Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions

• URL: http://arxiv.org/abs/2506.11111v2
• Date: Wed, 09 Jul 2025 06:18:33 GMT
• Title: Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions
• Authors: Kun Zhang, Le Wu, Kui Yu, Guangyi Lv, Dacao Zhang,
• Abstract summary: Large Language Models (LLMs) have gained enormous attention in recent years due to their capability of understanding and generating natural languages.<n>This paper aims to provide a comprehensive terminology of concepts and methods around this field and facilitate the community.
• Score: 23.024212585005714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have gained enormous attention in recent years due to their capability of understanding and generating natural languages. With the rapid development and wild-range applications (e.g., Agents, Embodied Intelligence), the robustness of LLMs has received increased attention. As the core brain of many AI applications, the robustness of LLMs requires that models should not only generate consistent contents, but also ensure the correctness and stability of generated content when dealing with unexpeted application scenarios (e.g., toxic prompts, limited noise domain data, outof-distribution (OOD) applications, etc). In this survey paper, we conduct a thorough review of the robustness of LLMs, aiming to provide a comprehensive terminology of concepts and methods around this field and facilitate the community. Specifically, we first give a formal definition of LLM robustness and present the collection protocol of this survey paper. Then, based on the types of perturbated inputs, we organize this survey from the following perspectives: 1) Adversarial Robustness: tackling the problem that prompts are manipulated intentionally, such as noise prompts, long context, data attack, etc; 2) OOD Robustness: dealing with the unexpected real-world application scenarios, such as OOD detection, zero-shot transferring, hallucinations, etc; 3) Evaluation of Robustness: summarizing the new evaluation datasets, metrics, and tools for verifying the robustness of LLMs. After reviewing the representative work from each perspective, we discuss and highlight future opportunities and research directions in this field. Meanwhile, we also organize related works and provide an easy-to-search project (https://github.com/zhangkunzk/Awesome-LLM-Robustness-papers) to support the community.

Related papers

• Teaching Language Models To Gather Information Proactively [53.85419549904644]
  Large language models (LLMs) are increasingly expected to function as collaborative partners.<n>In this work, we introduce a new task paradigm: proactive information gathering.<n>We design a scalable framework that generates partially specified, real-world tasks, masking key information.<n>Within this setup, our core innovation is a reinforcement finetuning strategy that rewards questions that elicit genuinely new, implicit user information.
  arXiv  Detail & Related papers  (2025-07-28T23:50:09Z)
• LLM-Safety Evaluations Lack Robustness [58.334290876531036]
  We argue that current safety alignment research efforts for large language models are hindered by many intertwined sources of noise.<n>We propose a set of guidelines for reducing noise and bias in evaluations of future attack and defense papers.
  arXiv  Detail & Related papers  (2025-03-04T12:55:07Z)
• Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis [78.07225438556203]
  We introduce LLM-Oasis, the largest resource for training end-to-end factuality evaluators.<n>It is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts.<n>We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for factuality evaluation systems.
  arXiv  Detail & Related papers  (2024-11-29T12:21:15Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing [0.2302001830524133]
  This survey paper addresses the absence of a comprehensive overview on Retrieval-Augmented Language Models (RALMs)<n>The paper discusses the essential components of RALMs, including Retrievers, Language Models, and Augmentations.<n>RALMs demonstrate utility in a spectrum of tasks, from translation and dialogue systems to knowledge-intensive applications.
  arXiv  Detail & Related papers  (2024-04-30T13:14:51Z)
• FELM: Benchmarking Factuality Evaluation of Large Language Models [40.78878196872095]
  We introduce a benchmark for Factuality Evaluation of large Language Models, referred to as felm. We collect responses generated from large language models and annotate factuality labels in a fine-grained manner. Our findings reveal that while retrieval aids factuality evaluation, current LLMs are far from satisfactory to faithfully detect factual errors.
  arXiv  Detail & Related papers  (2023-10-01T17:37:31Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility [37.682136465784254]
  We conduct over a million queries to the mainstream large language models (LLMs) including ChatGPT, LLaMA, and OPT. We find that ChatGPT is still capable to yield the correct answer even when the input is polluted at an extreme level. We propose a novel index associated with a dataset that roughly decides the feasibility of using such data for LLM-involved evaluation.
  arXiv  Detail & Related papers  (2023-05-15T15:44:51Z)

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.