# Reliability-Aware Determinantal Point Processes for Robust Informative Data Selection in Large Language Models

arXiv: https://arxiv.org/abs/2602.00885 · HTML: https://mlsanity.com/paper/2602.00885/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving.
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 31 Jan 2026.

## Placed in

- Efficient LLM inference and serving / [Early exit and edge inference](https://mlsanity.com/inference/early-exit-and-edge-inference/)

## Builds on (most cited first, 24 of 24)

- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://mlsanity.com/paper/2310.06839/) (2023)
- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://mlsanity.com/paper/2403.12968/) (2024)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [Compressing Context to Enhance Inference Efficiency of Large Language Models](https://mlsanity.com/paper/2310.06201/) (2023)
- [Fit and Prune: Fast and Training-free Visual Token Pruning for Multi-modal Large Language Models](https://mlsanity.com/paper/2409.10197/) (2024)
- [MeetingBank: A Benchmark Dataset for Meeting Summarization](https://mlsanity.com/paper/2305.17529/) (2023)
- [A Survey on Data Selection for LLM Instruction Tuning](https://mlsanity.com/paper/2402.05123/) (2024)
- [Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://mlsanity.com/paper/2404.13948/) (2024)
- [Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies](https://mlsanity.com/paper/2406.06461/) (2024)
- [Efficient Learning in Large-Scale Combinatorial Semi-Bandits](https://mlsanity.com/paper/1406.7443/) (2014)
- [V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multimodal Large Language Models](https://mlsanity.com/paper/2502.09980/) (2025)
- [Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models](https://mlsanity.com/paper/2407.15504/) (2024)
- [Characterizing Prompt Compression Methods for Long Context Inference](https://mlsanity.com/paper/2407.08892/) (2024)
- [Evaluating the Retrieval Component in LLM-Based Question Answering Systems](https://mlsanity.com/paper/2406.06458/) (2024)
- [Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process](https://mlsanity.com/paper/2408.02103/) (2024)
- [Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications](https://mlsanity.com/paper/2306.16481/) (2023)
- [SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval](https://mlsanity.com/paper/2409.13992/) (2024)
- [Importance-Aware Data Selection for Efficient LLM Instruction Tuning](https://mlsanity.com/paper/2511.07074/) (2025)
- [3D-Prover: Diversity Driven Theorem Proving With Determinantal Point Processes](https://mlsanity.com/paper/2410.11133/) (2024)
- [Structured Robust Submodular Maximization: Offline and Online Algorithms](https://mlsanity.com/paper/1710.04740/) (2017)
- [RD-DPP: Rate-Distortion Theory Meets Determinantal Point Process to Diversify Learning Data Samples](https://mlsanity.com/paper/2304.04137/) (2023)
- [FedMIL: Federated-Multiple Instance Learning for Video Analysis with Optimized DPP Scheduling](https://mlsanity.com/paper/2403.17331/) (2024)
- [Extended Visibility of Autonomous Vehicles via Optimized Cooperative Perception under Imperfect Communication](https://mlsanity.com/paper/2503.18192/) (2025)

## Built on by (most cited first, 1 of 1)

- [LLM-Oriented Information Retrieval: A Denoising-First Perspective](https://mlsanity.com/paper/2605.00505/) (2026)

## Abstract (arXiv)

Informative data selection is a key requirement for large language models (LLMs) to minimize the amount of data required for fine-tuning, network distillation, and token pruning, enabling fast and efficient deployment, especially under computational and communication constraints. Traditional subset selection methods, including those based on Determinantal Point Processes (DPP), focus on maximizing diversity but assume that selected data batches are always available error-free. This presumption prohibits their use under partial storage outage, imperfect communication, and stochastic access failures. Furthermore, we show that the original formulation collapses under such conditions. To address this gap, we introduce ProbDPP, a novel reliability-aware implementation of k-DPP that accounts for probabilistic data access by recasting the objective function with a regularization term that remains well-posed and decomposes into a geometric diversity term and unreliability cost. The resulting objective facilitates robust selection of diverse data batches under uncertainty. Furthermore, we frame this reliability-aware diversity maximization as a combinatorial semi-bandit problem and propose a UCB-style algorithm to efficiently learn the unknown reliability online. Theoretical analysis provides regret bounds for the proposed approach, ensuring performance guarantees.