BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation

• URL: http://arxiv.org/abs/2601.18253v1
• Date: Mon, 26 Jan 2026 08:20:02 GMT
• Title: BoRP: Bootstrapped Regression Probing for Scalable and Human-Aligned LLM Evaluation
• Abstract summary: We introduce BoRP, a scalable framework for high-fidelity satisfaction evaluation.<n>Experiments on industrial datasets show that BoRP significantly outperforms generative baselines.<n>BoRP reduces inference costs by orders of magnitude, enabling full-scale monitoring and highly sensitive A/B testing via CUPED.
• Score: 13.561789180344533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate evaluation of user satisfaction is critical for iterative development of conversational AI. However, for open-ended assistants, traditional A/B testing lacks reliable metrics: explicit feedback is sparse, while implicit metrics are ambiguous. To bridge this gap, we introduce BoRP (Bootstrapped Regression Probing), a scalable framework for high-fidelity satisfaction evaluation. Unlike generative approaches, BoRP leverages the geometric properties of LLM latent space. It employs a polarization-index-based bootstrapping mechanism to automate rubric generation and utilizes Partial Least Squares (PLS) to map hidden states to continuous scores. Experiments on industrial datasets show that BoRP (Qwen3-8B/14B) significantly outperforms generative baselines (even Qwen3-Max) in alignment with human judgments. Furthermore, BoRP reduces inference costs by orders of magnitude, enabling full-scale monitoring and highly sensitive A/B testing via CUPED.

Related papers

• DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs [71.85279836937578]
  We introduce DeepBias, an adaptive framework for the in-depth probing of social biases in LVLMs with carefully designed agents.<n>We build a benchmark named DeepBiasBench using our framework. By employing an ensemble of five diverse state-of-the-art LVLMs as anchors, the benchmark captures vulnerabilities shared across architectures.
  arXiv  Detail & Related papers  (2026-07-13T08:19:09Z)
• Support Vector Rubrics: Closing the Gap Between Self-Generated and Human Rubrics [33.21526869949403]
  We introduce SVR (Support Vectors), a framework that recasts rubrics as max-margin boundary learning over preference data.<n> SVR mines contrastive features from preference pairs into a bank, learns a prompt-conditioned selector together with global weights, and iteratively refines bank through support-pair selection and adversarial probing of hard negatives.<n>OnBench, SVR narrows the gap to human references from 24.1 to 0.3 points and outperforms strong self-rubrics, and the learned bank transfers across judges without retraining.
  arXiv  Detail & Related papers  (2026-06-06T09:55:22Z)
• ODRPO: Ordinal Decompositions of Discrete Rewards for Robust Policy Optimization [14.900223489465683]
  Large Language Models (LLMs) utilize Reinforcement Learning from AI Feedback (RLAIF) for non-verifiable domains.<n>These domains often rely on based auto-raters to provide granular, multi-tier discrete rewards.<n>We introduce $textbfO$ptimization ($textbfOOO$), a framework that structurally isolates evaluation noise by decomposing discrete rewards into a sequence of ordinal binary indicators.
  arXiv  Detail & Related papers  (2026-05-12T19:17:14Z)
• Quantifying and Mitigating Self-Preference Bias of LLM Judges [5.017055831476582]
  Self-Preference Bias (SPB) is a directional evaluative deviation in which LLMs systematically favor or disfavor their own generated outputs during evaluation.<n>Existing measurements rely on costly human annotations and conflate generative capability with evaluative stance, and thus are impractical for large-scale deployment in real-world systems.<n>We introduce a fully automated framework to quantifying and mitigating SPB, which constructs equal-quality pairs of responses with negligible quality differences.
  arXiv  Detail & Related papers  (2026-04-24T09:46:22Z)
• Improving Search Agent with One Line of Code [68.58667107354253]
  Tool-based Agentic Reinforcement Learning (TARL) has emerged as a promising paradigm for training search agents to interact with external tools.<n>We propose textbfSearch textbfAgent textbfPolicy textbfOptimization (textbfSAPO), which stabilizes training via a conditional token-level KL constraint.
  arXiv  Detail & Related papers  (2026-03-10T04:07:39Z)
• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval [74.72139580745511]
  LaSER is a novel self-distillation framework that internalizes explicit reasoning into the latent space of retrievers.<n>Our method successfully combines the reasoning depth of explicit CoT pipelines with the inference efficiency of standard dense retrievers.
  arXiv  Detail & Related papers  (2026-03-02T04:11:18Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• ProAct: Agentic Lookahead in Interactive Environments [56.50613398808361]
  ProAct is a framework that enables agents to internalize accurate lookahead reasoning through a two-stage training paradigm.<n>We introduce Grounded LookAhead Distillation (GLAD), where the agent undergoes supervised fine-tuning on trajectories derived from environment-based search.<n>We also propose the Monte-Carlo Critic (MC-Critic), a plug-and-play auxiliary value estimator designed to enhance policy-gradient algorithms.
  arXiv  Detail & Related papers  (2026-02-05T05:45:16Z)
• Richer Bayesian Last Layers with Subsampled NTK Features [25.566044416945875]
  Bayesian Last Layers (BLLs) provide a convenient and computationally efficient way to estimate uncertainty in neural networks.<n>We propose a method that improves BLLs by leveraging a projection of Neural Tangent Kernel (NTK) features onto the space spanned by the last-layer features.<n>This enables posterior inference that accounts for variability of the full network while retaining the low computational cost of inference of a standard BLL.
  arXiv  Detail & Related papers  (2026-02-01T15:24:20Z)
• TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG [71.06073770344732]
  Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval.<n>We present TreePS-RAG, an online, tree-based RL framework for agentic RAG that enables step-wise credit assignment while retaining outcome-only rewards.
  arXiv  Detail & Related papers  (2026-01-11T14:07:30Z)
• Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translation Evaluation [50.83502171176548]
  State-of-the-art generative ESD methods typically decode using Maximum a Posteriori (MAP)<n>We address this issue by applying Minimum Bayes Risk (MBR) decoding to generative ESD models.
  arXiv  Detail & Related papers  (2025-12-08T13:21:44Z)
• Efficient Thought Space Exploration through Strategic Intervention [54.35208611253168]
  We propose a novel Hint-Practice Reasoning (HPR) framework that operationalizes this insight through two synergistic components.<n>The framework's core innovation lies in Distributional Inconsistency Reduction (DIR), which dynamically identifies intervention points.<n> Experiments across arithmetic and commonsense reasoning benchmarks demonstrate HPR's state-of-the-art efficiency-accuracy tradeoffs.
  arXiv  Detail & Related papers  (2025-11-13T07:26:01Z)
• Improving Reasoning for Diffusion Language Models via Group Diffusion Policy Optimization [25.504794432255306]
  Diffusion language models (DLMs) enable parallel, order-agnostic generation with iterative refinement.<n>Adapting reinforcement learning fine-tuning to DLMs remains an open challenge because of the intractable likelihood.<n>We introduce textbfGroup Diffusion Policy Optimization (GDPO), a new RL algorithm tailored for DLMs.
  arXiv  Detail & Related papers  (2025-10-09T17:58:07Z)
• SoftPQ: Robust Instance Segmentation Evaluation via Soft Matching and Tunable Thresholds [0.0]
  We propose SoftPQ, a flexible and interpretable instance segmentation metric.<n>We show that SoftPQ captures meaningful differences in segmentation quality that existing metrics overlook.
  arXiv  Detail & Related papers  (2025-05-17T22:08:33Z)
• RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation [73.2390735383842]
  We introduce the first sample-efficient algorithm for LMDPs without any additional structural assumptions. We show how these can be used to derive near-optimal guarantees of an optimistic exploration algorithm. These results can be valuable for a wide range of interactive learning problems beyond LMDPs, and especially, for partially observed environments.
  arXiv  Detail & Related papers  (2024-06-03T14:51:27Z)
• Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing [37.92922713921964]
  Persona-driven role-playing (PRP) aims to build AI characters that can respond to user queries by faithfully sticking with all persona statements. This paper presents a pioneering exploration to quantify PRP faithfulness as a fine-grained and explainable criterion, which also serves as a reliable reference for optimization.
  arXiv  Detail & Related papers  (2024-05-13T13:21:35Z)
• Provably Efficient UCB-type Algorithms For Learning Predictive State Representations [55.00359893021461]
  The sequential decision-making problem is statistically learnable if it admits a low-rank structure modeled by predictive state representations (PSRs) This paper proposes the first known UCB-type approach for PSRs, featuring a novel bonus term that upper bounds the total variation distance between the estimated and true models. In contrast to existing approaches for PSRs, our UCB-type algorithms enjoy computational tractability, last-iterate guaranteed near-optimal policy, and guaranteed model accuracy.
  arXiv  Detail & Related papers  (2023-07-01T18:35:21Z)
• Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning [125.8224674893018]
  Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Applying off-policy algorithms to offline RL usually fails due to the extrapolation error caused by the out-of-distribution (OOD) actions. We propose Pessimistic Bootstrapping for offline RL (PBRL), a purely uncertainty-driven offline algorithm without explicit policy constraints.
  arXiv  Detail & Related papers  (2022-02-23T15:27:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.