Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence. However, evaluating the answer's attribution, i.e., whether every claim within the generated responses is fully supported by its cited evidence, remains an open problem. This verification, traditionally dependent on costly human evaluation, underscores the urgent need for automatic attribution evaluation methods. To bridge the gap in the absence of standardized benchmarks for these methods, we present AttributionBench, a comprehensive benchmark compiled from various existing attribution datasets. Our extensive experiments on AttributionBench reveal the challenges of automatic attribution evaluation, even for state-of-the-art LLMs. Specifically, our findings show that even a fine-tuned GPT-3.5 only achieves around 80% macro-F1 under a binary classification formulation. A detailed analysis of more than 300 error cases indicates that a majority of failures stem from the model's inability to process nuanced information, and the discrepancy between the information the model has access to and that human annotators do.
Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其交叉应用关键实验室（东南大学））；The University of Manchester（曼彻斯特大学）；The University of Edinburgh（爱丁堡大学）；Law and Innovation Lab, School of Law, Southeast University（东南大学法学院创新与法律实验室）；State Key Lab. for Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；The Chinese University of Hong Kong（香港中文大学）；Monash University（莫纳什大学）
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
Quality and Usability Lab, Technische Universität Berlin（柏林技术大学质量与可用性实验室）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Deutsche Telekom（德国电信）；Saarland Informatics Campus（萨尔州信息学校区）；Centre for European Research in Trusted AI (CERTAIN)（可信人工智能欧洲研究中心）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究院）
Stevens Institute of Technology（斯蒂文斯理工学院）；The University of Sydney（悉尼大学）；Zhejiang University（浙江大学）
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map. Despite the promising results achieved, such methods are sensitive to the non-informative high-frequency components and lack adaptability for various models and samples. In this paper, we propose a dedicated method to generate attribution maps that allow us to learn the propagation rules automatically, overcoming the flaws of the handcrafted ones. Specifically, we introduce a learn…
University of Konstanz（康斯坦茨大学）；Offenburg University of Applied Sciences（奥芬堡应用科学大学）
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation…
Med-V1: Small Language Models for Zero-shot and Scalable Biomedical Evidence Attribution
Division of Intramural Research, National Library of Medicine, National Institutes of Health（国家医学图书馆内部研究部，国立卫生研究院）；Department of Computer Science, University of Virginia（弗吉尼亚大学计算机科学系）；Center for Cancer Research, National Cancer Institute, National Institutes of Health（国家癌症研究所癌症研究中心，国立卫生研究院）；Department of Population Health Sciences, Weill Cornell Medicine Institute of AI for Digital Health, Weill Cornell Medicine（韦尔·科恩医学中心流行病学与健康科学系，韦尔·科恩医学中心人工智能与数字健康研究所）
Large language models (LLMs) have shown impressive results while requiring little or no direct supervision. Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios. We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting. We formulate and study Attributed QA as a key first step in the development of attributed LLMs. We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures. We take human annotations as a gold standard and show that a correlated automat…
Listening or Reading? Evaluating Speech Awareness in Chain-of-Thought Speech-to-Text Translation
Barcelona Supercomputing Center, Spain（巴塞罗那超级计算中心）；Universitat Politècnica de Catalunya, Spain（加泰罗尼亚理工大学）；DFKI GmbH, Saarland Informatics Campus, Saarbrücken, Germany（DFKI GmbH萨尔兰信息技术校区）
While many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we introduce a framework to quantify the value of explanations via the accuracy gains that they confer on a student model trained to simulate a teacher model. Crucially, the explanations are available to the student during training, but are not available at test time. Compared to prior proposals, our approach is less easily gamed, enabling principled, automatic, model-agnostic evaluation of attributions. Using…
Fraunhofer AISEC（弗劳恩霍夫应用与集成安全研究所）；Technical University Munich（慕尼黑工业大学）
Automatic speech recognition (ASR) is improving ever more at mimicking human speech processing. The functioning of ASR, however, remains to a large extent obfuscated by the complex structure of the deep neural networks (DNNs) they are based on. In this paper, we show how so-called attribution methods, that we import from image recognition and suitably adapt to handle audio data, can help to clarify the working of ASR. Taking DeepSpeech, an end-to-end model for ASR, as a case study, we show how these techniques help to visualize which features of the input are the most influential in determinin…
