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MarkrAI

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AutoRAG-evaluation-2024-LLM-paper-v1

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## - False Positive Rate (Fpr) FPR measures the proportion of false positives out of the total actual negatives, indicating how often false alarms occur. It is the rate at which regular instances are wrongly classified as anomalies. In many applications, it's crucial to minimize FPR to avoid the costs associated with false alarms, such as wasted resources or unnecessary anxiety. $$\mathrm{FPR}={\frac{F P}{F P+T N}}.$$

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## - False Negative Rate (Fnr) FNR quantifies the proportion of false negatives out of the total actual positives, indicating the model's miss rate. It quantifies the model's failure to detect anomalies. A high FNR indicates that many anomalies go undetected, potentially leading to missed opportunities for intervention in critical situations. $$\mathrm{FNR}={\frac{F N}{T P+F N}}.$$

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## - F1 Score The F1 Score is the harmonic mean of precision and recall, providing a single score that balances the trade-off between precision and recall. It is a useful metric for evaluating the overall performance of a model, especially when there is an uneven class distribution (e.g., a large number of normal instances and a small number of anomalies). It is particularly valuable in imbalanced datasets, where TPs are much less common than TNs. Therefore, F1 Score is particularly useful in anomaly detection because it balances the trade-off between minimizing false alarms (FP) and minimizing missed detections (FN). $$\mathrm{F}_{1}=2\cdot{\frac{\mathrm{Precision}\cdot\mathrm{Recall}}{\mathrm{Precision}+\mathrm{Recall}}}.$$ This metric was a key component in the experimental design of [10, 49, 23, 51, 53, 54, 24, 56, 57, 5, 58, 59, 60, 62, 63, 64, 65, 66].

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## - Area Under The Receiver Operating Characteristic (Auroc) AUROC represents the likelihood of the model distinguishing between the positive class (anomalies) and the negative class (normal cases) [189]. It reflects the model's ability to classify outcomes correctly at various threshold levels, providing a comprehensive measure of performance across all possible classification thresholds. AUROC stands out as a comprehensive measure that evaluates a model's ability to distinguish between classes across all thresholds. The ROC curve plots the TPR against the FPR at various threshold settings. AUROC represents the probability that a model will rank a randomly chosen positive instance higher than a randomly chosen negative one. A model with an AUROC of 1.0 is perfect, distinguishing between all positive and negative instances correctly, while a score of 0.5 suggests no discriminative ability, equivalent to random guessing. $$\mathrm{AUROC}=\int_{0}^{1}{\frac{T P}{T P+F P}}\;\mathrm{d}{\frac{F P}{F P+T N}}.$$ AUROC is particularly informative in anomaly detection because it provides insight into the model's performance across a range of conditions, allowing for the evaluation of the model's generalizability and robustness. It helps identify the best model that manages the trade-off between detecting as many anomalies as possible (high TPR) while keeping false alarms (high FPR) to a minimum. This is crucial in real-world applications where the cost of false positives and false negatives can vary significantly, and choosing an operating point (a specific threshold) that balances these costs is essential. As delineated in [49], the metric was critical to their evaluative strategy.

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## 7 Forecasting With Large Language Models In the domain of artificial intelligence, LLMs have emerged as pivotal instruments for advancing forecasting methodologies across a myriad of fields. This section delves into the transformative role these models play in predicting future events and trends with unprecedented accuracy. This section is meticulously structured to cover the versatile applications of LLMs in forecasting, starting with time series forecasting, a fundamental approach that is further delineated into short-term and long-term forecasting. Each of these subcategories showcases the specific challenges and solutions that LLMs address, highlighting their flexibility and efficiency. Moving beyond traditional time series analysis, the discussion extends to traffic flow forecasting, illustrating how LLMs enhance urban mobility and reduce congestion through predictive analytics. Furthermore, the section explores the profound impact of LLMs in healthcare clinical prediction, where they offer groundbreaking insights into patient outcomes, disease progression, and treatment efficacy. Through this comprehensive examination, we aim to underscore the significant advancements LLMs bring to forecasting practices, fostering a deeper understanding of their capabilities and potential for innovation in various sectors.

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## 7.1 Time Series Forecasting This section embarks on an in-depth exploration of how LLMs have revolutionized the analysis and prediction of sequential data over time. This critical area of forecasting serves as the backbone for numerous applications, ranging from financial market predictions to energy consumption planning. By dividing the discussion into short-term and longterm forecasting, this section meticulously addresses the nuances and specificities of forecasting at different horizons. Short-term forecasting focuses on the immediate future, where precision and speed are paramount, highlighting LLMs' ability to process and analyze data for near-term predictions rapidly. Conversely, long-term forecasting examines trends and patterns over extended periods, demonstrating how LLMs can identify underlying signals amidst noise, providing valuable foresight for strategic planning and decision-making. Gruver *et al.* (2023) [45] introduces an innovative approach for forecasting time series by utilizing LLMs like GPT-3 and LLaMA-2. This method involves encoding time series data as strings of numerical digits, thereby converting the forecasting challenge into predicting the next token in a sequence akin to text prediction. This strategy enables LLMs to extrapolate future values in time series data without any task-specific prior training. The effectiveness of this approach is noted to be on par with or superior to traditional time series models explicitly designed for such tasks. The authors emphasize the utility of LLMs in capturing the nuanced dynamics of time series forecasting due to their capability to encode multimodal distributions, which is advantageous for representing the inherent variability and repeated patterns found in many time series datasets. This attribute, combined with LLMs' inclination towards simplicity and pattern repetition, is critical to their success in time series analysis. One of the major advantages highlighted by the authors is the zero-shot nature of their approach, which prevents the need for detailed knowledge of model fine-tuning or the extensive computational resources typically required. This aspect is particularly beneficial when data is scarce, thus eliminating the necessity for extensive model training or fine-tuning. The broad generalization capacity of LLMs, thanks to their extensive pre-training, allows for effective pattern recognition and extrapolation without the need for domain-specific model development. Moreover, the methodology described facilitates handling missing data through non-numerical text, integrating textual information alongside numerical time series data, and explaining predictions by answering questions. This comprehensive capability demonstrates the versatility of LLMs in dealing with complex forecasting tasks. However, the authors also caution that larger LLMs, such as GPT-4, may not always yield improved performance

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## 7.1 Time Series Forecasting beneficial when data is scarce, thus eliminating the necessity for extensive model training or fine-tuning. The broad generalization capacity of LLMs, thanks to their extensive pre-training, allows for effective pattern recognition and extrapolation without the need for domain-specific model development. Moreover, the methodology described facilitates handling missing data through non-numerical text, integrating textual information alongside numerical time series data, and explaining predictions by answering questions. This comprehensive capability demonstrates the versatility of LLMs in dealing with complex forecasting tasks. However, the authors also caution that larger LLMs, such as GPT-4, may not always yield improved performance over smaller counterparts like GPT-3. This is attributed to differences in number tokenization and a lack of reliable uncertainty calibration, potentially due to modifications in model training procedures like RLHF. This groundbreaking work underscores the potential of leveraging LLMs for time series forecasting, showcasing their adaptability across diverse domains and ability to simplify the forecasting process without compromising accuracy or requiring extensive domain expertise. Zhou *et al.* (2023) [46] demonstrates the effectiveness of using pre-trained language and computer vision models for various time series analysis tasks without modifying their architecture. It's very challenging to efficiently utilize pre-trained models from other domains, like natural language processing and computer vision, for diverse time series analysis tasks, aiming to overcome the need for domain-specific architectural changes and to harness the power of these pre-trained models for improved performance in time series analysis. This work employs a novel architecture for time series analysis, using parameters from pre-trained NLP transformer models. Specifically, the study focuses on the GPT2 model and experiments with other models like BERT and BEiT. This approach represents a significant shift from traditional methods, as it leverages the strengths of pre-trained models from different domains (like language and vision) for time series analysis, thus exploring the universality and versatility of these models in a new context. The zero-shot performance of the proposed approach still lags behind the state-of-the-art methods. This suggests that while the method is effective in many scenarios, it may not yet be fully optimized for zero-shot learning tasks where the model makes predictions without any prior examples from the specific task domain. This work proposed a unified framework that uses a frozen pre-trained language model to achieve state-of-the-art or comparable performance in all major types of time series analysis tasks. This includes time series classification, short/long-term forecasting, imputation,

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## 7.1 Time Series Forecasting universality and versatility of these models in a new context. The zero-shot performance of the proposed approach still lags behind the state-of-the-art methods. This suggests that while the method is effective in many scenarios, it may not yet be fully optimized for zero-shot learning tasks where the model makes predictions without any prior examples from the specific task domain. This work proposed a unified framework that uses a frozen pre-trained language model to achieve state-of-the-art or comparable performance in all major types of time series analysis tasks. This includes time series classification, short/long-term forecasting, imputation, anomaly detection, and few-shot and zero-shot forecasting, supported by thorough and extensive experiments. Theoretical and empirical findings show that self-attention in transformers performs a function similar to Principal Component Analysis (PCA), helping to explain the universality of transformer models. The authors demonstrated the universality of their approach by successfully applying a pre-trained transformer model from another backbone model (like BERT) or modality (such as computer vision) to power time series forecasting. Shi *et al.* (2023) [47] investigates whether LLMs can reason about real-world events and improve event prediction. The motivation behind this objective is the potential usefulness of LLMs in handling event sequences that are often accompanied by rich text information. Large language models have shown impressive performance on various reasoning tasks, and the authors aim to explore their capabilities in reasoning about real-world events. Event sequences are often accompanied by text information, and LLMs excel at handling textual data. Therefore, integrating LLMs into event prediction models can potentially improve their performance. The authors propose a framework called LAMP incorporating a large language model in event prediction. They use abductive reasoning to suggest possible causes for event predictions and retrieve relevant events from history to support these predictions. One potential threat of this paper is the reliance on large language models, which may have limitations in terms of data leakage and generalization. However, the authors address these concerns by verifying the absence of data leakage and demonstrating the generalization capabilities of LLMs in their experiments. Another threat is the limited evaluation of specific datasets, which may not fully represent the complexity of real-world event prediction tasks. However, the authors mitigate this threat by conducting experiments on multiple datasets and demonstrating consistent improvements over baseline models. The proposed LAMP framework is innovative and practical, as it integrates a large language model into event prediction models, leveraging the reasoning capabilities of

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## 7.1 Time Series Forecasting . One potential threat of this paper is the reliance on large language models, which may have limitations in terms of data leakage and generalization. However, the authors address these concerns by verifying the absence of data leakage and demonstrating the generalization capabilities of LLMs in their experiments. Another threat is the limited evaluation of specific datasets, which may not fully represent the complexity of real-world event prediction tasks. However, the authors mitigate this threat by conducting experiments on multiple datasets and demonstrating consistent improvements over baseline models. The proposed LAMP framework is innovative and practical, as it integrates a large language model into event prediction models, leveraging the reasoning capabilities of LLMs. The framework provides insightful empirical findings through extensive experiments on challenging real-world datasets, demonstrating its significant improvement over state-of-the-art event sequence models. This work presents a well-structured review of relevant literature, discussing the existing event sequence models and their limitations, as well as the potential of LLMs in event prediction. It addresses the threats of data leakage by verifying that the LLMs used in the experiments were trained on data that does not include the datasets used in the experiments. Cao *et al.* (2024) [9] proposed TEMPO, which aims to leverage the strengths of transformer-based models for their ability and attention mechanism to handle sequential data, learn from context, and apply it to time-series forecasting tasks. It designed and evaluated an approach for time series forecasting using a method adapted from GPTs. Because of the success of GPTs in NLP, TEMPO hypothesizes that the same architecture can be adapted to understand and predict time series data, which is inherently sequential. It leverages the power of pre-training models and finds self-attention mechanisms to be good at capturing dependencies in sequential data. Inspired by the prompt-based GPTs, like ChatGPT, TEMPO uses historical data points as prompts, like a conversation. By leveraging the power of pre-training, TEMPO can generalize across different time series domains and tasks. TEMPO beats traditional methods in accuracy and other benchmarks in many datasets. However, it has computational cost and dataset requirements of quality and quantity concerns. Xue *et al.* (2023) [48] presented PromptCast, which aims to establish a new paradigm that transforms the traditional numerical time series forecasting task into a prompt-based task. This approach is motivated by the successes of pre-trained language foundation models in NLP. One of

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## 7.1 Time Series Forecasting uses historical data points as prompts, like a conversation. By leveraging the power of pre-training, TEMPO can generalize across different time series domains and tasks. TEMPO beats traditional methods in accuracy and other benchmarks in many datasets. However, it has computational cost and dataset requirements of quality and quantity concerns. Xue *et al.* (2023) [48] presented PromptCast, which aims to establish a new paradigm that transforms the traditional numerical time series forecasting task into a prompt-based task. This approach is motivated by the successes of pre-trained language foundation models in NLP. One of the primary challenges is the effective translation of numerical time series data into textual prompts that language models can process. This approach needs more benchmarks for evaluating the prompt-based methods and further evaluation under real-world scenarios, like financial market crashes. Zarzà *et al.* (2023) [190] studies the efficacy of modern deep learning methods for forecasting traffic accidents and enhancing Level-4 and Level-5 autonomous driving assistants with actionable visual and language cues. The motivation is to improve city planning and public safety by predicting accidents using a rich dataset of accident occurrences, thus paving the way for safer and smarter cities driven by data-driven decision-making. The authors identify the growing problem of traffic congestion and accidents in urban centers and the need for predictive analytics to mitigate these issues. This work acknowledges that traditional statistical models may only partially capture the complex interplay of factors leading to traffic accidents. The authors propose the use of advanced deep learning methods, such as Transformers, in conjunction with traditional time series models like ARIMA and Prophet for improved accident forecasting. They introduce the novel idea of employing LLMs and Visual Language Models (VLMs) to provide real-time interventions in autonomous driving. The rationale includes an in-depth analysis of feature importance using principal component analysis to identify key factors contributing to accidents. The paper also explores the concept of multimodality by utilizing a visual language model (LLaVA) to bridge visual and linguistic cues for enhancing autonomous driving systems. However, this work may face challenges in demonstrating the real-world applicability and scalability of the proposed methods, especially in diverse urban environments. There may be concerns regarding the interpretability and transparency of the deep learning models, which are often considered "black boxes." The reliance on a specific dataset for analysis could limit the generalizability of the findings to other regions or conditions not represented in the data. The integration of LLMs

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## 7.1 Time Series Forecasting analysis to identify key factors contributing to accidents. The paper also explores the concept of multimodality by utilizing a visual language model (LLaVA) to bridge visual and linguistic cues for enhancing autonomous driving systems. However, this work may face challenges in demonstrating the real-world applicability and scalability of the proposed methods, especially in diverse urban environments. There may be concerns regarding the interpretability and transparency of the deep learning models, which are often considered "black boxes." The reliance on a specific dataset for analysis could limit the generalizability of the findings to other regions or conditions not represented in the data. The integration of LLMs and VLMs into autonomous driving systems might raise questions about the safety and reliability of language-based interventions in real-time traffic situations. The paper's proposed methods may need to address the computational complexity and resource requirements associated with processing large multimodal datasets in real time. The work presents an innovative methodology that combines modern deep learning techniques with traditional time series models for traffic accident forecasting. It contributes to the field by introducing the use of compact LLMs, such as LLaMA-2 and Zephyr-7b-a, for real-time interventions in autonomous driving. The study provides empirical findings on feature importance using PCA loadings, which can inform the development of more effective predictive models. It offers a well-structured review of the relevant literature, situating the current work within the broader context of traffic safety and autonomous driving research. The introduction of LLaVA as a multimodal model that integrates visual and linguistic cues is a notable contribution, potentially enhancing the responsiveness of autonomous driving systems. The paper has practical implications for city planners, traffic management agencies, and emergency services by providing actionable insights for optimizing resource allocation and intervention strategies. Xue *et al.* (2022) [50] proposes a novel pipeline named AuxMobLCast that leverages language foundation models to discover temporal sequential patterns in human mobility forecasting tasks. In the new pre-train and fine-tune paradigm, a foundation model is pre-trained with large-scale data and then adapted to solve various downstream tasks. However, this shift only appears in the NLP and CV fields. How to apply a foundation model for spatio-temporal forecasting and human mobility prediction still needs to be explored. In the time-series data forecasting domain, especially with the human mobility data, there has yet to be any existing work on directly using pre-trained language foundation models for human mobility prediction due

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## 7.1 Time Series Forecasting LCast that leverages language foundation models to discover temporal sequential patterns in human mobility forecasting tasks. In the new pre-train and fine-tune paradigm, a foundation model is pre-trained with large-scale data and then adapted to solve various downstream tasks. However, this shift only appears in the NLP and CV fields. How to apply a foundation model for spatio-temporal forecasting and human mobility prediction still needs to be explored. In the time-series data forecasting domain, especially with the human mobility data, there has yet to be any existing work on directly using pre-trained language foundation models for human mobility prediction due to the sequential numerical data format. The authors denote a set of POIs, and each POI contains a history record of customer visits on N continuous days. The author formulated the human mobility forecasting problem as predicting the number of visits the next day, given the historical observation. Then, three types of mobility prompting are introduced to convert the sequential observation into language description to leverage pre-trained language models for forecasting human mobility. Finally, the paper proposes a novel pipeline, AuxMobLCast, based on the general encoder-decoder framework with an auxiliary classification task to classify the POI category. One limitation of this study is about the mobility prompt generation. In the future, they plan to thoroughly investigate mobility prompts based on the recent prompt learning techniques. An automatic approach for transforming diverse sequential numerical behavior data and various types of time-series data will be beneficial in exploring the forecasting ability of pre-trained language models. In addition, how to explore pre-trained language models for multi-variate time-series data forecasting could be another interesting future direction. Jin *et al.* (2024) [52] demonstrates that large language models' rich semantic understanding and contextual learning abilities can be effectively adapted for the structurally distinct challenge of time series forecasting. This work seeks to establish methods and techniques for this reprogramming process and to evaluate the efficacy of these adapted models in time series forecasting, potentially offering a new avenue for utilizing existing language models in diverse applications beyond text-based tasks. The motivation of this work is to harness the advanced capabilities of large language models for the task of time series forecasting, thereby expanding their applicability beyond traditional text-based tasks. The rationale of the paper is to demonstrate that the rich semantic understanding and contextual learning abilities of large language models can be effectively adapted for the structurally distinct challenge of time series forecasting. The work's limitation lies in its reliance on the

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## 7.1 Time Series Forecasting This work seeks to establish methods and techniques for this reprogramming process and to evaluate the efficacy of these adapted models in time series forecasting, potentially offering a new avenue for utilizing existing language models in diverse applications beyond text-based tasks. The motivation of this work is to harness the advanced capabilities of large language models for the task of time series forecasting, thereby expanding their applicability beyond traditional text-based tasks. The rationale of the paper is to demonstrate that the rich semantic understanding and contextual learning abilities of large language models can be effectively adapted for the structurally distinct challenge of time series forecasting. The work's limitation lies in its reliance on the inherent capabilities of pre-trained language models, which may not be ideally suited or optimized for the specific nuances and complexities of time series data. The authors use Llama2-7B as the foundation model for evaluating two public benchmarking datasets compared to the baseline models from open-source TSlib. The ETT dataset is used to assess the long-term forecasting capability, while the M4 dataset is employed for short-term forecasting. The proposed method outperforms other baseline models regarding MSA and MAE metrics. Li *et al.* (2022) [53] evaluates the performance of BERT, a prominent language model, in two distinct applications: cloud-edge time series forecasting and sentiment analysis, utilizing prompt learning techniques. Their study aims to assess BERT's effectiveness and limitations in these areas to understand its applicability and potential for improvement in such tasks. This work investigates the capability of BERT in cloud-edge time series forecasting, a task that requires logical reasoning and an understanding of temporal data trends. Given its primary design for language understanding, the challenge is determining how well BERT can perform in this context. The authors aim to provide insights into these challenges and BERT's applicability and limitations in addressing them. This work applies prompt learning with BERT for cloud-edge time series forecasting and sentiment analysis. This approach seeks to leverage BERT's language understanding capabilities by framing the forecasting and sentiment analysis tasks to align with natural language processing. Prompt learning, which involves creating prompts that guide the model to understand and execute specific tasks, is used to adapt BERT, initially designed for language tasks, to these new application areas. The effectiveness of this method is then evaluated to understand how well BERT can handle these challenges. The potential limitations of this paper include a limited scope, as the study may not encompass a wide range of scenarios or datasets, potentially affecting the generalizability of the findings.

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## 7.1 Time Series Forecasting for cloud-edge time series forecasting and sentiment analysis. This approach seeks to leverage BERT's language understanding capabilities by framing the forecasting and sentiment analysis tasks to align with natural language processing. Prompt learning, which involves creating prompts that guide the model to understand and execute specific tasks, is used to adapt BERT, initially designed for language tasks, to these new application areas. The effectiveness of this method is then evaluated to understand how well BERT can handle these challenges. The potential limitations of this paper include a limited scope, as the study may not encompass a wide range of scenarios or datasets, potentially affecting the generalizability of the findings. Additionally, the inherent limitations of BERT, particularly in non-language tasks, and the constraints of the methodological approach, such as prompt learning, might impact the results and their broader applicability. These factors suggest that while the study provides valuable insights, its conclusions might be specific to the contexts and models tested. Sheng (2023) [186] proposes a scheme to train models on multimodal data combined with external knowledge bases, fine-tune GPT-4 on domain-specific data, train models on multimodal data, and equip models with probabilistic reasoning capabilities to achieve analysis and Interpret financial and technical data to generate strategic insights and make future forecasts. The original datasets may pose a threat that financial data is easily manipulated, which could lead to inaccurate predictions. This work proposes a training scheme that combines multimodal data with external knowledge bases and domain-specific data, thereby improving the accuracy and reliability of domain-specific output from large language models. Dong *et al.* (2023) [61] introduces SimMTM, a streamlined pre-training framework for masked time-series modeling, aimed at enhancing the efficacy of time series analysis tasks like forecasting and classification. The framework's core strategy involves learning to reconstruct the original time series by leveraging multiple masked series. This initiative stems from the recognition that the most significant semantic information within time series is encapsulated in temporal variations, which pose annotation challenges due to their inherent complexity. The paper tackles the problem arising from conventional masked modeling techniques, where random masking of time points could obliterate critical temporal variations, complicating the task of reconstruction to the extent that it hampers effective representation learning. The proposed methodology is underpinned by the manifold perspective on masked modeling, positing that while direct reconstruction might be thwarted by the loss of crucial temporal variations, utilizing multiple neighbors (or multiple masked series) for reconstruction can mutually compensate

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## 7.1 Time Series Forecasting series by leveraging multiple masked series. This initiative stems from the recognition that the most significant semantic information within time series is encapsulated in temporal variations, which pose annotation challenges due to their inherent complexity. The paper tackles the problem arising from conventional masked modeling techniques, where random masking of time points could obliterate critical temporal variations, complicating the task of reconstruction to the extent that it hampers effective representation learning. The proposed methodology is underpinned by the manifold perspective on masked modeling, positing that while direct reconstruction might be thwarted by the loss of crucial temporal variations, utilizing multiple neighbors (or multiple masked series) for reconstruction can mutually compensate for this loss. This facilitates a more manageable reconstruction process. Moreover, this technique implicitly conditions the model to discern the local manifold structure of the time series, thereby fostering more robust representation learning.

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## 7.2 Event Sequence Prediction Event sequences and time series data are two fundamental concepts within the realm of data analysis and predictive modeling, each serving unique purposes and offering distinct insights. An event sequence, by its very nature, comprises a series of discrete actions or occurrences, meticulously cataloged based on the sequence in which they transpire. Unlike time series data, which is inherently quantitative and often measured at regular intervals, event sequences emphasize the order and timing of events without necessarily adhering to a uniform time scale [191, 192, 193]. This distinction is crucial as it underpins the divergent analytical approaches and methodologies applied to each data type. While time series analysis focuses on understanding trends, seasonality, and patterns over time, event sequence analysis delves into the intricacies of the relationships and dependencies between individual events. This analysis can uncover complex behavioral patterns and sequences of actions, which are particularly valuable in domains such as user behavior analysis, system logs, and transaction sequences, where the temporal ordering and occurrence of events hold significant analytical weight. LLMs have become instrumental in event sequence prediction, offering diverse capabilities and applications. Two key aspects of LLMs in event prediction are highlighted below, with references to the relevant papers. One significant aspect of LLMs is their ability to revolutionize event prediction through advanced reasoning techniques. Xue *et al.* (2023) [194] delve into the transformative potential of LLMs in advancing event prediction tasks through abductive reasoning in a few-shot setting. This aspect explores the core strengths of LLMs, exemplified by models like GPT-3. LLMs excel in understanding the contextual nuances of events, capturing intricate long-term dependencies, and exhibiting impressive generalization capabilities. Their innate ability to contextualize and reason over diverse data sources empowers event prediction systems to provide more accurate and insightful forecasts. Nakshatr *et al.* (2023) [195] proposed a generalized framework for newsflow clustering that automatically extracts potentially critical news events that attract high media attention by analyzing the temporal trends of news articles. Another critical dimension in event sequence prediction is the utilization of LLMs to handle streaming event sequences. Shi *et al.* (2023) [47] addresses the unique challenges posed by continuous streams of event data and presents innovative solutions enabled by LLMs. In real-world scenarios, event data often arrives in a continuous and dynamic stream, where the distribution of patterns may shift over time. Privacy concerns and memory constraints further complicate the task of continuous monitoring of event sequences. LLMs, with their adaptive

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## 7.2 Event Sequence Prediction generalized framework for newsflow clustering that automatically extracts potentially critical news events that attract high media attention by analyzing the temporal trends of news articles. Another critical dimension in event sequence prediction is the utilization of LLMs to handle streaming event sequences. Shi *et al.* (2023) [47] addresses the unique challenges posed by continuous streams of event data and presents innovative solutions enabled by LLMs. In real-world scenarios, event data often arrives in a continuous and dynamic stream, where the distribution of patterns may shift over time. Privacy concerns and memory constraints further complicate the task of continuous monitoring of event sequences. LLMs, with their adaptive and context-aware nature, offer a promising avenue for addressing these challenges.

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## 7.3 Traffic Flow Forecasting This section delves into the critical application of LLMs in addressing one of the most pressing challenges in urban planning and mobility management. This segment illuminates how LLMs are leveraged to predict traffic conditions, enabling cities and transportation authorities to optimize traffic flow, reduce congestion, and enhance road safety. By harnessing the power of vast datasets, including historical traffic patterns [196], real-time road conditions [197], and socio-economic factors [198], LLMs offer unparalleled accuracy in forecasting traffic volumes and speeds across different times and locations. This predictive capability is pivotal for planning efficient public transportation schedules, designing intelligent traffic management systems, and facilitating emergency response strategies. The discussion in this section underscores the transformative potential of LLMs in shaping the future of urban mobility and transportation infrastructure. Jin *et al.* (2021) [55] proposed TrafficBERT to address the challenge of accurately forecasting traffic flow over long ranges, which is a critical aspect of managing and optimizing traffic systems. Traditional traffic prediction models often struggle with capturing the intricate spatiotemporal dynamics of traffic flow. TrafficBERT, by leveraging the BERT model, aims to overcome these limitations. It is designed to better understand and predict complex traffic patterns, ultimately aiding in more efficient traffic management, reducing congestion, and enhancing road safety. The use of such advanced predictive models reflects the growing need for sophisticated tools in the realm of intelligent transportation systems. The objective of the TrafficBERT is to develop a model that can effectively forecast long-range traffic flow. By using a pre-trained BERT framework, TrafficBERT aims to analyze and predict traffic patterns and flow with high accuracy. This involves understanding and capturing the complex spatiotemporal correlations in traffic data, which is essential for accurate traffic forecasting over extended periods and across various road conditions. The rationale is that a model adept at understanding the nuanced patterns in language data can similarly excel in interpreting traffic patterns, thereby providing more accurate and reliable long-range traffic flow predictions. This approach aims to enhance traffic management and planning, reduce congestion, and improve road safety. The effectiveness of TrafficBERT hinges on the quality and diversity of training data, with potential risks of overfitting and limited adaptability to sudden changes in traffic conditions. Also, its complexity demands substantial computational resources and expertise, and raises privacy concerns regarding the use of traffic data, emphasizing the need for careful management and implementation in traffic systems. The contribution of TrafficBERT lies in its innovative approach to traffic flow forecasting, utilizing the BERT model to analyze and

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## 7.3 Traffic Flow Forecasting excel in interpreting traffic patterns, thereby providing more accurate and reliable long-range traffic flow predictions. This approach aims to enhance traffic management and planning, reduce congestion, and improve road safety. The effectiveness of TrafficBERT hinges on the quality and diversity of training data, with potential risks of overfitting and limited adaptability to sudden changes in traffic conditions. Also, its complexity demands substantial computational resources and expertise, and raises privacy concerns regarding the use of traffic data, emphasizing the need for careful management and implementation in traffic systems. The contribution of TrafficBERT lies in its innovative approach to traffic flow forecasting, utilizing the BERT model to analyze and predict traffic patterns with high accuracy over long ranges. By adapting a model proven in natural language processing to the domain of traffic management, TrafficBERT demonstrates enhanced capability in understanding complex spatial and temporal dependencies in traffic data. This advancement represents a significant step forward in the field of intelligent transportation systems, offering a more sophisticated tool for traffic analysis and management.

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## 7.4 Healthcare Clinical Prediction This section delves into the transformative potential of LLMs within the healthcare sector, highlighting their role in advancing predictive analytics for patient care and clinical outcomes. In this critical exploration, we uncover how LLMs harness vast arrays of clinical data, including electronic health records, medical imaging [199, 200], and genomic information, to forecast disease progression, patient outcomes, and treatment responses with remarkable precision. This section elucidates the complex methodologies LLMs employ to navigate the intricacies of medical data, offering insights into their ability to identify patterns and correlations that elude traditional analytical methods. By integrating these advanced predictive models, healthcare professionals can achieve a more nuanced understanding of patient health, enabling personalized treatment plans, early intervention strategies, and improved resource allocation. Through a detailed examination of LLMs' impact on healthcare clinical prediction, this segment aims to showcase the profound implications for patient care, medical research, and the broader healthcare ecosystem, underscoring the pivotal role of AI-driven innovations in shaping the future of medicine. Jiang *et al.* (2023) [201] proposed an LLM-based system that can integrate in real-time with clinical workflows centered around writing notes and placing electronic orders, presenting the results from developing, evaluating, deploying, and prospectively assessing NYUTron. This approach relies on the fact that all clinically useful data and medical professionals' decision-making. The authors showed unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. This approach leverages recent advances in natural language processing to train a large language model for medical language (NYUTron) and fine-tune it across a wide range of clinical and operational predictive tasks.

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## 8 Anomaly Detection Using Large Language Models The advent of LLMs has significantly broadened the horizons of anomaly detection, offering sophisticated solutions to identify irregularities across diverse datasets and domains. This section embarks on a comprehensive examination of how LLMs are being utilized to pinpoint deviations that could signify errors, fraud, system failures, or cyber threats. This exploration begins with time series anomaly detection, where LLMs analyze sequential data to detect unusual patterns, benefiting industries reliant on continuous monitoring, such as finance, manufacturing, and energy. Moving forward, the discussion transitions to anomaly log analysis, highlighting the capacity of LLMs to sift through vast quantities of log data to identify and classify anomalies, thereby enhancing IT security and operational efficiency. The section on microservice anomaly detection showcases the application of LLMs in the increasingly complex domain of cloud computing and distributed systems, where they play a crucial role in maintaining system health and security by detecting anomalies at the microservice level. This detailed exploration aims to illuminate the cutting-edge methodologies and impactful applications of LLMs in anomaly detection, underscoring their critical role in safeguarding and optimizing modern digital infrastructures.

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## 8.1 Time Series Anomaly Detection This section delves into the intricate world of identifying outliers in sequential data sets, where LLMs have become invaluable tools. This critical facet of anomaly detection focuses on uncovering patterns that deviate from the norm within time-dependent data, a task essential for various sectors, including finance, healthcare, and cybersecurity. Through applying LLMs, this section explores the nuanced approaches to detecting such anomalies, ranging from sudden spikes in financial markets to unexpected patient vital signs, providing early warnings of potential issues. LLMs' ability to process and analyze vast amounts of data with temporal dependencies allows for a more sophisticated and accurate detection of anomalies than traditional statistical methods. This exploration covers the technical methodologies employed by LLMs and discusses their implementation challenges and the solutions developed to overcome them. By highlighting the significance of time series anomaly detection, this section aims to provide insights into the advanced capabilities of LLMs, demonstrating their critical role in predictive analytics and their impact on enhancing decision-making processes across various industries. Dang *et al.* (2020) [23] proposes a BERT based on a natural language processing model to solve the problem of time series anomaly detection; its motivation stems from the similarity between time series anomaly detection and text classification tasks in natural language processing. Simulation results demonstrate that this method only needs a small amount of label data to train the BERT model to obtain better results than the state-of-the-art work. Dang *et al.* (2021) [24] introduces the pattern of pre-training and fine-tuning and proposes to adopt the BERT model in the NLP field to model time series, thus addressing the long-distance dependent modeling issue. The performance on two widely used public datasets demonstrates that the method is more accurate on the KPI and Yahoo datasets than the SOTA solutions. This work uses Spectral Residual (SR) to generate labels for unlabeled data. SR is Fourier transform-based and designed for unsupervised anomaly detection in univariate time series. The proposed approach overperforms the SR and SR variation methods regarding the F1 score. The main threat is that this proposed method heavily relies on the pre-training process, which requires massive amounts of data. Therefore, it may not be suitable for anomaly detection task scenarios without much historical data.

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## 8.2 Anomaly Log Analysis This section is dedicated to exploring the capabilities of LLM in scrutinizing log data, a fundamental aspect of maintaining the integrity and performance of IT systems. Log files, generated by various applications, networks, and systems, are rich sources of data that, when analyzed effectively, can unveil operational anomalies, security breaches, and potential system failures. This section discusses how LLMs are revolutionizing the field of log analysis by applying advanced natural language processing techniques to automatically detect, classify, and respond to anomalies within vast, unstructured datasets. The ability of LLMs to understand and interpret the context of log entries enables a more nuanced and efficient anomaly detection process, significantly reducing the time and resources traditionally required for manual log review. By detailing the methodologies, challenges, and success stories of anomaly log analysis using LLMs, this section aims to highlight the transformative impact of these models on cybersecurity, system diagnostics, and operational efficiency, illustrating their indispensable role in modern digital ecosystems. Chen *et al.* (2022) [10] argues that system logs, which are a primary resource for fault diagnosis and anomaly detection in large-scale computer systems, are challenging to classify due to their unstructured nature. Recent studies have focused on extracting semantic information from these unstructured log messages and converting them into word vectors. However, these methods often overlook the order of words in sequences. To address this, the authors propose BERT-Log, a method that treats the log sequence as a natural language sequence. It uses a pre-trained language model to learn the semantic representation of normal and anomalous logs. A fully connected neural network is then used to fine-tune the BERT model to detect abnormalities. This approach can capture all the semantic information from the log sequence, including context and position. The authors claim that BERT-Log has achieved the highest performance among all the methods on the HDFS dataset, with an F1-score of 99.3%. They also propose a new log feature extractor on the BGL dataset to obtain log sequences by sliding window, including node ID, window size, and step size. BERT-Log detects anomalies on the BGL dataset with an F1-score of 99.4%, representing a 19% performance improvement compared to LogRobust and a 7% performance improvement compared to HitAnomaly. The model also demonstrated strong generalizability, achieving high F1 scores even when trained on only 1% of the dataset. The authors conclude that BERT-Log offers better accuracy and generalization ability than

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## 8.2 Anomaly Log Analysis F1-score of 99.3%. They also propose a new log feature extractor on the BGL dataset to obtain log sequences by sliding window, including node ID, window size, and step size. BERT-Log detects anomalies on the BGL dataset with an F1-score of 99.4%, representing a 19% performance improvement compared to LogRobust and a 7% performance improvement compared to HitAnomaly. The model also demonstrated strong generalizability, achieving high F1 scores even when trained on only 1% of the dataset. The authors conclude that BERT-Log offers better accuracy and generalization ability than previous anomaly detection approaches. They also highlight that their work is the first to utilize node ID and time to form log sequences. Lee *et al.* (2023) [49] presents a novel approach to log-based anomaly detection using a LLM called LanoBERT. The authors argue that existing log-based anomaly detection methods are limited by their inability to capture the complex relationships between log messages and the context in which they occur. LanoBERT addresses this limitation by leveraging the pre-trained BERT model to learn the contextual representations of log messages and their relationships. The authors propose a log sequence representation method that captures the temporal and contextual information of log messages. They also introduce a anomaly detection algorithm that utilizes the learned representations to detect anomalies in log sequences. The authors evaluate LanoBERT on three real-world log datasets. The results show that LanoBERT outperforms existing methods in terms of anomaly detection F1 score and AUROC. The authors conclude that LanoBERT is an effective and efficient approach to log-based anomaly detection and has the potential to improve the reliability and security of large-scale computer systems. However, LAnoBERT requires individual training for each log dataset. Ott *et al.* (2021) [51] proposes a framework for anomaly detection in log data, aiming to utilize pre-trained generalpurpose language models to preserve the semantics of log messages and map them into log vector embeddings. The motivation behind this work is the need for timely and accurate anomaly detection for the reliability, security, safe operation, and mitigation of losses in large computer systems. The challenges include addressing the software evolution due to software upgrades and solving the cold-start problem, where data from the system of interest is unavailable. The rationale for using pre-trained language models is that these representations for the logs are robust and less invariant to log

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## 8.2 Anomaly Log Analysis 2021) [51] proposes a framework for anomaly detection in log data, aiming to utilize pre-trained generalpurpose language models to preserve the semantics of log messages and map them into log vector embeddings. The motivation behind this work is the need for timely and accurate anomaly detection for the reliability, security, safe operation, and mitigation of losses in large computer systems. The challenges include addressing the software evolution due to software upgrades and solving the cold-start problem, where data from the system of interest is unavailable. The rationale for using pre-trained language models is that these representations for the logs are robust and less invariant to log changes, resulting in better generalization of the anomaly detection models. The authors believe that improvements in log vectorization can translate to improvements in the robustness and generalization of the anomaly detection models. The authors present a general framework for learning context and semantic-aware numerical log vector representations suitable for anomaly detection, a comparison of three semantic-level general-purpose language embedding models for anomaly detection, a comparison of two learning objectives for anomaly detection utilizing general language models, a robust model transfer approach for reduction of the false positive rate after software update, and a publicly available implementation of the method and the datasets. Zhang *et al.* (2023) [54] proposes a log anomaly detection framework named LogPrompt based on prompt tuning constructs to guide the PLM in learning semantic and sequential information in the logs, improving the evaluation metrics of log anomaly detection tasks. The motivation behind this framework is that traditional log data analysis and anomaly detection are performed manually. Semantic and sequential tokens are comprehensively considered and embedded to help PLM detect point and conditional anomalies effectively and efficiently. Focal loss is used to replace cross-entropy loss, which alleviates the class imbalance of real-world log data. However, methods based on deep learning have significantly progressed, and learning from the labeled log is costly and impractical. Huang *et al.* (2023) [56] proposes a pre-trained log representation model with hierarchical bidirectional encoder transformers named HilBERT. This method parses logs into templates before using the log templates to pre-train HilBERT and design a hierarchical transformer model to capture log template sequence-level information. This work first introduces the design and architecture of the model, which discovers global information and preserves local information. The authors then describe the process of HilBERT pre-training and apply WordPiece tokenization to slice log lines into log sequences. Finally

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## 8.2 Anomaly Log Analysis learning from the labeled log is costly and impractical. Huang *et al.* (2023) [56] proposes a pre-trained log representation model with hierarchical bidirectional encoder transformers named HilBERT. This method parses logs into templates before using the log templates to pre-train HilBERT and design a hierarchical transformer model to capture log template sequence-level information. This work first introduces the design and architecture of the model, which discovers global information and preserves local information. The authors then describe the process of HilBERT pre-training and apply WordPiece tokenization to slice log lines into log sequences. Finally, to utilize HilBERT for anomaly detection tasks, they fine-tune the model with corresponding training data and use the log sequence representation to predict the abnormality of a sequence. Le *et al.* (2023) [202] designs appropriate prompts to guide ChatGPT in understanding the log parsing task and extracting the log event/template from the input log messages. In addition, this paper evaluates the effectiveness and the performance of ChatGPT-based log parsing in different scenarios, such as using few-shot scenarios and different prompting methods. The authors designed appropriate prompts to guide ChatGPT in understanding the log parsing task and compared its performance with log parsers in zero-shot and few-shot scenarios. Gupta *et al.* (2023) [57] introduces BERTOps, an LLM for AI in the operations domain, pre-trained over large-scale public and proprietary log data. The architectural design of BERTOPS is motivated by BERT-BASE. The transformer encoder of BERTOps is further trained on log data using the masked language modeling task. After the pretraining of the BERTOps model is complete, it is fine-tuned using a cross-entropy-loss classification for each task. Shao *et al.* (2023) [58] proposes a Prog-BERT-LSTM model to detect system faults from log text data, improving abnormal logs' detection performance and generalization ability. This approach extracts the log template and then uses the BERT model of the progressive masking strategy to generate the log vectorization representation and combines Mogrifier LSTM with log vector learning sequence features to avoid the loss of sequence features caused by the disappearance of gradient in the calculation process. In the end, Softmax logical regression outputs the predicted abnormal log. This work designs and implements a neural network model combined

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## 8.2 Anomaly Log Analysis . Shao *et al.* (2023) [58] proposes a Prog-BERT-LSTM model to detect system faults from log text data, improving abnormal logs' detection performance and generalization ability. This approach extracts the log template and then uses the BERT model of the progressive masking strategy to generate the log vectorization representation and combines Mogrifier LSTM with log vector learning sequence features to avoid the loss of sequence features caused by the disappearance of gradient in the calculation process. In the end, Softmax logical regression outputs the predicted abnormal log. This work designs and implements a neural network model combined with dynamic mask ratio and Mogrifier LSTM, which detects log anomalies based on semantic understanding and long-term dependence of sequences. This model uses Magnifier LSTM as the cycle unit, which has the advantages of solid sequence expression ability and simple parameters, and the accuracy is further improved. This paper further enhances the BERT model in log anomaly detection in the Prog-BERT-LSTM model. It introduces the BERT model of progressive masking strategy to vectorize the log sequence to improve the model's training speed and semantic understanding ability. He *et al.* (2023) [59] proposes a new approach for log anomaly detection that efficiently captures semantic information among logs while minimizing training overhead. This approach, named LogBP-LoRA, integrates a pre-training model (BERT) with Low-Rank Adaptation (LoRA) to enhance the detection of anomalies in log data. The goal is to overcome the limitations of traditional BERT models in handling log data and to provide a more resource-efficient solution for anomaly detection in this context. The proposed LogBP-LoRA method aims to solve these challenges by integrating a pre-training model with Low-Rank Adaptation, thereby reducing training overhead while enhancing the model's capability to extract meaningful semantic information from log data. This paper uses a novel LogBP-LoRA approach, which combines a pre-trained BERT model with LoRA specifically for log anomaly detection. The critical innovation is integrating a bypass connection in the self-attention layer of BERT, which allows for efficient training and better semantic information extraction from log data. This method addresses the high computational requirements of traditional BERT models and improves anomaly detection accuracy by effectively capturing the relationships and patterns within log sequences. The approach is validated through extensive experiments on public log datasets, demonstrating its effectiveness and efficiency. However, the proposed method is

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## 8.2 Anomaly Log Analysis the model's capability to extract meaningful semantic information from log data. This paper uses a novel LogBP-LoRA approach, which combines a pre-trained BERT model with LoRA specifically for log anomaly detection. The critical innovation is integrating a bypass connection in the self-attention layer of BERT, which allows for efficient training and better semantic information extraction from log data. This method addresses the high computational requirements of traditional BERT models and improves anomaly detection accuracy by effectively capturing the relationships and patterns within log sequences. The approach is validated through extensive experiments on public log datasets, demonstrating its effectiveness and efficiency. However, the proposed method is limited to log data and the complexity of implementing the LogBP-LoRA model in diverse real-world scenarios. Since the model is tailored for log anomaly detection, its applicability might be limited to similar datasets. It may not generalize well to other types of anomaly detection tasks. Additionally, while it addresses the computational efficiency of traditional BERT models, implementing and tuning the proposed method may still present challenges in practical applications, especially in environments with varying log formats and characteristics. Bobur *et al.* (2020) [203] proposed two new methods to detect outliers in a collection of judicial acts and found the second one more fit for the recommender system. The first method for searching for anomalies combines two models: classification and similarity algorithms. The second method shows the usage of the BERT embedding model and the annoy indexing model. This work mentioned that existing model improvements associated with the accuracy and speed of execution for one request, try to use other pre-trained BERT models (token-based sentences), try to change the size of BERT embedding vector, try to use BERT and other similarity distance algorithms. The authors solved the problem of finding anomalies in judicial acts. By comparing different methods, this paper recognized the BERT embedding model is better for their recommender system. Karlsen *et al.* (2023) [60] applies two feature extraction techniques (syntactic and semantic) in NLP, with no a priori information on the data's log formats. The semantic method (LLM-based Feature Extraction) focuses on extracting meaning from contextual relationships within the log. In contrast, the syntactic approach (TF-IDF-based Feature Extraction) identifies keywords through their frequency in the log file's syntax representation. Semantic LLM comes out on top among the two feature extraction techniques. Semantic extraction yields better results in two

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## 8.2 Anomaly Log Analysis model is better for their recommender system. Karlsen *et al.* (2023) [60] applies two feature extraction techniques (syntactic and semantic) in NLP, with no a priori information on the data's log formats. The semantic method (LLM-based Feature Extraction) focuses on extracting meaning from contextual relationships within the log. In contrast, the syntactic approach (TF-IDF-based Feature Extraction) identifies keywords through their frequency in the log file's syntax representation. Semantic LLM comes out on top among the two feature extraction techniques. Semantic extraction yields better results in two out of three datasets and matching performance in the third, albeit at higher computational costs. Future research will aim to reduce this computational cost and refine the sentence embedding representations of log files by exploring and adapting diverse large language models. The lower performances achieved while using the syntactic approach are likely due to the anonymized nature of the dataset, making it more challenging to differentiate abnormal behavior from normal behavior. Hu *et al.* (2023) [62] explores the intricate challenges of detecting log anomalies in computer systems, an essential task for pinpointing abnormal events or potential issues that may compromise system stability and reliability. This research is propelled by the burgeoning complexity and rapid expansion of software systems, leading to a substantial increase in log data. This surge in data volume has rendered traditional anomaly detection methods inadequate for timely anomaly identification, underscoring the urgency for more refined and universally applicable log anomaly detection techniques. Traditional methods, often reliant on rule-based or statistical approaches, necessitate extensive human oversight, rendering them both time-intensive and less efficacious amidst the deluge of log data from contemporary software systems. Addressing these limitations, the study introduces a pioneering approach, LogADSBERT, which capitalizes on the Sentence-BERT model to distill semantic behavioral attributes from log events, and employs a bidirectional recurrent neural network, specifically Bi-LSTM, for anomaly detection. The paper, however, acknowledges potential validity threats, such as the dependency on the Sentence-BERT model's ability to accurately capture semantic nuances, and the broad applicability of the proposed method across various log data types and software systems. Despite these considerations, experimental outcomes affirm LogADSBERT's superior accuracy over existing log anomaly detection techniques and its resilience in handling novel log event scenarios. Almodovar *et al.* (2024) [63] presents LogFiT, an innovative

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## 8.2 Anomaly Log Analysis from log events, and employs a bidirectional recurrent neural network, specifically Bi-LSTM, for anomaly detection. The paper, however, acknowledges potential validity threats, such as the dependency on the Sentence-BERT model's ability to accurately capture semantic nuances, and the broad applicability of the proposed method across various log data types and software systems. Despite these considerations, experimental outcomes affirm LogADSBERT's superior accuracy over existing log anomaly detection techniques and its resilience in handling novel log event scenarios. Almodovar *et al.* (2024) [63] presents LogFiT, an innovative log anomaly detection model that transcends the limitations of traditional methods by eschewing the dependence on log templates and the necessity for labeled data in supervised training. This research is propelled by the critical need for efficient anomaly detection within system logs to uphold the security and reliability of computing systems. Conventional techniques are hampered either by their inability to assimilate semantic information, owing to a reliance on log templates, or by the requirement for extensive labeled datasets for supervised learning, which is often unfeasible. LogFiT is grounded in the concept of utilizing the linguistic insights embedded in a pretrained BERT-based language model, refining it to discern the linguistic patterns characteristic of normal system logs. This methodology enables LogFiT to accommodate the diverse nature of log content and proficiently identify anomalies. By employing a self-supervised learning paradigm, leveraging a pretrained BERT- based framework fine-tuned on patterns of normal log data, LogFiT facilitates effective anomaly detection without necessitating labeled datasets. Zhang *et al.* (2022) [64] proposes a novel representation technique for log semantics employing Sentence-BERT, aimed at enhancing the accuracy of anomaly detection by considering both semantic and word order relationships. This method exhibits consistent performance even with a limited number of labeled normal logs and surpasses previous techniques on the HDFS dataset. The approach seeks to address the challenge of accurately capturing log semantics, a task where existing word embedding methods fall short, by offering a more effective anomaly detection solution in software system logs. The initiative to develop this method stems from the limitations of traditional methods that rely on word embedding and aggregate weighting, which often overlook the semantic relationship dictated by word order and neglect word interactions. The underlying hypothesis is that a more advanced semantic extraction technique will enable a superior understanding and representation of log events. By leveraging Sentence-BERT for semantic representation extraction, the authors aim to maintain the crucial

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## 8.2 Anomaly Log Analysis and surpasses previous techniques on the HDFS dataset. The approach seeks to address the challenge of accurately capturing log semantics, a task where existing word embedding methods fall short, by offering a more effective anomaly detection solution in software system logs. The initiative to develop this method stems from the limitations of traditional methods that rely on word embedding and aggregate weighting, which often overlook the semantic relationship dictated by word order and neglect word interactions. The underlying hypothesis is that a more advanced semantic extraction technique will enable a superior understanding and representation of log events. By leveraging Sentence-BERT for semantic representation extraction, the authors aim to maintain the crucial semantic and word order relationships, essential for contextual comprehension of log sequences. Le *et al.* (2021) [65] introduces NeuralLog, an innovative technique for anomaly detection in software systems using log analysis. This approach bypasses the conventional requirement for log parsing, aiming to directly derive semantic insights from unprocessed log messages for anomaly detection purposes. The motivation behind NeuralLog stems from the observation that errors in log parsing can significantly undermine anomaly detection performance. By circumventing log parsing, NeuralLog intends to preserve the integrity of log data, thereby enhancing the accuracy of anomaly detection. The challenge that NeuralLog addresses is twofold: first, to effectively interpret the semantic content of raw log messages without the preprocessing step of parsing, and second, to accurately identify anomalies within the vast and complex data environment of software system logs. This task is complicated by the diverse formats and unstructured nature of log messages, which traditionally necessitated parsing to standardize the data for analysis. The rationale for the development of NeuralLog is supported by an empirical study demonstrating the negative impact of log parsing errors on anomaly detection efficacy. By eliminating the log parsing stage, NeuralLog seeks to avoid the potential loss of critical information due to parsing inaccuracies. The approach employs a Transformer-based classification model, capitalizing on its ability to understand contextual relationships in log sequences, which is crucial for identifying anomalies within the logs effectively. The contribution of this work lies in its novel methodology for log-based anomaly detection, which could potentially set a new standard in the field by offering a more reliable and efficient means of identifying system anomalies. This method promises to reduce the time and resources required for anomaly detection by simplifying the preprocessing steps and improving the detection accuracy. However, the study also acknowledges potential threats to its validity, such as the generalizability of the approach across different types of software systems and the effectiveness of the Transformer model in

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## 8.2 Anomaly Log Analysis model, capitalizing on its ability to understand contextual relationships in log sequences, which is crucial for identifying anomalies within the logs effectively. The contribution of this work lies in its novel methodology for log-based anomaly detection, which could potentially set a new standard in the field by offering a more reliable and efficient means of identifying system anomalies. This method promises to reduce the time and resources required for anomaly detection by simplifying the preprocessing steps and improving the detection accuracy. However, the study also acknowledges potential threats to its validity, such as the generalizability of the approach across different types of software systems and the effectiveness of the Transformer model in handling the highly variable and domain-specific nature of log data. Further research and extensive testing across varied datasets are necessary to fully evaluate the robustness and applicability of NeuralLog in real-world scenarios. Huang *et al.* (2020) [66] introduces a hierarchical transformer-based anomaly detection framework designed to analyze system logs by examining both log template sequences and their parameter values. This approach is driven by the critical need for reliable anomaly detection mechanisms within the increasingly complex architectures of contemporary computer systems. Since system log anomalies can significantly affect numerous users and services, developing precise and efficient anomaly detection models is imperative for effective service management and system maintenance. The authors address the limitations of current log-based anomaly detection techniques, which often struggle with unrecognized log templates or overlook the significance of parameter values, resulting in imprecise anomaly identification. The proposed model, HitAnomaly, emerges from the understanding that certain anomalies manifest not only through irregularities in log template sequences but also through unusual parameter values. The authors posit that incorporating the semantic content of log template sequences and the specific parameter values is crucial for identifying a broader spectrum of performance anomalies, suggesting that a model adept at integrating these elements would enhance anomaly detection capabilities. However, the model faces potential challenges, including its ability to generalize across diverse log data types and systems, scale to manage extensive log datasets and adapt to modifications in log formats or system upgrades. Moreover, the model's effectiveness is contingent on the accuracy of the log parser, with parsing inaccuracies posing additional risks to its operational performance.

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## 8.3 Microservice Anomaly Detection In this section, we delve into the sophisticated realm of monitoring and ensuring the reliability of distributed systems through the lens of LLMs. As the architecture of digital services shifts towards microservices—a collection of loosely coupled, independently deployable services—the complexity of detecting anomalies increases significantly. This subsection explores how LLMs are leveraged to navigate this complexity. It offers advanced solutions for identifying discrepancies that may indicate performance issues, failures, or security threats within individual microservices or their interactions. The ability of LLMs to analyze and interpret the vast amounts of data generated by these distributed systems enables a proactive approach to anomaly detection, facilitating early identification and resolution of potential issues. By examining the unique challenges posed by microservice architectures, including dynamic scaling and inter-service communication, this subsection showcases the innovative use of LLMs in enhancing system resilience, security, and operational efficiency. Through detailed case studies and technical insights, this section aims to underscore the transformative impact of LLMs on anomaly detection in modern distributed systems, emphasizing their critical role in maintaining the reliability and security of digital infrastructures. Sarda *et al.* (2023) [204] proposes a pipeline named ADARMA platform for automatic anomaly detection and remediation based on LLMs aiming to enhance real-time anomaly detection and auto-remediation for microservice deployments. The combination of anomaly detection and auto-remediation reduces downtime and enhances system reliability, resulting in increased productivity and customer satisfaction, which, in turn, drives higher revenue. Prior works have overlooked auto-remediation. The current work focuses on prompt developing and fine-tuning LLMs for auto-remediating anomalies rather than the entire pipeline. In the future, the authors plan to refine detection accuracy, expand remediation tactics, and evaluate the approach's long-term impact. Khlaisamniang *et al.* (2023) [205] integrates generative AI technology into self-healing systems, which leverages GPT-4 for automated code generation to enhance the operations of large-scale systems and facilitate automatic repairs. The focus is optimizing system functionality and efficiency at scale while reducing reactive tasks requiring human intervention. The threat might be that ChatGPT was provided with unsuitable prompts to yield unexpected outcomes in log parsing.

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## 9 Threats In the exploration of leveraging LLMs for forecasting and anomaly detection, several significant challenges and deficiencies have become apparent, shaping the landscape of current methodologies and their practical applications. This section delves into the core threats that hinder the effectiveness and reliability of LLMs in these domains. Firstly, the dependence on extensive historical datasets raises concerns about data availability, quality, and the potential for model bias. The issue of generalizability is also critical, as models often struggle to apply learned patterns across diverse contexts or when encountering novel scenarios. Furthermore, the phenomena of hallucination and robustness underscore the models' tendencies to generate misleading or inaccurate outputs under certain conditions, questioning their reliability. The knowledge boundary of LLMs, defined by the scope of their training data, presents another fundamental challenge, limiting their ability to generate insights beyond their informational horizon. Lastly, computational efficiency remains a daunting obstacle, as the resource-intensive nature of these models can restrict their accessibility and scalability. Addressing these threats is paramount for advancing the utility of LLMs in forecasting and anomaly detection, necessitating a multifaceted approach to enhance their performance, reliability, and applicability in real-world settings.

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## 9.1 Extensive Historical Datasets Dependence The dependence on extensive historical datasets stands as a formidable challenge in the deployment of LLMs for forecasting and anomaly detection. This reliance not only necessitates the availability of vast amounts of data but also raises critical issues regarding the representativeness, quality, and bias inherent in the collected information. Historical data, by its nature, may not always encapsulate future trends or rare, anomalous events with sufficient accuracy, leading to models that are potentially myopic or skewed in their predictions and detections. Moreover, the acquisition of such datasets often involves significant financial, legal, and ethical considerations, particularly when dealing with sensitive or proprietary information. To mitigate these challenges, several strategies can be employed. One approach involves enhancing data diversity and representativeness through techniques such as data augmentation, synthetic data generation, and transfer learning, which can help models generalize better to unseen scenarios. Additionally, employing robust data cleaning and preprocessing methodologies can significantly improve the quality of the datasets, reducing noise and minimizing bias. Active learning and few-shot learning techniques offer promising avenues to reduce the dependency on large datasets by enabling models to learn effectively from smaller, more targeted data samples. Lastly, the development of models that can dynamically update and incorporate new data streams can help alleviate the reliance on static historical datasets, making them more adaptive to evolving trends and patterns. Addressing the extensive historical datasets dependence not only involves technical and methodological advancements but also a concerted effort to ensure ethical data practices, emphasizing transparency, fairness, and inclusivity in data collection and model training processes. By tackling these issues head-on, the field can move towards more reliable, efficient, and equitable forecasting and anomaly detection solutions that are less tethered to the limitations of their underlying data.

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## 9.2 Generalizability Generalizability emerges as a pivotal concern in harnessing LLMs for forecasting and anomaly detection, highlighting the challenge of applying insights derived from specific datasets across varied contexts and domains. This issue is particularly pronounced when models trained on data from one domain or time period are expected to perform accurately on data from another, often leading to suboptimal predictions and detections. The root of this challenge lies in the models' ability to abstract and transfer learned patterns to new, unseen environments, a task that is not trivial given the complex and dynamic nature of real-world data. To enhance the generalizability of LLMs, several strategies can be considered. Developing models with a stronger emphasis on domain adaptation techniques allows for more flexible adjustments to different data characteristics, potentially improving performance across diverse settings. Incorporating multi-task learning frameworks can also aid in this endeavor by enabling models to learn from a variety of tasks simultaneously, fostering a broader understanding that can be applied to new problems. Further, the application of meta-learning approaches, where models learn to learn, offers a pathway to quickly adapt to new domains with minimal data requirements. Another solution lies in the rigorous evaluation of models across heterogeneous datasets and conditions prior to deployment, ensuring their robustness and adaptability. Investing in these approaches not only addresses the immediate challenge of generalizability but also contributes to the development of more versatile and resilient forecasting and anomaly detection systems. By prioritizing the creation of models that can navigate the nuances of different domains with greater ease, researchers and practitioners can expand the applicability and effectiveness of LLMs, paving the way for innovations that are both impactful and enduring across a multitude of scenarios.

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## 9.3 Hallucination And Robustness The phenomena of hallucination and robustness in LLMs for forecasting and anomaly detection underscore a critical vulnerability: the tendency of these models to generate false or misleading information (hallucinations) and their susceptibility to performance degradation under adversarial or noisy conditions. Hallucination challenges the credibility of model outputs, as LLMs might produce plausible yet entirely fabricated data points or trends, leading to misguided decisions or analyses. Similarly, a lack of robustness signifies that minor perturbations in the input data or adversarial attacks could significantly impair the model's accuracy and reliability, jeopardizing its utility in sensitive or critical applications. Addressing these issues requires a multifaceted approach focused on enhancing the integrity and resilience of model outputs. Implementing rigorous validation and verification mechanisms can help in identifying and mitigating hallucinations, ensuring that model predictions are grounded in the data. Techniques such as adversarial training, where models are exposed to and learn from perturbed or challenging inputs during training, can improve robustness by preparing the model for a wider array of input scenarios. Furthermore, incorporating uncertainty quantification methods allows for a better assessment of the confidence in model outputs, providing users with valuable context regarding the reliability of predictions and detections. The development of interpretability tools and frameworks also plays a crucial role, as understanding the reasoning behind model outputs can help in diagnosing and correcting for hallucinations and vulnerabilities. By investing in these strategies, the field can advance towards creating LLMs that not only excel in forecasting and anomaly detection tasks but do so with a higher degree of trustworthiness and resilience, marking a significant step forward in the practical deployment of these technologies.

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## 9.4 Knowledge Boundary The concept of the knowledge boundary in the context of LLMs for forecasting and anomaly detection refers to the inherent limitations of these models to generate insights or predictions beyond the scope of their training data. This limitation poses a significant challenge, as LLMs may struggle to accurately address novel events, emerging trends, or previously unseen anomalies, leading to potential gaps in their predictive capabilities. The knowledge boundary essentially demarcates the frontier of the model's understanding, beyond which its reliability and accuracy can sharply decline. This is particularly problematic in rapidly evolving domains or in situations where the future does not neatly reflect the past. To extend the knowledge boundary of LLMs, several strategies can be implemented. One approach is continuous or incremental learning, where models are routinely updated with new data, allowing them to adapt to recent developments and incorporate emerging patterns into their predictions. Another strategy involves leveraging transfer learning, where a model trained on one task is adapted for another, potentially related task, thereby utilizing its pre-existing knowledge base to bridge gaps in understanding. Additionally, employing ensemble methods that combine the outputs of multiple models can help in mitigating the knowledge boundary issue, as different models may capture varied aspects of the data, providing a more comprehensive overview. The integration of external knowledge bases or expert systems with LLMs offers another promising solution, where models can access and incorporate specialized knowledge that may not be present in their training datasets. Furthermore, developing models with advanced reasoning capabilities and the ability to query external sources when faced with unknowns can enhance their ability to navigate beyond their initial knowledge boundaries. By adopting these strategies, the field can make strides towards developing LLMs with broader, more flexible knowledge bases, significantly enhancing their utility and effectiveness in forecasting and anomaly detection across a wider range of scenarios and domains.

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## 9.5 Computational Efficiency The challenge of computational efficiency in deploying LLMs for forecasting and anomaly detection cannot be overstated. The sheer scale and complexity of these models demand substantial computational resources, making them less accessible for many organizations and potentially limiting their scalability and practicality for real-time applications. High computational costs are associated not only with training these models but also with their inference, especially when processing large volumes of data or requiring rapid response times. This computational burden poses significant hurdles, particularly for small to medium-sized enterprises or in scenarios where computational resources are constrained. Addressing the computational efficiency of LLMs involves a multi-pronged approach. Model optimization techniques, such as pruning, quantization, and knowledge distillation, can significantly reduce model size and complexity while maintaining performance, making the models lighter and faster for both training and inference phases. Additionally, adopting efficient architectures specifically designed for speed and low resource consumption, such as transformer variants optimized for efficiency, can further alleviate computational demands. Leveraging hardware acceleration through the use of GPUs, TPUs, and specialized inference chips offers another avenue to enhance computational efficiency. These technologies can dramatically speed up model computations, making it feasible to deploy LLMs in more resource-sensitive environments. Furthermore, the development of cloud-based solutions and edge computing allows for the distribution of computational tasks, optimizing resource usage across networks and devices, thereby reducing the overall computational load on individual systems. Efforts to improve algorithmic efficiency, through advancements in model design and training methodologies, also play a critical role. Techniques that enable more data-efficient learning, such as few-shot learning or transfer learning, can reduce the need for extensive computation by minimizing the amount of data required to train or adapt models effectively. By focusing on these strategies, the research and development community can make significant strides towards creating LLMs that are not only powerful and accurate but also computationally efficient, ensuring their wider accessibility and applicability in a diverse range of forecasting and anomaly detection tasks.

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## 10 Future Directions And Trends As the field of LLMs continues to evolve, its application in forecasting and anomaly detection is poised for transformative advancements. The convergence of technological innovation, research breakthroughs, and interdisciplinary collaboration heralds a future where LLMs can offer unprecedented accuracy, adaptability, and insight. This section outlines key future directions and trends that are expected to shape the utilization of LLMs in these domains.

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## - Integration Of Multimodal Data Sources The future of LLMs in forecasting and anomaly detection is likely to see a significant shift towards the integration of multimodal data sources. By combining textual data with visual, auditory, and sensor-based information, LLMs can develop a more holistic understanding of complex phenomena. This multimodal approach could enhance the models' ability to detect nuanced anomalies and forecast events with greater precision, leveraging the complementary strengths of diverse data types, and benefit data automated validation process [206].

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## - Advancements In Transfer And Meta-Learning Transfer and meta-learning represent promising avenues for making LLMs more adaptable and efficient. Future developments in these areas could enable models to swiftly adjust to new domains or tasks with minimal additional training. Such capabilities would be invaluable in rapidly changing environments or in applications where data scarcity poses a challenge [207, 208]. By improving the versatility of LLMs, these techniques can expand their applicability across a wider range of forecasting and anomaly detection scenarios.

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## - Focus On Explainability And Trustworthiness As LLMs assume a more prominent role in decision-making processes, the demand for explainability and trustworthiness will intensify. Future research is expected to prioritize the development of models that not only perform with high accuracy but also provide transparent and interpretable explanations for their outputs. Enhancing the explainability of LLMs can build trust among users [209], facilitate the identification of biases, and ensure the ethical application of these technologies.

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## - Medical Analysis Recent research in medical analysis has achieved notable advancements in image segmentation, classification, and trend prediction through end-to-end applications of deep learning and machine learning techniques [210, 211]. Medical imaging data, including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Optical Coherence Tomography (OCT) [212], often consist of multilayer scan results, with pathological changes potentially distorting these results. Accurately segmenting or classifying such lesions necessitates extensive training on a large corpus of manually annotated medical images, enabling the model to learn pathological changes end-to-end. Interestingly, the task of identifying pathologies in medical images bears a strong resemblance to abnormality detection utilizing LLMs. This parallel raises a compelling research challenge: how can prior knowledge embedded in LLMs be harnessed to enhance learning efficiency, particularly in the context of limited labeled medical imagery? Concurrently, advancements in medical imaging technology have significantly enhanced imaging quality and increased the volume of data available [213, 214, 215]. Despite these advances, efforts to integrate medical data with LLMs have primarily focused on individual images [216, 217]. The task of employing LLMs to process and analyze largescale medical image volumes, which often include multilayer information, continues to pose a significant challenge. Future research directions could explore the development of LLMs specifically tailored to navigate and interpret the complexities introduced by these technological advancements in medical imaging. As LLMs holds the potential to revolutionize doctor-patient interactions, paving the way for more efficient and comprehensive communication channels. With the assistance of LLMs, healthcare professionals can anticipate improved patient education, enhanced clarity in medical discussions, and streamlined dissemination of complex medical information.

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## - Real-Time Processing And Edge Computing The ability to process data in real-time and deploy LLMs closer to data sources, such as through edge computing, is set to become a crucial trend. This shift towards real-time analysis and decentralized processing can significantly reduce latency, increase the timeliness of insights, and enable the deployment of LLMs in environments where immediate responses are critical. It also opens the door to new applications in sectors such as finance, healthcare, and manufacturing, where quick decision-making is paramount [218]. As a concrete example, in the realm of indoor positioning, LLMs have the potential to discern patterns in edge data, such as trends in WiFi signals, thereby augmenting the precision of existing WiFi signal-based indoor positioning systems [219]. This capability demonstrates the extensive applicability of LLMs in leveraging real-time edge data for improved accuracy in critical applications.

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## - Sustainable And Energy-Efficient Modeling As computational demands continue to grow, the sustainability and energy efficiency of LLMs will become a pressing concern. Future trends are likely to include the pursuit of more environmentally friendly models through optimized algorithms, energy-efficient hardware, and practices that minimize the carbon footprint of training and deploying LLMs. This focus on sustainability is essential for ensuring that the benefits of LLMs can be realized without exacerbating environmental impacts. In conclusion, the trajectory of LLMs in forecasting and anomaly detection is marked by exciting opportunities and challenges. By embracing these future directions and trends, the field can unlock new potentials, addressing pressing issues while paving the way for innovative applications that leverage the full capabilities of LLMs in understanding and predicting complex systems.

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## - Computer Vision LLMs have made significant strides in aligning with images and performing tasks such as classification and semantic segmentation [220, 221, 222]. However, within the field of computer vision, several low-level visual processing tasks persist, which pose challenges in establishing direct relationships with semantics, thus hindering the direct application of LLMs to these tasks. These low-level visual tasks encompass various aspects, including denoising, defect detection, multi-view reconstruction, and more [223, 224]. Despite their crucial role in image processing and analysis, these tasks often involve intricate visual patterns and features that are not easily discernible through high-level semantic representations alone. As a result, incorporating LLMs into these tasks remains challenging due to the inherent disparity between low-level visual processing and semantic understanding.

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## - Collaboration Across Disciplines The future development and application of LLMs in forecasting and anomaly detection will benefit greatly from increased collaboration across different fields, including multi-core system [225], statistics, machinery [226, 227], robotics [228], other domain-specific areas [229, 230, 231, 232, 233, 234, 235], and ethics [236, 237, 238]. Such interdisciplinary efforts can enrich the models with diverse perspectives and expertise, leading to more robust, innovative, and ethically sound solutions.

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## 11 Related Surveys And Reviews With the rapid advancement of LLMs, considerable comprehensive reviews have appeared, offering in-depth analyses of different facets of this technology. Zhao *et al.* [239] provide an extensive overview of LLMs, detailing their background, fundamental discoveries, and core technologies, summarizing a broad spectrum of existing research. Conversely, Yang *et al.* [240] concentrate on the application spectrum of LLMs across various downstream tasks, highlighting the deployment challenges that accompany their use. Chang *et al.* [241] focus on the evaluation methodologies for LLMs, exploring the criteria, contexts, and methodologies for assessing their performance in downstream applications and societal impacts. Chang and Bergen [242] delve into the abilities and constraints of LLMs across varied downstream tasks. Huang *et al.* [243] review the progress in enhancing and assessing the reasoning capabilities of LLMs. These studies collectively address multiple aspects of LLMs, like training, evaluation, and application to different domains. However, before this paper, the burgeoning and promising domain of LLM-based Agents had not been focused specifically. This work compiles over 40 latest relevant works on LLM-based forecaster and anomaly detectors, encapsulating their development, applications, and evaluation processes.

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## 12 Conclusion This systematic literature review has explored the burgeoning field of LLMs in the context of forecasting and anomaly detection, offering a comprehensive overview of current methodologies, challenges, and future directions. As we have seen, LLMs hold immense potential for transforming these domains, providing sophisticated tools capable of parsing vast datasets to predict future events and identify deviations from norms with remarkable accuracy. However, the journey is fraught with challenges, including the dependence on extensive historical datasets, issues of generalizability, the occurrence of hallucinations, knowledge boundaries, and the need for computational efficiency. Despite these obstacles, the path forward is illuminated by promising solutions and innovations. The integration of multimodal data sources, advancements in transfer and meta-learning, a focus on explainability and trustworthiness, the push towards real-time processing and edge computing, interdisciplinary collaboration, and a commitment to sustainable modeling practices all represent key trends that will shape the future of LLMs in forecasting and anomaly detection. The review underscores the importance of continued research and development in this area, highlighting the need for models that are not only powerful and accurate but also transparent, adaptable, and accessible. As technology advances, so too must our approaches to ethical considerations, ensuring that the deployment of LLMs contributes positively to society and does not exacerbate existing inequalities or environmental issues. In conclusion, the potential of LLMs to revolutionize forecasting and anomaly detection is clear, yet realizing this potential requires a concerted effort across the scientific community, industry stakeholders, and policymakers. By addressing the challenges outlined in this review and harnessing the opportunities presented by emerging trends, we can look forward to a future where LLMs play a pivotal role in navigating the complexities of the modern world, driving insights and innovations that benefit all of society.

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# Can Separators Improve Chain-Of-Thought Prompting? Yoonjeong Park1* Hyunjin Kim1* Chanyeol Choi2 Junseong Kim2† Jy-yong Sohn1,2† Yonsei University1 Linq2 {dbw2140, hjhyunjinkim, jysohn1108}@yonsei.ac.kr {jacob.choi, junseong.kim}@getlinq.com

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# Can Separators Improve Chain-Of-Thought Prompting? ## Abstract Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs). The basic idea of CoT is to let LLMs break down their thought processes step-bystep by putting exemplars in the input prompt. However, the densely structured prompt exemplars of CoT may cause the cognitive overload of LLMs. Inspired by human cognition, we introduce COT-SEP, a novel method that strategically employs separators at the end of each exemplar in CoT prompting. These separators are designed to help the LLMs understand their thought processes better while reasoning. It turns out that COT-SEP significantly improves the LLMs' performances on complex reasoning tasks (e.g., GSM8K, AQuA, CSQA), compared with the vanilla CoT, which does not use separators. We also study the effects of the type and the location of separators tested on multiple LLMs, including GPT-3.5-Turbo, GPT-4, and LLaMA-2 7B. Interestingly, the type/location of separators should be chosen appropriately to boost the reasoning capability of CoT.

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# Can Separators Improve Chain-Of-Thought Prompting? ## 1 Introduction The use of large language models (LLMs) has significantly transformed our methods of processing information, enhancing performance across various fields. A key development in this area is Chainof-Thought (CoT) prompting (Wei et al., 2022b), which enables LLMs to process complex reasoning. By outlining thought processes in a step-by-step manner, CoT prompting enables LLMs to demonstrate more sophisticated reasoning. By reasoning step-by-step, similar to the human cognition process, LLMs are able to tackle complicated problems with enhanced precision. A noteworthy observation in the current implementation of CoT prompting is the densely structured few-shot exemplars within a single prompt (see the left column of Fig. 1). While this approach gives a broad context to the LLMs, it may also cause a cognitive overload, ultimately limiting the LLM's capability to process and analyze information efficiently. In human cognition, strategic separations and breaks in text, such as a linebreak, play an essential role in human comprehension and enhancing reasoning. Inspired by this observation, we present COT-SEP, a novel and simple approach that strategically inserts separators at the optimal positions. Our method puts separators at the position where the LLM can segment information into manageable portions, therefore enabling better comprehension by the LLM. Our approach led to a significant improvement in the performance of LLMs over CoT prompting, underscoring the importance of structured formatting for optimizing LLM outputs. We conduct evaluations on multiple separators on arithmetic reasoning benchmarks and a commonsense reasoning benchmark, revealing that inserting separators at an adequate location and creating a pattern for LLMs to decipher outperforms the original CoT prompting technique. Various prompting methods are proposed to improve the performance of CoT and its variants (Ling et al., 2023; Long, 2023; Besta et al., 2023; Weng et al., 2023b; Zhang et al., 2023), which introduce additional modules for iterative refinement and verification of intermediate thoughts. Another line of works proposes refining input questions (Xi et al., 2023; Deng et al., 2023) to let LLMs better interpret the target reasoning tasks. However, these existing methods require multiple iterations through LLMs, leading to increased expenses and extended timeframes. Additionally, their taskspecific designs make practical applications challenging. Unlike these existing methods, COT-SEP is straightforward and effective in improving CoT prompting,

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# Can Separators Improve Chain-Of-Thought Prompting? ## 1 Introduction a et al., 2023; Weng et al., 2023b; Zhang et al., 2023), which introduce additional modules for iterative refinement and verification of intermediate thoughts. Another line of works proposes refining input questions (Xi et al., 2023; Deng et al., 2023) to let LLMs better interpret the target reasoning tasks. However, these existing methods require multiple iterations through LLMs, leading to increased expenses and extended timeframes. Additionally, their taskspecific designs make practical applications challenging. Unlike these existing methods, COT-SEP is straightforward and effective in improving CoT prompting, by simply introducing a separator (SEP) between exemplars. Our empirical results on COT-SEP indicate that using separators between exemplars in CoT prompting significantly aids the reasoning process of LLMs. For example, adding separators increases the accuracy by 2.8% on AQuA dataset and by 1.3% on GSM8K dataset, when tested on GPT-3.5- Turbo model. Interestingly, we discover that the proper placement of separators in prompts considerably impacts the LLM's reasoning capabilities, potentially setting a new standard in the design of prompts for LLMs.

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# Can Separators Improve Chain-Of-Thought Prompting? ## 2 Related Work Large Language Model (LLM) Reasoning Complex tasks involving logical thinking, particularly in solving mathematical problems, are commonly challenging for natural language processing (NLP) models. (Cobbe et al., 2021; Koncel-Kedziorski et al., 2016; Patel et al., 2021). Recent progress in the development of LLMs (Touvron et al., 2023; Zhang et al., 2022; Chowdhery et al., 2022; Wei et al., 2022a; Brown et al., 2020) demonstrates remarkable capabilities in complex reasoning tasks. These works suggest that instead of directly generating final answers, LLMs perform better on reasoning tasks when guided through a step-by-step process. This approach involves using examples in few-shot settings and employing effective prompting strategies, such as the Chainof-Thought (CoT) (Wei et al., 2023) prompting. To enhance the step-by-step reasoning process further, recent studies have employed external modules (Bostrom et al., 2022; Creswell and Shanahan, 2022; Tafjord et al., 2021; Lyu et al., 2023; Chen et al., 2023) to verify and refine intermediate thoughts. Similar to these previous work, our work uses the step-by-step reasoning process and focuses on aiding the process for enhanced reasoning. In-Context Learning Large Language Models (LLMs) have demonstrated impressive abilities in few-shot learning (Lu et al., 2023; Qiao et al., 2023). In particular, by incorporating a few exemplars into the prompts, LLMs can show exceptional performance without the need for finetuning on a training dataset (Weng et al., 2023a; Wang et al., 2023a), and such LLMs' ability is termed as in-context learning (ICL). However, these approaches face challenges when encountering tasks that demand complex reasoning. This led to the emergence of CoT (Wei et al., 2022b), which is an approach that generates a series of reasoning steps to arrive at the final answer and is highly effective in solving difficult tasks. Recent work has also highlighted the in-context abilities of LLMs when combined with CoT prompting (Wang et al., 2023b; Ling et al., 2023). Building upon in-context learning, we propose the first work using separators in exemplars to enhance complex reasoning.

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep Consider one's ability to process complex information. It is common to break down the information into small parts for ease of processing, and using separators can significantly enhance one's comprehension and readability by providing visual breaks. Inspired by this observation, we introduce COT-SEP, a method that places separators at the | | | Arithmetic Reasoning | Commonsense Reasoning | |-----------------|------------|------------------------|-------------------------| | Method | GSM8K | AQuA | CSQA | | Vanilla CoT ( | Wei et al. | , | 2022b | | ± | | | | | 0.17 | | | | | 46.5 | | | | | ± | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | 46.5 | | | | | ± | | | | | 0.82 | | | | | 76.5 | | | | | ± | | | | | 0.14 | | | | | C |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | 0.14 | | | | | C | | | | | O | | | | | T-S | | | | | EP | | | | | ( | | | | | TripleSkip |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | ( | | | | | TripleSkip | | | | | , | i.e., | \n\n\n) | | | 71.7 | | | | | ± | | | | | 0.26 | | | | | 49.3

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | 0.26 | | | | | 49.3 | | | | | ± | | | | | 0.19 | | | | | 77.4 | | | | | ± | | | | | 0.16

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | ± | | | | | 0.16 | | | | | C | | | | | O | | | | | T-S | | | | | EP | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | EP | | | | | ( | | | | | TripleHash | | | | | , | i.e., | ###) | 70.6 | | ± | | | | | 0.09 | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | 0.09 | | | | | 47.1 | | | | | ± | | | | | 0.33 | | | | | 76.9 | | | | | ± | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | ± | | | | | 0.08 | | | | | C | | | | | O | | | | | T-S | | | | | EP | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | | EP | | | | | ( | | | | | TripleStar | | | | | , | i.e., | ***) | 70.9 | | ± | | | | | 0.14 | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | 0.14 | | | | | 46.3 | | | | | ± | | | | | 0.78 | | | | | 76.7 | | | | | ± | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | | ± | | | | | 0.24 | | | | | C | | | | | O | | | | | T-S | | | | | EP | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep T-S | | | | | EP | | | | | ( | | | | | <br> | | | | | ) | 71.6 | | | | ± | | | | | 0.29 | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | ± | | | | | 0.29 | | | | | 46.6 | | | | | ± | | | | | 1.10 | | | | | 76.9 | | | | | ± | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | 76.9 | | | | | ± | | | | | 0.08 | | | | | C | | | | | O | | | | | T-S | | | | | EP |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | T-S | | | | | EP | | | | | ( | | | | | <br/> | | | | | ) | 70.0 | | | | ± | | | | | 0.43 |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | ± | | | | | 0.43 | | | | | 45.8 | | | | | ± | | | | | 0.94 | | | | | 76.5 | | | | | ±

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | 76.5 | | | | | ± | | | | | 0.24 | | | | | Heterogeneous C | | | | | O | | | | | T-S | | | | | EP

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | T-S | | | | | EP | | | | | 71.3 | | | | | ± | | | | | 0.42 | | | | | 47.6 | | | | | ±

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | 47.6 | | | | | ± | | | | | 0.52 | | | | | 76.9 | | | | | ± | | | | | 0.14 | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 3 Cot-Sep | | | | 0.14 | | | | end of each prompt exemplar. Similar to Chain-of- Thought prompting, COT-SEP generates a logical sequence of intermediate reasoning steps leading to the final answer. In contrast, as shown on the right of Fig. 1, when we include examples of chainof-thought sequences into the prompt exemplars, we place separators at the end of each exemplar, which enables the LLMs to process a large amount of information in that manner.

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4 Experiments 4.1 Experimental Settings Our experiments are heavily based on the Vanilla CoT prompting (Wei et al., 2022b), and we use OpenAI's gpt-3.5-turbo-06131 for the results in Table 1 and 3. For the results in Table 2, we use Meta's llama-2-7b2, OpenAI's gpt-4-06131 and gpt-4-turbo, specifically the version of gpt-4-0125-preview1. For our experiments, we use benchmarks where CoT prompting leads to substantial improvement over standard prompting. To be specific, we test on two challenging mathematical reasoning task benchmarks (GSM8K (Cobbe et al., 2021) with 1319 samples and AQuA (Ling et al., 2017) with 254 samples) and one commonsense reasoning benchmark (CSQA (Talmor et al., 2019) with 1221 samples). We follow the prompts designed by Wei et al. (2022b) and added separators to test COT-SEP. More details regarding the exemplars used in our experiments are included in Sec. B in Appendix. We report the statistics of accuracy values obtained by running the experiments for 3 trials.

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results Table 1 compares the performances of vanilla CoT and COT-SEP tested on three reasoning tasks, for GPT-3.5-Turbo. One can confirm that LLMs attain higher performances with the inclusion of separators (specifically, \n\n\n) at the end of each prompt exemplar. This validates that the insertion of separators after each exemplar significantly improves LLM reasoning. Now, the follow-up question is, which separator is effective in improving reasoning? We test 5 different separators: (1) TripleSkip (\n\n\n) which uses three newline separators, (2) TripleHash (###), (3) TripleStar (***), and two versions of HTML line break tags: (4) <br> and (5) <br/>. Table 1 shows that although the use of various separators in COT-SEP improves LLM reasoning compared to the vanilla CoT, TripleSkip is the most effective separator for enhancing LLM reasoning for the tested datasets, on GPT-3.5-Turbo. It is true that some separators are hurting the accuracy (e.g., inserting <br/> separator after exemplars is not desirable for GSM8K and AQuA datasets), which implies that it is necessary to appropriately choose separators. In a situation when there is uncertainty about which separator is the appropriate choice in a specific task, one might wonder which separator should be chosen. To answer this question, we test with a variant of our method, dubbed as Heterogeneous COT-SEP, which uses different separators for different exemplars within the prompt. Recall that we consider 5 different types of separators, TripleSkip, TripleHash, TripleStar, <br> and <br/>. These types of separators are placed in turn after distinct exemplars. See Table 10 for an example prompt for Heterogeneous COT-SEP. Our result in Table 1 shows that Heterogeneous COT- SEP outperforms vanilla CoT, implying that even in the absence of a clear choice for the best separator, COT-SEP improves LLM reasoning compared to vanilla CoT. Thus, Heterogeneous COT-SEP can be used in practical scenarios where we do not have | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results br/>. These types of separators are placed in turn after distinct exemplars. See Table 10 for an example prompt for Heterogeneous COT-SEP. Our result in Table 1 shows that Heterogeneous COT- SEP outperforms vanilla CoT, implying that even in the absence of a clear choice for the best separator, COT-SEP improves LLM reasoning compared to vanilla CoT. Thus, Heterogeneous COT-SEP can be used in practical scenarios where we do not have | | | | | | | | Llama2-7b | GPT-4-turbo | GPT-4 | |-------------|-------|------|------|-------|------|------|-------------|---------------|---------| | Method | GSM8K | AQuA | CSQA | GSM8K | AQuA | CSQA | GSM8K | AQuA | CSQA | | Vanilla CoT | 14.7 | 19.3 | 62.2 | 63.0 | | | | | | | ± | | | | | | | | | | | 0.95 | 31.4 | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | 0.95 | 31.4 | | | | | | | | | | ± | | | | | | | | | | | 1.32 | | | | | | | | | | | 86.4 | | | | | | | | | | | ± | | | | | | | | | | | 0.31 | 89.4

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | ± | | | | | | | | | | | 0.31 | 89.4 | | | | | | | | | | ± | | | | | | | | | | | 0.19 | 71.1 | | | | | | | | | | ± | | | | | | | | | | | 0.78 | 86.4 | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | | | | | 0.78 | 86.4 | | | | | | | | | | ± | | | | | | | | | | | 0.14 | | | | | | | | | | | C | | | | | | | | | | | O | | | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | O | | | | | | | | | | | T-S | | | | | | | | | | | EP | | | | | | | | | | | ( | | | | | | | | | | | TripleSkip | | | | | | | | | | | ) | 13.4 | 19.3 | 62.4 |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | TripleSkip | | | | | | | | | | | ) | 13.4 | 19.3 | 62.4 | | | | | | | | 68.1 | | | | | | | | | | | ± | | | | | | | | | | | 0.45 | | | | | | | | | | | 34.0 | | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | | 34.0 | | | | | | | | | | | ± | | | | | | | | | | | 0.65 | 86.0 | | | | | | | | | | ± | | | | | | | | | | | 0.65 | 88.6 | | | | | | | | | | ±

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | 0.65 | 88.6 | | | | | | | | | | ± | | | | | | | | | | | 0.29 | | | | | | | | | | | 71.5 | | | | | | | | | | | ± | | | | | | | | | | | 0.82 | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | | | | | 0.82 | | | | | | | | | | | 86.5 | | | | | | | | | | | ± | | | | | | | | | | | 0.24 | | | | | | | | | | | C | | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | C | | | | | | | | | | | O | | | | | | | | | | | T-S | | | | | | | | | | | EP | | | | | | | | | | | ( | | | | | | | | | | | TripleHash | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | ( | | | | | | | | | | | TripleHash | | | | | | | | | | | ) | | | | | | | | | | | 15.1 | | | | | | | | | | | 19.3 | | | | | | | | | | | 62.7 | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | | | | | 62.7 | | | | | | | | | | | 67.5 | | | | | | | | | | | ± | | | | | | | | | | | 0.19 | 34.0 | | | | | | | | | | ± | | | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | ± | | | | | | | | | | | 1.90 | 86.1 | | | | | | | | | | ± | | | | | | | | | | | 0.22 | | | | | | | | | | | 89.6 | | | | | | | | | | | ± | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | 89.6 | | | | | | | | | | | ± | | | | | | | | | | | 0.12 | 69.6 | | | | | | | | | | ± | | | | | | | | | | | 1.47 | 86.3 | | | | | | | | | | ± | | | | | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | | | | | | | ± | | | | | | | | | | | 0.22 | | | | | | | | | | any prior knowledge on the optimal separator for our new target task. We also test COT-SEP on other models including Llama2-7b, GPT-4-turbo and GPT-4, in Table 2. It turns out that compared with vanilla CoT, adding separators (either TripleSkip or TripleHash) increases the accuracy on most cases. For example, in GPT-4-turbo model, COT-SEP (TripleSkip) enjoys 5.1% and 2.6% gap for GSM8K and AQuA datasets, respectively. We also explore whether the location/number of separators used in COT-SEP contributes to enhancing CoT prompting. To check the effect of the location of separators, we experiment COT-SEP with two versions, COT-SEP (Unit: Exemplar) and COT-SEP (Unit: Sentence), as shown in Fig. 2. The first version adds the separator at the end of each exemplar, while the second version adds the separator between sentences within each exemplar's CoT. Table 3 shows that COT-SEP (Unit: Exemplar) outperforms COT-SEP (

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results of separators used in COT-SEP contributes to enhancing CoT prompting. To check the effect of the location of separators, we experiment COT-SEP with two versions, COT-SEP (Unit: Exemplar) and COT-SEP (Unit: Sentence), as shown in Fig. 2. The first version adds the separator at the end of each exemplar, while the second version adds the separator between sentences within each exemplar's CoT. Table 3 shows that COT-SEP (Unit: Exemplar) outperforms COT-SEP (Unit: Sentence). This can be explained by the visualization in Fig. 2: in the prompt of COT-SEP (Unit: Sentence), the next question (say Question2) and the answer of the previous question (say Answer1) appear as a set, making it difficult for not only LLMs but also humans to understand, which results in the performance worse than COT-SEP and even vanilla CoT. This shows that the placement of separators is crucial. Table 3 also shows the effect of the number of separators. The result indicates that our COT-SEP (TripleSkip) method, with its specific number of three newline separators, achieves the best performance in GSM8K and AQuA benchmarks compared to the reduced number of separators. COT-SEP (TripleSkip) is also on par with the highest performance for the CSQA benchmark. | | | Arithmetic Reasoning | Commonsense Reasoning | |-------------------|-------|------------------------|-------------------------| | Method | GSM8K | AQuA | CSQA | | Vanilla CoT | 70.4 | | | | ±

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | GSM8K | AQuA | CSQA | | Vanilla CoT | 70.4 | | | | ± | | | | | 0.17 | | | | | 46.5 | | | | | ± | | | | | 0.82 | | | | | 76.5

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | 0.82 | | | | | 76.5 | | | | | ± | | | | | 0.14 | | | | | C | | | | | O | | | | | T-S |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | | O | | | | | T-S | | | | | EP | | | | | (Unit : Exemplar) | | | | | \n | 71.0 | | | | ± | | | | | 0.29 | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | ± | | | | | 0.29 | | | | | 46.0 | | | | | ± | | | | | 0.68 | | | | | 77.5 | | | | | ± | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results | | 77.5 | | | | | ± | | | | | 0.12 | | | | | \n\n | 70.5 | | | | ± | | | | | 0.22 | | | | | 47.6 | | |

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# Can Separators Improve Chain-Of-Thought Prompting? ## 4.2 Results 0.22 | | | | | 47.6 | | | | | ± | | | | | 0.83 | | | | | 77.0 | | | | | ± | | | | | 0.12 | | |