SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models

• URL: http://arxiv.org/abs/2503.00137v1
• Date: Fri, 28 Feb 2025 19:27:29 GMT
• Title: SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models
• Authors: Grigor Nalbandyan, Rima Shahbazyan, Evelina Bakhturina,
• Abstract summary: We present SCORE ($mathbfS$ystematic $mathbfCO$nsistency and $mathbfR$obustness $mathbfE$valuation), a comprehensive framework for non-adversarial evaluation of Large Language Models.<n>The SCORE framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency.
• Score: 4.875712300661656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Typical evaluations of Large Language Models (LLMs) report a single metric per dataset, often representing the model's best-case performance under carefully selected settings. Unfortunately, this approach overlooks model robustness and reliability in real-world applications. For instance, simple paraphrasing of prompts on the MMLU-Pro dataset causes accuracy fluctuations of up to 10\%, while reordering answer choices in the AGIEval dataset results in accuracy differences of up to 6.1\%. While some studies discuss issues with LLM robustness, there is no unified or centralized framework for evaluating the robustness of language models. To address this gap and consolidate existing research on model robustness, we present SCORE ($\mathbf{S}$ystematic $\mathbf{CO}$nsistency and $\mathbf{R}$obustness $\mathbf{E}$valuation), a comprehensive framework for non-adversarial evaluation of LLMs. The SCORE framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. We release the code publicly and start an LLM robustness leaderboard to facilitate further development and research.

Related papers

• Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation [6.549143816134529]
  We introduce bftextBi'an, a novel framework featuring a bilingual benchmark dataset and lightweight judge models.<n>The dataset supports rigorous evaluation across multiple RAG scenarios, while the judge models are fine-tuned from compact open-source LLMs.
  arXiv  Detail & Related papers  (2025-02-26T15:12:59Z)
• Beyond the Singular: The Essential Role of Multiple Generations in Effective Benchmark Evaluation and Analysis [10.133537818749291]
  Large language models (LLMs) have demonstrated significant utilities in real-world applications.<n> Benchmark evaluations are crucial for assessing the capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-02-13T03:43:33Z)
• A Statistical Framework for Ranking LLM-Based Chatbots [57.59268154690763]
  We propose a statistical framework that incorporates key advancements to address specific challenges in pairwise comparison analysis.<n>First, we introduce a factored tie model that enhances the ability to handle groupings of human-judged comparisons.<n>Second, we extend the framework to model covariance tiers between competitors, enabling deeper insights into performance relationships.<n>Third, we resolve optimization challenges arising from parameter non-uniqueness by introducing novel constraints.
  arXiv  Detail & Related papers  (2024-12-24T12:54:19Z)
• Ranked from Within: Ranking Large Multimodal Models for Visual Question Answering Without Labels [64.94853276821992]
  Large multimodal models (LMMs) are increasingly deployed across diverse applications.<n>Traditional evaluation methods are largely dataset-centric, relying on fixed, labeled datasets and supervised metrics.<n>We explore unsupervised model ranking for LMMs by leveraging their uncertainty signals, such as softmax probabilities.
  arXiv  Detail & Related papers  (2024-12-09T13:05:43Z)
• MM-R$^3$: On (In-)Consistency of Multi-modal Large Language Models (MLLMs) [26.475993408532304]
  We study the ability of an MLLM model to produce semantically similar or identical responses to semantically similar queries. We propose the MM-R$3$ benchmark, which analyses the performance in terms of consistency and accuracy in SoTA MLLMs. Our analysis reveals that consistency does not always align with accuracy, indicating that models with higher accuracy are not necessarily more consistent, and vice versa.
  arXiv  Detail & Related papers  (2024-10-07T06:36:55Z)
• Quantifying Prediction Consistency Under Model Multiplicity in Tabular LLMs [10.494477811252034]
  Fine-tuning large language models can lead to textitfine-tuning multiplicity, where equally well-performing models make conflicting predictions on the same inputs. This raises critical concerns about the robustness and reliability of Tabular LLMs. This work proposes a novel metric to quantify the robustness of individual predictions without expensive model retraining.
  arXiv  Detail & Related papers  (2024-07-04T22:22:09Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation [51.99752147380505]
  This paper presents a benchmark self-evolving framework to dynamically evaluate Large Language Models (LLMs) We utilize a multi-agent system to manipulate the context or question of original instances, reframing new evolving instances with high confidence. Our framework widens performance discrepancies both between different models and within the same model across various tasks.
  arXiv  Detail & Related papers  (2024-02-18T03:40:06Z)
• Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations [111.88727295707454]
  This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. We conduct experiments on pre-trained language models for analysis and evaluation of OOD robustness.
  arXiv  Detail & Related papers  (2023-06-07T17:47:03Z)
• Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing [72.14557106085284]
  slice detection models (SDM) automatically identify underperforming groups of datapoints. This paper proposes a benchmark named "Discover, Explain, improve (DEIM)" for classification NLP tasks. Our evaluation shows that Edisa can accurately select error-prone datapoints with informative semantic features.
  arXiv  Detail & Related papers  (2022-11-08T19:00:00Z)
• 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.