Cheap Ways of Extracting Clinical Markers from Texts
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917616577150976 |
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| author | Sandu, Anastasia Mihailescu, Teodor Nisioi, Sergiu |
| author_facet | Sandu, Anastasia Mihailescu, Teodor Nisioi, Sergiu |
| contents | This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task, which involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluating Large Language Models (LLM) as opposed to an alternative method that is much more memory and resource efficient. The first approach employs a good old-fashioned machine learning (GOML) pipeline consisting of a tf-idf vectorizer with a logistic regression classifier, whose representative features are used to extract relevant highlights. The second, more resource intensive, uses an LLM for generating the summaries and is guided by chain-of-thought to provide sequences of text indicating clinical markers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11227 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cheap Ways of Extracting Clinical Markers from Texts Sandu, Anastasia Mihailescu, Teodor Nisioi, Sergiu Computation and Language Machine Learning This paper describes the work of the UniBuc Archaeology team for CLPsych's 2024 Shared Task, which involved finding evidence within the text supporting the assigned suicide risk level. Two types of evidence were required: highlights (extracting relevant spans within the text) and summaries (aggregating evidence into a synthesis). Our work focuses on evaluating Large Language Models (LLM) as opposed to an alternative method that is much more memory and resource efficient. The first approach employs a good old-fashioned machine learning (GOML) pipeline consisting of a tf-idf vectorizer with a logistic regression classifier, whose representative features are used to extract relevant highlights. The second, more resource intensive, uses an LLM for generating the summaries and is guided by chain-of-thought to provide sequences of text indicating clinical markers. |
| title | Cheap Ways of Extracting Clinical Markers from Texts |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2403.11227 |