Paper 29
- From Local to Global: A GraphRAG Approach to Query-Focused Summarization
- RzenEmbed: Towards Comprehensive Multimodal Retrieval
- U-MARVEL: Unveiling Key Factors for Universal Multimodal Retrieval via Embedding Learning with MLLMs
- MokA : Multimodal Low-Rank Adaptation for MLLMs
- MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction
- Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation
- Guiding Cross-Modal Representations with MLLM Priors via Preference Alignment
- CLIP-MoE: Towards Building Mixture of Experts for CLIP with Diversified Multiplet Upcycling
- SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compression
- FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training
- Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks
- LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
- Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from Token and Parameter Levels
- Training LLMs to be Better Text Embedders through Bidirectional Reconstruction
- Boosting Data Utilization for Multilingual Dense Retrieval
- Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings
- Differential-informed Sample Selection Accelerates Multimodal Contrastive Learning
- Aligning Information Capacity Between Vision and Language via Dense-to-Sparse Feature Distillation for Image-Text Matching
- Negative Matters: Multi-Granularity Hard-Negative Synthesis and Anchor-Token-Aware Pooling for Enhanced Text Embeddings
- Fixing Data That Hurts Performance: Cascading LLMs to Relabel Hard Negatives for Robust Information Retrieval
- SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
- OG-RAG: Ontology-Grounded Retrieval-Augmented Generation for Large Language Models
- SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs
- FinLoRA : Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets
- Quantifying Uncertainty in Answers from Any Language Model and Enhancing Their Trustworthiness
- Chain-of-Thought Prompting Obscures Hallucination Cues in Large Language Models: An Empirical Evaluation
- SCAR: Data Selection via Style Consistency-Aware Response Ranking for Efficient Instruction-Tuning of Large Language Models
- Smurfs: Multi-Agent System using Context-Efficient DFSDT for Tool Planning
- Re-Invoke: Tool Invocation Rewriting for Zero-Shot Tool Retrieval