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Detecting Bias in LLMs' natural language Inference Using Metamorphic Testing 24

Detecting Bias in LLMs' Natural Language Inference Using Met...

引用

24th ieee international conference on Software Quality, Reliability and Security Companion, QRS-C 2024

作者： Li, Zhahao Chen, Jinfu Chen, Haibo Xu, Leyang Guo, Wuhao School of Computer Science & Communication Engineering Jiangsu University Zhenjiang212013 China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Zhenjiang212013 China Asiainfo Security Technology Co. Ltd Nanjing210012 China

ISBN: (纸本)9798350365658

The increasing integration of Large language Mod-els (LLMs) into pivotal decision-making contexts highlights the necessity for rigorous scrutiny to ensure fairness, especially in tasks involving natural language processing (NLP). This paper introduces a novel method employing Metamorphic Testing (MT) to discern bias within the natural language Inference (NLI) task performed by LLMs. Our approach establishes a structured framework for generating test cases using Metamorphic Relations (MRs), incorporating nuanced demographic attributes such as sex, race, occupation, age, and socioeconomic status into textual inputs. We conduct prelimi-nary experiment with 3 common datasets, over 400k test cases in 4 state-of-art LLMs. Through evaluation of model predictions across these MRs, we unveil potential biases indicative of fairness violations. Preliminary investigations conducted with prominent LLMs underscore the effectiveness of our method in illuminating disparities in model performance, thus providing valuable insights into the bias implications of LLMs in NLI tasks. Our method is intuitive and adaptable for other NLP tasks. To the best of our knowledge, this represents the first application of MT to NLI bias detection. © 2024 ieee.

关键词： natural language processing systems

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Hop-level Direct Preference Optimization for knowledge Graph Reasoning with Trees

Hop-level Direct Preference Optimization for Knowledge Graph...

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2025 ieee international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Shen, Tiesunlong Wang, Jin Zhang, Xuejie Cambria, Erik School of Information Science and Engineering Yunnan University China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350368741

Recent advancements in knowledge graph question answering (KGQA) have shown promise, yet existing methods often fail to align with human reasoning patterns. This study proposes HD-PORT (hop-level direct preference optimization for knowledge graph reasoning with trees), a novel approach that combines Monte Carlo Tree Search (MCTS) with hop-level direct preference optimization (HDPO) for KGQA tasks. MCTS simulates human-like reasoning by exploring multiple inference paths in knowledge graphs, generating interpretable reasoning chains and rich hop-level preferences. HDPO then leverages these preferences for model optimization, addressing the limitations of traditional supervised learning and existing DPO methods that focus on overall solution preferences. By optimizing preferences at each reasoning step, HD-PORT more closely mirrors human problem-solving strategies. Experimental results on benchmark datasets demonstrate that HD-PORT significantly outperforms state-of-the-art methods in both accuracy and interpretability, particularly for complex, multi-hop reasoning tasks. © 2025 ieee.

关键词： Graph signal processing knowledge graph Large language model Reasoning

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Deep Reasoning of Large Models Based on knowledge Graph 16

Deep Reasoning of Large Models Based on Knowledge Graph

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2024 16th international conference on Graphics and Image processing, ICGIP 2024

作者： Chenghao, Cao Du, Zhenlong Li, Xiaoli College of Computer and Information Engineering Nanjing Tech University China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination issues in scenarios requiring deep reasoning. Incorporating external knowledge into LLM reasoning can alleviate some of these issues. This paper proposes an efficient graph search method that integrates knowledge graphs. A knowledge graph is firstly constructed from the original document. This graph is then partitioned into multiple relevant communities using a community detection algorithm, and natural language summaries for each community are generated using an LLM. During the search phase, the LLM performs beam search on the knowledge graph, iteratively exploring multiple potential reasoning paths on the graph until it determines that it can answer the question based on the current reasoning path. Experiments demonstrate that our search method has lower computational costs and better generalizability, outperforming even large LLMs like GPT-4 in certain vertical domains. © 2025 SPIE.

关键词： knowledge graph

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Attention Distillation for Accuracy Improvement of Vision Transformer 12

Attention Distillation for Accuracy Improvement of Vision Tr...

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12th ieee international conference on Identification, Information and knowledge in the Internet of Things, IIKI 2024

作者： Tanaka, Taiga Ishibashi, Ryuto Meng, Lin Ritsumeikan University Graduate School of Science and Engineering 1-1-1 Noji-higashi Shiga Kusatsu525-8577 Japan Ritsumeikan University College of Science and Engineering 1-1-1 Noji-higashi Shiga Kusatsu525-8577 Japan

ISBN: (纸本)9798331510633

In recent years, rapid advancements in technology have propelled progress in the fields of image recognition and natural language processing, resulting in the development of numerous applications and services. In particular, the advancement of deep learning has greatly contributed to improving the accuracy of these fields, and the use of AI is becoming more widespread in everyday life [1]. Among them, Vision Transformers (ViT) has emerged as a robust architecture in image recognition. ViT requires a large dataset for training to improve accuracy. To solve this problem, DeiT using knowledge distillation has been proposed. However, this DeiT does not consider each token's contribution to [CLS] in learning. In this study, a knowledge distillation model training method called ADViT that uses the contribution of each token to Classify token ([CLS]) is proposed. Specifically, the attention map of each token for [CLS] is calculated. In each layer, learning is performed to bring the student model's attention map closer to the teacher model's attention map. Then, the accuracy is evaluated, and experimental results show the effectiveness of the proposed method, with accuracy rates of ADViT-Tiny(L1): 98.35%, ADViT-Tiny(L2): 98.06%, ADViT-Small(L1):99.00%, and ADViT-Small(L2): 98.75%, which are higher than those of DeiT, DeiT-Tiny: 96.39%, DeiT-Small: 96.83%. Future work includes managing the number of heads and reducing the computation for more optimization. © 2024 ieee.

关键词： natural language processing systems

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Towards Interpreting the Behavior of Large language Models on Software engineering Tasks 24

Towards Interpreting the Behavior of Large Language Models o...

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46th international conference on Software engineering: Companion, ICSE-Companion 2024

作者： Dipongkor, Atish Kumar Moran, Kevin University of Central Florida OrlandoFL United States

ISBN: (纸本)9798400705021

Large language Models (LLMs) have ushered in a significant breakthrough within the field of natural language processing. Building upon this achievement, analogous language models have been developed specifically for code-related tasks, commonly referred to as Large language Models for Code (LLMsC). Notable examples of LLMsC include CodeBERT, UnixCoder, CoPilot, among others. These models have demonstrated exceptional performance across various Software engineering (SE) tasks, encompassing code summarization, test case generation, natural language to code conversion, bug triaging, malware detection, program repair, and more. Despite the promising results achieved by LLMsC in SE tasks, there remains fundamental questions regarding their decision-making processes. Understanding these model decision mechanisms is crucial for further enhancing the performance of LLMsC. In pursuit of this objective, my PhD dissertation aims to pioneer novel methodologies for interpreting and comprehending the behavior of LLMsC. © 2024 ieee Computer Society. All rights reserved.

关键词： Software testing

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BigText-QA: Question Answering over a Large-Scale Hybrid knowledge Graph 13th

BigText-QA: Question Answering over a Large-Scale Hybrid Kno...

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13th international conference on Big Data Technologies and Applications (BDTA)

作者： Xu, Jingjing Biryukov, Maria Theobald, Martin Venugopal, Vinu Ellampallil Univ Luxembourg L-4365 Esch Sur Alzette Luxembourg Int Inst Informat Technol IIIT Bangalore Karnataka India

ISBN: (纸本)9783031522642;9783031522659

Answering complex questions over textual resources remains a challenge, particularly when dealing with nuanced relationships between multiple entities expressed within natural-language sentences. To this end, curated knowledge bases (KBs) like YAGO, DBpedia, Freebase, and Wikidata have been widely used and gained great acceptance for question-answering (QA) applications in the past decade. While these KBs offer a structured knowledge representation, they lack the contextual diversity found in natural-language sources. To address this limitation, BigText-QA introduces an integrated QA approach, which is able to answer questions based on a more redundant form of a knowledge graph (KG) that organizes both structured and unstructured (i.e., "hybrid") knowledge in a unified graphical representation. Thereby, BigText-QA is able to combine the best of both worlds-a canonical set of named entities, mapped to a structured background KB (such as YAGO or Wikidata), as well as an open set of textual clauses providing highly diversified relational paraphrases with rich context information. Our experimental results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based unsupervised QA system.

关键词： Question Answering Large-Scale Graph Hybrid knowledge Graph natural language processing

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Faithful Fake News Detection Based on natural language Explanation Generation 24

Faithful Fake News Detection Based on Natural Language Expla...

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Yoshida, Ko Zhang, Jianwei Graduate School of Information Sciences Tohoku University Miyagi Sendai Japan Faculty of Science and Engineering Iwate University Iwate Morioka Japan

ISBN: (纸本)9798400717383

In this paper, we propose a fake news detection model that generates natural language explanations and leverages these explanations for prediction. Deep learning models, particularly Large language Models like BERT, are effective for fake news detection. However, interpreting and explaining predictions from these complex models remains challenging. Algorithms like LIME and SHAP provide numerical explanations, but these are often insufficient for users without technical knowledge. Our model generates text-based explanations, making them more user-friendly. Experimental results show our model improves binary classification accuracy and F1-score. Additionally, we demonstrate that the explanations contribute to classification, ensuring the model's faithfulness to the generated explanations. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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CyGPT: knowledge Graph-Based Enhancement Techniques for Large language Models in Cybersecurity 9

CyGPT: Knowledge Graph-Based Enhancement Techniques for Larg...

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9th ieee international conference on Data Science in Cyberspace, DSC 2024

作者： Ou, Lu Ni, Xiaoya Wu, Wei Tian, Zhihong Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou510006 China

ISBN: (纸本)9798350391367

Large language Models (LLMs) excel in numerous natural language processing (NLP) tasks but encounter significant challenges in practical applications, including hallucinations, outdated information, and a lack of domain-specific external knowledge. This study proposes a collaborative, training-free reasoning approach, leveraging close cooperation between knowledge Graphs (KG) and LLMs for cybersecurity applications. Our approach employs the 'Joint Reasoning Chain,' which dynamically integrates information from network security-specific knowledge graphs, serving as an external knowledge base to enhance the domain-specific external knowledge of LLMs. This cooperative method not only improves reliable knowledge-based reasoning but also enhances the traceability of decision-making processes. Furthermore, we introduce a novel GPT-based technique to evaluate answer quality and have performed systematic experiments on a purpose-built test set. The results confirm that our method significantly boosts GPT's performance in network security knowledge, demonstrating the potential of knowledge graphs to augment LLMs' reasoning abilities and their applicability in specialized fields. © 2024 ieee.

关键词： knowledge graph

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Comparative Analysis of Large language Models Fine-tuned on a Singular Dataset 24

Comparative Analysis of Large Language Models Fine-tuned on ...

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8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Tadaga Gangadhar, Adithya Pranav Rao, Harsh Shylaja, S. Department of Computer Science Engineering PES University Karnataka Bangalore India

ISBN: (纸本)9798400717383

This paper presents a comparative analysis of several state of the art language models fine-tuned for question answering tasks using the ieee research papers dataset. The models evaluated include GPT-2, BERT, BART, Llama Mistral and RAG with GPT-3.5-Turbo. Each model was fine tuned and evaluated based on their accurate responses generated to the questions posed on different categories of academic paper titles and abstracts. The study employs evaluation metrics such as BLEU score, ROUGE 1 and ROUGE 2 to assess linguistic quality, factual accuracy and content overlap between generated answers and reference texts. The results obtained show the different performance characteristics of the models, providing information into their suitability for question answering in academic contexts. This paper aims to inform researchers and other users to select the appropriate models based on specific task requirements, improving the application of natural language processing in academic research domains. © 2024 Copyright held by the owner/author(s).

关键词： Question answering

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MApp-KG: Mobile App knowledge Graph for Document-Based Feature knowledge Generation 1

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36th international conference on Advanced Information Systems engineering (CAiSE)

作者： Motger, Quim Franch, Xavier Marco, Jordi Univ Politecn Cataluna Barcelona Spain

ISBN: (数字)9783031610004

ISBN: (纸本)9783031609992;9783031610004

Mobile app repositories serve as large-scale crowdsourced information systems used for various document-based software engineering tasks, leveraging product descriptions, user reviews, and other natural language documents. Particularly, feature extraction (i.e., identifying functionalities or capabilities of a mobile app mentioned in these documents) is key for product recommendation, topic modelling, and feedback analysis. However, researchers often face domain-specific challenges in mining these repositories, including the integration of heterogeneous data sources, large-scale data collection, normalization and ground-truth generation for feature-oriented tasks. In this paper, we introduce MAppKG, a combination of software resources and data artefacts in the field of mobile app repositories aimed at supporting feature-oriented knowledge generation tasks. Our contribution provides a framework for automatically constructing a knowledge graph that models a domain-specific catalog of natural language documents related to mobile applications. We distribute MApp-KG through a public triplestore, enabling its immediate use for future research and replication of our findings.

关键词： Mobile Apps knowledge Graph User Reviews natural language Document Feature Extraction RDF

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