56 citations · 76 across the 5 of their papers we have counts for
5 papers
RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation
Chi-Min Chan, Chunpu Xu, Ruibin Yuan +4
Large Language Models (LLMs) exhibit remarkable capabilities but are prone to generating inaccurate or hallucinatory responses. This limitation stems from their reliance on vast pr…
Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings
Isabelle Mohr, Markus Krimmel, Saba Sturua +16
We introduce a novel suite of state-of-the-art bilingual text embedding models that are designed to support English and another target language. These models are capable of process…
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
Hao Zhao, Jie Fu, Zhaofeng He
Parameter-efficient fine-tuning (PEFT) has shown its effectiveness in adapting the pre-trained language models to downstream tasks while only updating a small number of parameters.…
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Chi-Min Chan, Weize Chen, Yusheng Su +5
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost. With the emergence of large language models (LLMs), researchers have…
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
Gaole Dai, Wei Wu, Ziyu Wang +3
Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed opti…