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Found 10 papers, 3 papers with code
RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelines
Retrieval-augmented generation (RAG) pipelines have become the de-facto approach for building AI assistants with access to external, domain-specific knowledge.
LLM-Powered Proactive Data Systems
Such data systems do as they are told, but fail to understand and leverage what the LLM is being asked to do (i. e. the underlying operations, which may be error-prone), the data the LLM is operating on (e. g., long, complex documents), or what the user really needs.
Towards Establishing Guaranteed Error for Learned Database Operations
Machine learning models have demonstrated substantial performance enhancements over non-learned alternatives in various fundamental data management operations, including indexing (locating items in an array), cardinality estimation (estimating the number of matching records in a database), and range-sum estimation (estimating aggregate attribute values for query-matched records).
Theoretical Analysis of Learned Database Operations under Distribution Shift through Distribution Learnability
Use of machine learning to perform database operations, such as indexing, cardinality estimation, and sorting, is shown to provide substantial performance benefits.
NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval
Existing approaches either fine-tune the pre-trained model itself or, more efficiently, but at the cost of accuracy, train adaptor models to transform the output of the pre-trained model.
BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data
We show that statistical debiasing, although in some cases useful, often fails to improve accuracy.
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing
In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in $O(\log\log n)$ expected query time.
Towards Accurate Spatiotemporal COVID-19 Risk Scores using High Resolution Real-World Mobility Data
As countries look towards re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging.
Policy Tree Network
However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem.
Finding Average Regret Ratio Minimizing Set in Database
This problem takes into account the probability distribution of the users and considers the satisfaction (ratio) of all users, which is more reasonable in practice, compared with the existing studies that only consider the worst-case satisfaction (ratio) of the users, which may not reflect the whole population and is not useful in some applications.
