What is the current state of research on information extraction using large language models. Literature review in various studies?
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The current state of research on information extraction using large language models (LLMs) is highly promising and rapidly evolving. Studies have shown that LLMs, such as GPT-4, are capable of significantly enhancing biomedical knowledge curation. These models excel in tasks like recognizing protein interactions, pathways, and gene regulatory relations, offering automated extraction of crucial biological knowledge from scientific literature. Moreover, LLMs have revolutionized the field of Information Retrieval (IR), providing advanced solutions for text understanding, generation, and knowledge inference, while also posing challenges like computational costs and ethical considerations. Bibliometric analyses reveal the extensive impact of LLMs across various domains, showcasing their potential to transform science and technology.
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| Papers (5) | Insight |
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17 Citations | The paper provides a comprehensive review of large language models (LLMs) research from 2017 to 2023, covering core algorithm developments, NLP tasks, applications in diverse fields, and evolving research trends. |
03 Apr 2023 | The current state of research on information extraction using large language models is explored in various studies, showcasing trends, applications, and collaborations in LLMs research. |
| The current state of research on information extraction using Large Language Models (LLMs) is evolving, with LLMs enhancing text understanding, generation, and knowledge inference in Information Retrieval (IR) research. | |
| The research evaluates large language models for extracting molecular interactions and pathway knowledge, highlighting their effectiveness in recognizing protein interactions, pathways, and gene regulatory relations. | |
| Large language models like GPT-4 can enhance biomedical knowledge extraction through distillation, achieving significant gains in tasks like adverse drug event extraction without labeled data, showcasing promising advancements in information extraction. |
Related Questions
What are the latest advancements in Large Language Models for natural language processing tasks?5 answersThe latest advancements in Large Language Models (LLMs) for natural language processing tasks include exploring diverse attributed prompts for training data generation, developing specialized models like ClinicalGPT for medical applications, and introducing challenging benchmarks for assessing LLM capabilities across various dimensions like processing long documents, domain-specific knowledge utilization, multilingual understanding, and multitasking. These advancements aim to enhance model performance, address domain-specific requirements, and push the boundaries of LLM capabilities. Additionally, ongoing research focuses on refining LLM architectures, training strategies, and performance evaluations to improve training stability, generalization, and overall model effectiveness.
How do language models contribute to the efficiency and accuracy of information extraction processes?5 answersLanguage models (LMs) play a crucial role in enhancing the efficiency and accuracy of information extraction processes. They achieve this by leveraging pre-trained models like large language models (LLMs) and incorporating various techniques such as transformer-based models and graph neural networks. LMs aid in tasks like text classification, word sense disambiguation, named entity recognition, and relation extraction. Additionally, LMs reduce the need for data labeling and training multiple models by enabling end-to-end solutions for information extraction, thus streamlining the process. Furthermore, LMs like LLMs can be prompted to directly extract values from documents or synthesize code for extraction, showcasing a cost-quality tradeoff where code synthesis can be more cost-effective but less accurate than direct extraction. This demonstrates how language models significantly contribute to the efficiency and accuracy of information extraction tasks.
How effective are large language models in named entity extraction compared to traditional machine learning algorithms?10 answersLarge Language Models (LLMs) have shown significant promise in the field of Named Entity Recognition (NER), a crucial task in Natural Language Processing (NLP) that involves identifying specific entities within text. However, their effectiveness compared to traditional machine learning algorithms varies depending on the context and the specific requirements of the task.
Recent studies have demonstrated that while LLMs, such as ChatGPT, can perform NER tasks with a degree of success, they still lag behind fine-tuned models like BioClinicalBERT in terms of overall performance. For instance, in the context of extracting rare disease phenotypes, fine-tuning BioClinicalBERT achieved a higher F1 score compared to ChatGPT in both zero- and few-shot settings. Nonetheless, ChatGPT showed potential in achieving similar or even higher accuracy for certain entity types with minimal labeled data, suggesting that with appropriate prompt engineering, LLMs could match or outperform fine-tuned models for specific entities.
The introduction of methodologies like GPT-NER, which bridges the gap between sequence labeling tasks and text-generation models, has further enhanced the capabilities of LLMs in NER tasks. GPT-NER, for example, has demonstrated comparable performances to fully supervised baselines and has shown greater ability in low-resource and few-shot setups.
Moreover, LLMs have been explored for their utility in extracting structured tabular data from textual medical reports, showcasing their potential beyond traditional text classification models. However, challenges such as the "hallucination" issue, where LLMs might over-confidently label null inputs as entities, necessitate strategies like self-verification to ensure reliability.
In comparison, traditional machine learning models, when fine-tuned and combined with LLMs, can offer a synergistic approach. For instance, an adaptive filter-then-rerank paradigm that leverages both small Pre-trained Language Models (SLMs) and LLMs has shown promising improvements in information extraction tasks.
In summary, while LLMs have made remarkable strides in NER and related tasks, their effectiveness is enhanced when combined with traditional machine learning algorithms or when specific methodologies, such as GPT-NER, are employed to address their inherent limitations. This hybrid approach, leveraging the strengths of both LLMs and traditional algorithms, appears to be a promising direction for future research and application.
What are some studies which describe how large language models are being utilized?4 answersLarge language models (LLMs) have been utilized in various studies. One study explores the extension of chain-of-thought (CoT) prompting to medical reasoning, showing that prompting LLMs with Diagnostic-Reasoning CoT exemplars improves diagnostic accuracy. Another study investigates how LLMs can be used for spear phishing, demonstrating their ability to assist with reconnaissance and message generation stages of attacks. Additionally, LLMs have been applied in the analysis of ROS 2 logs generated by autonomous robots, with GPT-4 outperforming other models in answering questions related to log files. These studies highlight the versatility of LLMs in various domains, including medical reasoning, cybersecurity, and autonomous robotics log analysis.
Can large language models be used to extract crime events from text?5 answersLarge language models can be used to extract crime events from text. By converting video descriptions into high-quality textual descriptions, these models can detect and classify crimes with state-of-the-art performance using zero-shot reasoning. Additionally, language models can be instructed to extract a variety of structures from texts, including information related to crimes, by adding specific instructions before feeding the text into the model. Furthermore, large language models, such as GPT-3, have been shown to segment continuous narrative text into events, including crime events, with annotations that are significantly correlated with human annotations. This suggests that language models provide a feasible solution for automated event annotations, including crimes, and can contribute to the understanding of human event perception.
What are the current limitations of Large Language Models?5 answersLarge Language Models (LLMs) have several limitations. One limitation is the potential for biases in their output, which can introduce inaccuracies and reinforce societal biases. Another limitation is the vulnerability of LLMs to adversarial prompting attacks, where prompts can trigger the model to output undesired behaviors. Additionally, LLMs may struggle with aligning their behavior to be useful and unharmful for human users, as the alignment process may not completely remove undesired behaviors. Furthermore, the performance of LLMs in diagnostic tasks can vary depending on the type of input, with feature-based approaches yielding worse results compared to narrative-based approaches. These limitations highlight the need for further research and algorithmic development to ensure the safety, accuracy, and ethical use of LLMs in various applications.