The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retrieval researchers together around the…
Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This…
vAttention is a memory manager for KV-cache in LLM serving systems. It decouples the allocation of virtual memory and physical memory using the CUDA virtual memory APIs. This approach enables allocating physical memory on demand while retaining the contiguity of KV-cache in virtual memory. This…
In this issue: Skeleton Posterior-guided OpTimization (SPOT) exhibits potential in various causal discovery tasks; Using visual imagery for an EEG-based brain–computer interface; Developing human-centered AI systems to assist creative professionals.
Microsoft Orleans is a popular open source distributed programming framework and platform which invented the virtual actor model, and has since evolved into an actor-oriented database system with the addition of database abstractions such as ACID transactions. Properties of Orleans' virtual actor model imply that…
The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the power multimodal models like GPT-4V as a general agent on multiple operating systems across different applications is largely underestimated due…
Offline Reinforcement Learning (RL) aims to learn policies from pre-collected datasets that capture only a subset of the environment's dynamics. The predominant approach has been to solve a constrained optimization formulation, which ensures that the policy visits state-action pairs within the support of the offline…
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the partial correlation between the connected features which gives information about their direct dependence. In this survey, we list out different…
Foundation models are rapidly being developed for computational pathology applications. However, it remains an open question which factors are most important for downstream performance with data scale and diversity, model size, and training algorithm all playing a role. In this work, we present the result…
AI-generated deepfakes are realistic, easy for nearly anyone to make, and increasingly being used for fraud, abuse, and manipulation – especially to target kids and seniors. While the tech sector and non-profit groups have taken recent steps to address this problem, it has become apparent…
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automate these high-value generic R&D processes through our open source R&D…
A lack of appropriate data, decreased model performance, and other obstacles have made it difficult to expand the input language models can receive. Li Lyna Zhang introduces LongRoPE, a method capable of extending content windows to more than 2 million tokens.
