5 papers
CitySeeker: How Do VLMS Explore Embodied Urban Navigation With Implicit Human Needs?
Siqi Wang, Chao Liang, Yunfan Gao +5
Vision-Language Models (VLMs) have made significant progress in explicit instruction-based navigation; however, their ability to interpret implicit human needs (e.g., "I am thirsty…
C2T-ID: Converting Semantic Codebooks to Textual Document Identifiers for Generative Search
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +4
Designing document identifiers (docids) that carry rich semantic information while maintaining tractable search spaces is a important challenge in generative retrieval (GR). Popula…
LLMs as Sparse Retrievers:A Framework for First-Stage Product Search
Hongru Song, Yu-an Liu, Ruqing Zhang +6
Product search is a crucial component of modern e-commerce platforms, with billions of user queries every day. In product search systems, first-stage retrieval should achieve high…
Retrieval-in-the-Chain: Bootstrapping Large Language Models for Generative Retrieval
Yingchen Zhang, Ruqing Zhang, Jiafeng Guo +3
Generative retrieval (GR) is an emerging paradigm that leverages large language models (LLMs) to autoregressively generate document identifiers (docids) relevant to a given query.…
From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Models
Dana Alsagheer, Yang Lu, Abdulrahman Kamal +7
Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains. However, effective decision-making relies heavily on strong reason…