Large language models (LLM) have demonstrated emergent abilities in text generation,
question answering, and reasoning, facilitating various tasks and domains. Despite their …

Passage retrieval is a fundamental task in many information systems, such as web search
and question answering, where both efficiency and effectiveness are critical concerns. In …

Improving user retention with reinforcement learning (RL) has attracted increasing attention
due to its significant importance in boosting user engagement. However, training the RL …

Unbiased Learning to Rank (ULTR), ie, learning to rank documents with biased user
feedback data, is a well-known challenge in information retrieval. Existing methods in …

In this paper, we systematically study the potential of pre-training with Large Language
Model (LLM)-based document expansion for dense passage retrieval. Concretely, we …

In this paper, we make a case for a proxy for large language models which has explicit
support for cost-saving optimizations. We design LLMProxy, which supports three key …

In this work, the IRGAN model is revisited to tackle semi-supervised information retrieval (IR)
problems, considering the premature convergence of IRGAN caused by mismatching the …

Detecting fraudulent online text is essential, as these manipulative messages exploit human
greed, deceive individuals, and endanger societal security. Currently, this task remains …

Online learning to rank (OLTR), which directly optimizes the ranker with interactive user
feedback, has gained considerable attention in both academia and industry. However, most …

Cloud providers are highly incentivized to reduce latency. One way they do this is by
locating data centers as close to users as possible. These “cloud edge” data centers are …