# itemDataValues

value:
  Grammar checking is the task of detection and correction of grammatical errors in the text. English is the dominating language in the field of science and technology. Therefore, the non-native English speakers must be able to use correct English grammar while reading, writing or speaking. This generates the need of automatic grammar checking tools. So far many approaches have been proposed and implemented. But less efforts have been made in surveying the literature in the past decade. The objective of this systematic review is to examine the existing literature, highlighting the current issues and suggesting the potential directions of future research. This systematic review is a result of analysis of 12 primary studies obtained after designing a search strategy for selecting papers found on the web. We also present a possible scheme for the classification of grammar errors. Among the main observations, we found that there is a lack of efficient and robust grammar checking tools for real time applications. We present several useful illustrations- most prominent are the schematic diagrams that we provide for each approach and a table that summarizes these approaches along different dimensions such as target error types, linguistic dataset used, strengths and limitations of the approach. This facilitates better understandability, comparison and evaluation of previous research.


value:
  Natural language processing (NLP) is the study of mathematical and computational modeling of various aspects of language and the development of a wide range of systems. These include spoken language systems that integrate speech and natural language; cooperative interfaces to databases and knowledge bases that model aspects of human-human interaction; multilingual interfaces; machine translation; and message-understanding systems, among others. Research in NLP is highly interdisciplinary, involving concepts in computer science, linguistics, logic, and psychology. NLP has a special role in computer science because many aspects of the field deal with linguistic features of computation and NLP seeks to model language computationally.


value:
  "The book provides a technical perspective on the most contemporary data-driven approaches, focusing on techniques from supervised and unsupervised machine learning. It also includes background in the salient linguistic issues, as well as computational representations and algorithms. The first section of the book explores what can be with individual words. The second section concerns structured representations such as sequences, trees, and graphs. The third section highlights different approaches to the representation and analysis of linguistic meaning. The final section describes three of the most transformative applications of natural language processing: information extraction, machine translation, and text generation. The book describes the technical foundations of the field, including the most relevant machine learning techniques, algorithms, and linguistic representations. From these foundations, it extends to contemporary research in areas such as deep learning. Each chapter contains exercises that include paper-and-pencil analysis of the computational algorithms and linguistic issues, as well as software implementations"--Provided by publisher


value:
  Automated essay scoring (AES) is the use of specialized computer programs to assign grades to essays written in an educational setting. It is a form of educational assessment and an application of natural language processing. Its objective is to classify a large set of textual entities into a small number of discrete categories, corresponding to the possible grades, for example, the numbers 1 to 6. Therefore, it can be considered a problem of statistical classification.
  Several factors have contributed to a growing interest in AES. Among them are cost, accountability, standards, and technology. Rising education costs have led to pressure to hold the educational system accountable for results by imposing standards. The advance of information technology promises to measure educational achievement at reduced cost.
  The use of AES for high-stakes testing in education has generated significant backlash, with opponents pointing to research that computers cannot yet grade writing accurately and arguing that their use for such purposes promotes teaching writing in reductive ways (i.e. teaching to the test).


value:
  AI-driven automated scoring systems offer scalable and efficient means of evaluating complex student-generated responses. Yet, despite increasing demand for transparency and interpretability, the field has yet to develop a widely accepted solution for interpretable automated scoring to be used in large-scale real-world assessments. This work takes a principled approach to address this challenge. We analyze the needs and potential benefits of interpretable automated scoring for various assessment stakeholders and develop four principles of interpretability -- Faithfulness, Groundedness, Traceability, and Interchangeability (FGTI) -- targeted at those needs. To illustrate the feasibility of implementing these principles, we develop the AnalyticScore framework for short answer scoring as a baseline reference framework for future research. AnalyticScore operates by (1) extracting explicitly identifiable elements of the responses, (2) featurizing each response into human-interpretable values using LLMs, and (3) applying an intuitive ordinal logistic regression model for scoring. In terms of scoring accuracy, AnalyticScore outperforms many uninterpretable scoring methods, and is within only 0.06 QWK of the uninterpretable SOTA on average across 10 items from the ASAP-SAS dataset. By comparing against human annotators conducting the same featurization task, we further demonstrate that the featurization behavior of AnalyticScore aligns well with that of humans.


value:
  Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.


value:
  Automated Essay Scoring Using Advanced Transformer-Based Models for Comprehensive Assessment Evaluation


value:
  Automated essay scoring (AES) is essential in the field of educational technology, providing rapid and accurate evaluations of student writing. This study presents an innovative AES method that integrates Sentence-BERT (SBERT) with Long Short-Term Memory (LSTM) networks and attention mechanisms to improve the scoring process. SBERT generates embedding vectors for each essay, which are subsequently analyzed using a bidirectional LSTM (BiLSTM) to learn the features of these embedding vectors. An attention layer is introduced to enable the system to prioritize the most significant components of the essay. Evaluated using a benchmark dataset, our approach shows significant improvements in scoring accuracy, highlighting its ability to improve the reliability and efficiency of automated assessment systems.


value:
  Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


value:
  Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


value:
  Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Triples Modeling


value:
  LLMs obtain remarkable performance but suffer from hallucinations. Most research on detecting hallucination focuses on questions with short and concrete correct answers that are easy to check faithfulness. Hallucination detections for text generation with open-ended answers are more hard. Some researchers use external knowledge to detect hallucinations in generated texts, but external resources for specific scenarios are hard to access. Recent studies on detecting hallucinations in long texts without external resources conduct consistency comparison among multiple sampled outputs. To handle long texts, researchers split long texts into multiple facts and individually compare the consistency of each pair of facts. However, these methods (1) hardly achieve alignment among multiple facts; (2) overlook dependencies between multiple contextual facts. In this paper, we propose a graph-based context-aware (GCA) hallucination detection method for text generations, which aligns facts and considers the dependencies between contextual facts in consistency comparison. Particularly, to align multiple facts, we conduct a triple-oriented response segmentation to extract multiple knowledge triples. To model dependencies among contextual triples (facts), we construct contextual triples into a graph and enhance triples’ interactions via message passing and aggregating via RGCN. To avoid the omission of knowledge triples in long texts, we conduct an LLM-based reverse verification by reconstructing the knowledge triples. Experiments show that our model enhances hallucination detection and excels all baselines.


value:
  Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. It learns to represent text as a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state of the art for large language models. As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. 
  BERT is trained by masked token prediction and next sentence prediction. With this training, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. It improved on ELMo and spawned the study of "BERTology", which attempts to interpret what is learned by BERT.
  BERT was originally implemented in the English language at two model sizes, BERTBASE (110 million parameters) and BERTLARGE (340 million parameters). Both were trained on the Toronto BookCorpus (800M words) and English Wikipedia  (2,500M words). The weights were released on GitHub. On March 11, 2020, 24 smaller models were released, the smallest being BERTTINY with just 4 million parameters.


value:
  Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)


value:
  Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source. We conduct an extensive human evaluation to obtain FACTSCOREs of people biographies generated by several state-of-the-art commercial LMs -- InstructGPT, ChatGPT, and the retrieval-augmented PerplexityAI -- and report new analysis demonstrating the need for such a fine-grained score (e.g., ChatGPT only achieves 58%). Since human evaluation is costly, we also introduce an automated model that estimates FACTSCORE using retrieval and a strong language model, with less than a 2% error rate. Finally, we use this automated metric to evaluate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evaluated by humans, with various findings: GPT-4 and ChatGPT are more factual than public models, and Vicuna and Alpaca are some of the best public models. FACTSCORE is available for public use via `pip install factscore`.


value:
  HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses


value:
  Recent advances in large language models (LLMs) have shown promising improvements, often surpassing existing methods across a wide range of downstream tasks in natural language processing. However, these models still face challenges, which may hinder their practical applicability. For example, the phenomenon of hallucination is known to compromise the reliability of LLMs, especially in fields that demand high factual precision. Current benchmarks primarily focus on hallucination detection and factuality evaluation but do not extend beyond identification. This paper proposes an explanation enhanced hallucination-detection model, coined as HuDEx, aimed at enhancing the reliability of LLM-generated responses by both detecting hallucinations and providing detailed explanations. The proposed model provides a novel approach to integrate detection with explanations, and enable both users and the LLM itself to understand and reduce errors. Our measurement results demonstrate that the proposed model surpasses larger LLMs, such as Llama3 70B and GPT-4, in hallucination detection accuracy, while maintaining reliable explanations. Furthermore, the proposed model performs well in both zero-shot and other test environments, showcasing its adaptability across diverse benchmark datasets. The proposed approach further enhances the hallucination detection research by introducing a novel approach to integrating interpretability with hallucination detection, which further enhances the performance and reliability of evaluating hallucinations in language models.


value:
  Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference


value:
  Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite being the workhorse of numerous production pipelines, there have been limited Pareto improvements to BERT since its release. In this paper, we introduce ModernBERT, bringing modern model optimizations to encoder-only models and representing a major Pareto improvement over older encoders. Trained on 2 trillion tokens with a native 8192 sequence length, ModernBERT models exhibit state-of-the-art results on a large pool of evaluations encompassing diverse classification tasks and both single and multi-vector retrieval on different domains (including code). In addition to strong downstream performance, ModernBERT is also the most speed and memory efficient encoder and is designed for inference on common GPUs.


value:
  Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language Models


value:
  Automated data curation for niche scientific topics, where data quality and contextual accuracy are paramount, poses significant challenges. Bidirectional contextual models such as BERT and ELMo excel in contextual understanding and determinism. However, they are constrained by their narrower training corpora and inability to synthesize information across fragmented or sparse contexts. Conversely, autoregressive generative models like GPT can synthesize dispersed information by leveraging broader contextual knowledge and yet often generate plausible but incorrect (“hallucinated”) information. To address these complementary limitations, we propose an ensemble approach that combines the deterministic precision of BERT/ELMo with the contextual depth of GPT. We have developed a hierarchical knowledge extraction framework to identify perovskites and their associated solvents in perovskite synthesis, progressing from broad topics to narrower details using two complementary methods. The first method leverages deterministic models like BERT/ELMo for precise entity extraction, while the second employs GPT for broader contextual synthesis and generalization. Outputs from both methods are validated through structure-matching and entity normalization, ensuring consistency and traceability. In the absence of benchmark data sets for this domain, we hold out a subset of papers for manual verification to serve as a reference set for tuning the rules for entity normalization. This enables quantitative evaluation of model precision, recall, and structural adherence while also providing a grounded estimate of model confidence. By intersecting the outputs from both methods, we generate a list of solvents with maximum confidence, combining precision with contextual depth to ensure accuracy and reliability. This approach increases precision at the expense of recall─a trade-off we accept given that, in high-trust scientific applications, minimizing hallucinations is often more critical than achieving full coverage, especially when downstream reliability is paramount. As a case study, the curated data set is used to predict the endocrine-disrupting (ED) potential of solvents with a pretrained deep learning model. Recognizing that machine learning models may not be trained on niche data sets such as perovskite-related solvents, we have quantified epistemic uncertainty using Shannon entropy. This measure evaluates the confidence of the ML model predictions, independent of uncertainties in the NLP-based data curation process, and identifies high-risk solvents requiring further validation. Additionally, the manual verification pipeline addresses ethical considerations around trust, structure, and transparency in AI-curated data sets. © 2025 The Authors. Published by American Chemical Society


value:
  Survey and analysis of hallucinations in large language models: attribution to prompting strategies or model behavior


value:
  Hallucination in Large Language Models (LLMs) refers to outputs that appear fluent and coherent but are factually incorrect, logically inconsistent, or entirely fabricated. As LLMs are increasingly deployed in education, healthcare, law, and scientific research, understanding and mitigating hallucinations has become critical. In this work, we present a comprehensive survey and empirical analysis of hallucination attribution in LLMs. Introducing a novel framework to determine whether a given hallucination stems from not optimize prompting or the model's intrinsic behavior. We evaluate state-of-the-art LLMs-including GPT-4, LLaMA 2, DeepSeek, and others-under various controlled prompting conditions, using established benchmarks (TruthfulQA, HallucinationEval) to judge factuality. Our attribution framework defines metrics for Prompt Sensitivity (PS) and Model Variability (MV), which together quantify the contribution of prompts vs. model-internal factors to hallucinations. Through extensive experiments and comparative analyses, we identify distinct patterns in hallucination occurrence, severity, and mitigation across models. Notably, structured prompt strategies such as chain-of-thought (CoT) prompting significantly reduce hallucinations in prompt-sensitive scenarios, though intrinsic model limitations persist in some cases. These findings contribute to a deeper understanding of LLM reliability and provide insights for prompt engineers, model developers, and AI practitioners. We further propose best practices and future directions to reduce hallucinations in both prompt design and model development pipelines.


value:
  Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.


value:
  Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under RAG, it is important to create benchmark datasets that can measure the extent of hallucination. This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains and tasks within the standard RAG frameworks for LLM applications. RAGTruth comprises nearly 18,000 naturally generated responses from diverse LLMs using RAG. These responses have undergone meticulous manual annotations at both the individual cases and word levels, incorporating evaluations of hallucination intensity. We not only benchmark hallucination frequencies across different LLMs, but also critically assess the effectiveness of several existing hallucination detection methodologies. Furthermore, we show that using a high-quality dataset such as RAGTruth, it is possible to finetune a relatively small LLM and achieve a competitive level of performance in hallucination detection when compared to the existing prompt-based approaches using state-of-the-art large language models such as GPT-4.


value:
  Large Language Models (LLMs) have demonstrated remarkable capabilities, revolutionizing the integration of AI in daily life applications. However, they are prone to hallucinations, generating claims that contradict established facts, deviating from prompts, and producing inconsistent responses when the same prompt is presented multiple times. Addressing these issues is challenging due to the lack of comprehensive and easily assessable benchmark datasets. Most existing datasets are small and rely on multiple-choice questions, which are inadequate for evaluating the generative prowess of LLMs. To measure hallucination in LLMs, this paper introduces a comprehensive benchmark dataset comprising over 75,000 prompts across eight domains. These prompts are designed to elicit definitive, concise, and informative answers. The dataset is divided into two segments: one publicly available for testing and assessing LLM performance and a hidden segment for benchmarking various LLMs. In our experiments, we tested six LLMs-GPT-3.5, LLama 2, LLama 3, Gemini, Mixtral, and Zephyr-revealing that overall factual hallucination ranges from 59% to 82% on the public dataset and 57% to 76% in the hidden benchmark. Prompt misalignment hallucination ranges from 6% to 95% in the public dataset and 17% to 94% in the hidden counterpart. Average consistency ranges from 21% to 61% and 22% to 63%, respectively. Domain-wise analysis shows that LLM performance significantly deteriorates when asked for specific numeric information while performing moderately with person, location, and date queries. Our dataset demonstrates its efficacy and serves as a comprehensive benchmark for LLM performance evaluation. Our dataset and LLMs responses are available at \href{https://github.com/ashikiut/DefAn}{https://github.com/ashikiut/DefAn}.


value:
  Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that addresses two critical limitations in existing hallucination detection methods: (1) the context window constraints of traditional encoder-based methods, and (2) the computational inefficiency of LLM based approaches. Building on ModernBERT's extended context capabilities (up to 8k tokens) and trained on the RAGTruth benchmark dataset, our approach outperforms all previous encoder-based models and most prompt-based models, while being approximately 30 times smaller than the best models. LettuceDetect is a token-classification model that processes context-question-answer triples, allowing for the identification of unsupported claims at the token level. Evaluations on the RAGTruth corpus demonstrate an F1 score of 79.22% for example-level detection, which is a 14.8% improvement over Luna, the previous state-of-the-art encoder-based architecture. Additionally, the system can process 30 to 60 examples per second on a single GPU, making it more practical for real-world RAG applications.


value:
  We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. A distinct production version of Codex powers GitHub Copilot. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28.8% of the problems, while GPT-3 solves 0% and GPT-J solves 11.4%. Furthermore, we find that repeated sampling from the model is a surprisingly effective strategy for producing working solutions to difficult prompts. Using this method, we solve 70.2% of our problems with 100 samples per problem. Careful investigation of our model reveals its limitations, including difficulty with docstrings describing long chains of operations and with binding operations to variables. Finally, we discuss the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics.


value:
  Generative models such as large language models are extensively used as code copilots and for whole program generation. However, the programs they generate often have questionable correctness, authenticity and reliability in terms of integration as they might not follow the user requirements, provide incorrect and/or nonsensical outputs, or even contain semantic/syntactic errors - overall known as LLM hallucination. In this work, we present several types of code hallucination. We have generated such hallucinated code manually using large language models. We also present a technique - HallTrigger, in order to demonstrate efficient ways of generating arbitrary code hallucination. Our method leverages 3 different dynamic attributes of LLMs to craft prompts that can successfully trigger hallucinations from models without the need to access model architecture or parameters. Results from popular blackbox models suggest that HallTrigger is indeed effective and the pervasive LLM hallucination have sheer impact on software development.


value:
  Large Language Models (LLMs) have shown promising potentials in program generation and no-code automation. However, LLMs are prone to generate hallucinations, i.e., they generate text which sounds plausible but is incorrect. Although there has been a recent surge in research on LLM hallucinations for text generation, similar hallucination phenomenon can happen in code generation. Sometimes the generated code can have syntactical or logical errors as well as more advanced issues like security vulnerabilities, memory leaks, etc. Given the wide adaptation of LLMs to enhance efficiency in code generation and development in general, it becomes imperative to investigate hallucinations in code generation. To the best of our knowledge, this is the first attempt at studying hallucinations in the code generated by LLMs. We start by introducing the code hallucination definition and a comprehensive taxonomy of code hallucination types. We propose the first benchmark CodeMirage dataset for code hallucinations. The benchmark contains 1,137 GPT-3.5 generated hallucinated code snippets for Python programming problems from two base datasets - HumanEval and MBPP. We then propose the methodology for code hallucination detection and experiment with open source LLMs such as CodeLLaMA as well as OpenAI's GPT-3.5 and GPT-4 models using one-shot prompt. We find that GPT-4 performs the best on HumanEval dataset and gives comparable results to the fine-tuned CodeBERT baseline on MBPP dataset. Towards the end, we discuss various mitigation strategies for code hallucinations and conclude our work.


value:
  Recent technical breakthroughs in large language models (LLMs) have enabled them to fluently generate source code. Software developers often leverage both general-purpose and code-specialized LLMs to revise existing code or even generate a whole function from scratch. These capabilities are also beneficial in no-code or low-code contexts, in which one can write programs without a technical background. However, due to their internal design, LLMs are prone to generating hallucinations, which are incorrect, nonsensical, and not justifiable information but difficult to identify its presence. This problem also occurs when generating source code. Once hallucinated code is produced, it is often challenging for users to identify and fix it, especially when such hallucinations can be identified under specific execution paths. As a result, the hallucinated code may remain unnoticed within the codebase. This survey investigates recent studies and techniques relevant to hallucinations generated by CodeLLMs. We categorize the types of hallucinations in the code generated by CodeLLMs, review existing benchmarks and mitigation strategies, and identify open challenges. Based on these findings, this survey outlines further research directions in the detection and removal of hallucinations produced by CodeLLMs.


value:
  Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.


# itemNotes

title: Literatur-Review stammt von 2021, seit dem hat sich im Bereich KI eine Menge verändert.
note:
  <div class="zotero-note znv1"><div data-schema-version="9"><p>Literatur-Review stammt von 2021, seit dem hat sich im Bereich KI eine Menge verändert.</p>
  </div></div>


title: Sehr Interessantes Paper um Training von Halluzination zu optimieren!
note:
  <div class="zotero-note znv1"><div data-schema-version="9"><p>Sehr Interessantes Paper um Training von Halluzination zu optimieren!</p>
  </div></div>


title: Eher weniger interessant weil der Fokus auf img2text geht. Methodik von interesse.
note:
  <div class="zotero-note znv1"><div data-schema-version="9"><p>Eher weniger interessant weil der Fokus auf img2text geht. <br><br>Methodik von interesse.</p>
  </div></div>


note:
  <div class="zotero-note znv1">Comment: 25 pages; 7 figures. Published as a main conference paper at EMNLP 2023. Code available at https://github.com/shmsw25/FActScore</div>

title:
  Comment: 25 pages; 7 figures. Published as a main conference paper at EMNLP 2023. Code available at https://github.com/s


note:
  <div class="zotero-note znv1"><div data-schema-version="9"><p>Erstellt wurde ein Model, welches von Llama3.1:70B ausging und anschließend durch training neue parameter übergeben wurde.<br><br>Dieser Ansatz ist im Rahmen der Thesis sehr interessant, da man hier anhand eines bereits existierenden Modells weiterarbeiten und diesen verbessern kann.<br><br>Problem ist das das resultierende Modell groß bleibt und dementsprechend nicht Effizient genug wäre um diesen ständig anzuwenden.<br><br>Dafür bietet es aber verständliche Prompts und liefert vergleichbare Ergebnisse für die eigene Thesisarbeit.</p>
  </div></div>

title:
  Erstellt wurde ein Model, welches von Llama3.1:70B ausging und anschließend durch training neue parameter übergeben wurd


title: Hier wurde ein ähnliches Verfahren durchgeführt wie das was ich Erstellen will.
note:
  <div class="zotero-note znv1"><div data-schema-version="9"><p>Hier wurde ein ähnliches Verfahren durchgeführt wie das was ich Erstellen will.</p>
  <p></p>
  <p></p>
  </div></div>


title: Comment: corrected typos, added references, added authors, added acknowledgements
note:
  <div class="zotero-note znv1">Comment: corrected typos, added references, added authors, added acknowledgements</div>


# itemAttachments

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path:
  storage:Soni and Thakur - 2018 - A Systematic Review of Automated Grammar Checking in English Language.pdf


contentType: application/pdf
path:
  storage:Zhang et al. - 2025 - Ethical Issues in AI-Generated Texts A Systematic Review and Analysis.pdf


contentType: application/pdf
path:
  storage:Ramesh and Sanampudi - 2022 - An automated essay scoring systems a systematic literature review.pdf


contentType: application/pdf
path:
  storage:Kim et al. - 2025 - Principled Design of Interpretable Automated Scoring for Large-Scale Educational Assessments.pdf


contentType: application/pdf
path:
  storage:Mohamed Noor and Mohamed Badauraudine - 2025 - Evaluating Small BERT Based Models on Automated Essay Scoring Task.pdf


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  storage:Chen et al. - 2018 - Neural Natural Language Inference Models Enhanced with External Knowledge.pdf


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  storage:Chen et al. - 2024 - Unified Hallucination Detection for Multimodal Large Language Models.pdf


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  storage:Fang et al. - 2025 - Zero-resource Hallucination Detection for Text Generation via Graph-based Contextual Knowledge Tripl.pdf


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path:
  storage:Yamada and Arase - 2025 - Light-Weight Hallucination Detection using Contrastive Learning for Conditional Text Generation.pdf


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path:
  storage:Min et al. - 2023 - FActScore Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation.pdf


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  storage:Lee et al. - 2025 - HuDEx Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM r.pdf


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path:
  storage:Warner et al. - 2024 - Smarter, Better, Faster, Longer A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long.pdf


contentType: application/pdf
path:
  storage:Mukherjee et al. - 2025 - Uncertainty-Informed Screening for Safer Solvents Used in the Synthesis of Perovskites via Language.pdf


contentType: application/pdf
path:
  storage:Anh-Hoang et al. - 2025 - Survey and analysis of hallucinations in large language models attribution to prompting strategies.pdf


contentType: application/pdf
path:
  storage:Li et al. - 2023 - HaluEval A Large-Scale Hallucination Evaluation Benchmark for Large Language Models.pdf


contentType: application/pdf
path:
  storage:Niu et al. - 2024 - RAGTruth A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models.pdf


contentType: application/pdf
path:
  storage:Rahman et al. - 2024 - DefAn Definitive Answer Dataset for LLMs Hallucination Evaluation.pdf


contentType: application/pdf
path:
  storage:Kovács and Recski - 2025 - LettuceDetect A Hallucination Detection Framework for RAG Applications.pdf


contentType: application/pdf
path:
  storage:Agarwal et al. - 2025 - CodeMirage Hallucinations in Code Generated by Large Language Models.pdf


contentType: application/pdf
path:
  storage:Lee et al. - 2025 - Hallucination by Code Generation LLMs Taxonomy, Benchmarks, Mitigation, and Challenges.pdf


contentType: application/pdf
path:
  storage:Becker et al. - 2025 - Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity.pdf


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  After analyzing all the papers, we found that content-based assessment is a qualitative assessment of essays.


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  All evaluating methods are not evaluated based on coherence, relevance, completeness, feedback, and knowledge-based.


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  Among the most telling critiques are reports of intentionally gibberish essays being given high scores.[32]

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  Knowledge-based Inference Model (KIM), which enriches ESIM with external knowledge, obtains an accuracy of 88.6%

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comment: Mögliches Dataset
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  To address these complementary limitations, we propose an ensemble approach that combines the deterministic precision of BERT/ ELMo with the contextual depth of GPT.

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  (1) incorrect or insufficient understanding of task requirements, (2)  lack of factual knowledge relevant to the generation tasks, and (3) inability to access the necessary code and non-code  resources from the repository.

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  We investigate the causes of hallucinations by CodeLLMs and classify them into three main issues: Training Data Issues,  Trained Model Issues, and Prompt Issues.


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  Hallucination Rate (HR), Validate Rate (VR) and Accuracy of Hallucination Existence Recognition (ACCrec )


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