Accepted Papers

Learned Sparse Retrieval (LSR) methods construct sparse lexical representations of queries and documents that can be efficiently searched using inverted indexes. Existing LSR approaches have almost exclusively relied on uncased backbone models, whose vocabularies exclude case-sensitive distinctions,...

Retrieval-Augmented Generation (RAG) couples document retrieval with large language models (LLMs). While scaling generators improves accuracy, it also raises cost and limits deployability. We explore an orthogonal axis: enlarging the retriever¡¯s corpus to reduce reliance on large LLMs. Experiment...

Dense retrieval is a promising approach for acquiring relevant context or world knowledge in open-domain natural language processing tasks and is now widely used in information retrieval applications. However, recent reports claim a broad preference for text generated by large language models (LLMs)...

Instruction-aware retrievers are retrieval models that usenatural language instructions to specify fine-grainedretrieval conditions beyond the original query. Theseretrievers, built on large language models, are trainedusing contrastive learning to consider both the relevancebetween a query and a do...

LLM-based listwise passage reranking has attracted attention for its effectiveness in ranking candidate passages. However, these models suffer from positional bias, where passages positioned towards the end of the input are less likely to be moved to top positions in the ranking. We hypothesize that...

Neural models have transformed Information Retrieval (IR) by enabling semantic search, representing queries and documents as dense embeddings in latent spaces. However, recent works indicate the contribution of single dimensions in these representations to ranking quality is uneven: some dimensions ...

Unlike short-form retrieval-augmented generation (RAG), such as factoid question answering, long-form RAG applications require retrieval to provide documents covering a wide range of relevant information. Automated report generation exemplifies this setting: it needs not only relevant information bu...

2D Matryoshka training enables a single embedding model to produce sub-network representations across varying layers and embedding dimensions, offering flexibility under different computational and task constraints. However, its performance remains below that of individually trained models of compar...