RoParQ: Paraphrase-Aware Alignment of Large Language Models Towards Robustness to Paraphrased Questions

• URL: http://arxiv.org/abs/2511.21568v1
• Date: Wed, 26 Nov 2025 16:40:53 GMT
• Title: RoParQ: Paraphrase-Aware Alignment of Large Language Models Towards Robustness to Paraphrased Questions
• Authors: Minjoon Choi,
• Abstract summary: Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions.<n>We introduce RoParQ, a benchmark to evaluate cross-paraphrase consistency in closed-book multiple-choice QA.<n>We also propose XParaCon, a novel evaluation metric that quantifies a model's robustness.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions, suggesting a reliance on surface-level patterns rather than true semantic understanding. To address this limitation, we introduce RoParQ, a benchmark specifically constructed to evaluate cross-paraphrase consistency in closed-book multiple-choice QA. This benchmark is derived from standard datasets by generating paraphrases via proprietary models and selectively retaining examples that elicit inconsistent confidence from a judge model. We further propose XParaCon, a novel evaluation metric that quantifies a model's robustness by measuring the standard deviation of accuracies across question variants. Additionally, we implement a reasoning-based, paraphrase-aware Supervised Fine-Tuning (SFT) strategy designed to align models toward semantic invariance. Our experiments demonstrate that this targeted alignment significantly enhances robustness. Notably, fine-tuned lightweight models achieved consistency levels comparable to much larger pre-trained models. These results highlight the efficacy of our approach in mitigating superficial memorization and fostering more robust, reliable LLMs.

Related papers

• IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation [85.56193980646981]
  We propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following.<n>For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses.<n>Experiments on IF-RewardBench reveal significant deficiencies in current judge models.
  arXiv  Detail & Related papers  (2026-03-05T02:21:17Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• Enhancing Language Models for Robust Greenwashing Detection [36.1214446480006]
  Greenwashing and vague claims undermine sustainability reports.<n>We propose a parameter-efficient framework that structures latent spaces by combining contrastive learning with an ordinal ranking objective.
  arXiv  Detail & Related papers  (2026-01-29T13:46:15Z)
• When Words Change the Model: Sensitivity of LLMs for Constraint Programming Modelling [1.052782170493037]
  Large language models show impressive results in automatically generating models for classical benchmarks.<n>Many standard CP problems are likely included in the training data of these models.<n>We show that while LLMs can produce syntactically valid and semantically plausible models, their performance drops sharply under contextual and linguistic variation.
  arXiv  Detail & Related papers  (2025-11-18T10:40:32Z)
• Efficient semantic uncertainty quantification in language models via diversity-steered sampling [46.23327887393273]
  We introduce a diversity-steered sampler that discourages semantically redundant outputs during decoding.<n>Key idea is to inject a continuous semantic-similarity penalty into the model's proposal distribution.<n>Being modular and requiring no gradient access to the base LLM, the framework promises to serve as a drop-in enhancement for uncertainty estimation.
  arXiv  Detail & Related papers  (2025-10-24T10:06:21Z)
• VAL-Bench: Measuring Value Alignment in Language Models [10.745372809345412]
  Large language models (LLMs) are increasingly used for tasks where outputs shape human decisions.<n>Existing benchmarks mostly track refusals or predefined safety violations but do not reveal whether a model upholds a coherent value system.<n>We introduce the Value ALignment Benchmark (VAL-Bench), which evaluates whether models maintain a stable value stance across paired that frame opposing sides of public debates.
  arXiv  Detail & Related papers  (2025-10-06T23:55:48Z)
• Model Correlation Detection via Random Selection Probing [62.093777777813756]
  Existing similarity-based methods require access to model parameters or produce scores without thresholds.<n>We introduce Random Selection Probing (RSP), a hypothesis-testing framework that formulates model correlation detection as a statistical test.<n>RSP produces rigorous p-values that quantify evidence of correlation.
  arXiv  Detail & Related papers  (2025-09-29T01:40:26Z)
• Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models [60.00178316095646]
  Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using datasets like NLI.<n>Recent studies leverage large language models (LLMs) to generate sentence pairs, reducing annotation dependency.<n>We propose a method for controlling the generation direction of LLMs in the latent space. Unlike unconstrained generation, the controlled approach ensures meaningful semantic divergence.<n> Experiments on multiple benchmarks demonstrate that our method achieves new SOTA performance with a modest cost in ranking sentence synthesis.
  arXiv  Detail & Related papers  (2025-02-19T12:07:53Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.