becomes increasingly important. However, most existing rerankers, whether encoder-based or decoder-based, jointly encode the quer…
Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer
Salimeh Sekeh, Xin Zhang
Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted…
Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation
Xin Zhang, Yang Cao, Baoxing Wu +2
Large language models have shown strong performance in natural language generation and downstream reasoning tasks, but they still struggle with logical consistency, factual groundi…
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…