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  1. Article: What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature.

    Wallace, Byron C

    IJCAI : proceedings of the conference

    2020  Volume 2019, Page(s) 6416–6420

    Abstract: Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these ...

    Abstract Ideally decisions regarding medical treatments would be informed by the totality of the available evidence. The best evidence we currently have is in published natural language articles describing the conduct and results of clinical trials. Because these are unstructured, it is difficult for domain experts (e.g., physicians) to sort through and appraise the evidence pertaining to a given clinical question. Natural language technologies have the potential to improve access to the evidence via semi-automated processing of the biomedical literature. In this brief paper I highlight work on developing tasks, corpora, and models to support semi-automated evidence retrieval and extraction. The aim is to design models that can consume articles describing clinical trials and automatically extract from these key clinical variables and findings, and estimate their reliability. Completely automating 'machine reading' of evidence remains a distant aim given current technologies; the more immediate hope is to use such technologies to help domain experts access and make sense of unstructured biomedical evidence more efficiently, with the ultimate aim of improving patient care. Aside from their practical importance, these tasks pose core NLP challenges that directly motivate methodological innovation.
    Language English
    Publishing date 2020-05-26
    Publishing country United States
    Document type Journal Article
    ISSN 1045-0823
    ISSN 1045-0823
    DOI 10.24963/ijcai.2019/899
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Revisiting Relation Extraction in the era of Large Language Models.

    Wadhwa, Somin / Amir, Silvio / Wallace, Byron C

    Proceedings of the conference. Association for Computational Linguistics. Meeting

    2023  Volume 2023, Page(s) 15566–15589

    Abstract: Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. ... ...

    Abstract Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a
    Language English
    Publishing date 2023-08-19
    Publishing country United States
    Document type Journal Article
    ISSN 0736-587X
    ISSN 0736-587X
    DOI 10.18653/v1/2023.acl-long.868
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Self-Repetition in Abstractive Neural Summarizers.

    Salkar, Nikita / Trikalinos, Thomas / Wallace, Byron C / Nenkova, Ani

    Proceedings of the conference. Association for Computational Linguistics. Meeting

    2023  Volume 2022, Page(s) 341–350

    Abstract: We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number ... ...

    Abstract We provide a quantitative and qualitative analysis of self-repetition in the output of neural summarizers. We measure self-repetition as the number of
    Language English
    Publishing date 2023-07-06
    Publishing country United States
    Document type Journal Article
    ISSN 0736-587X
    ISSN 0736-587X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Evaluating Factuality in Text Simplification.

    Devaraj, Ashwin / Sheffield, William / Wallace, Byron C / Li, Junyi Jessy

    Proceedings of the conference. Association for Computational Linguistics. Meeting

    2022  Volume 2022, Page(s) 7331–7345

    Abstract: ... ...

    Abstract Automated
    Language English
    Publishing date 2022-10-24
    Publishing country United States
    Document type Journal Article
    ISSN 0736-587X
    ISSN 0736-587X
    DOI 10.18653/v1/2022.acl-long.506
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article: Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges.

    Ramprasad, Sanjana / Marshall, Iain J / McInerney, Denis Jered / Wallace, Byron C

    Proceedings of the conference. Association for Computational Linguistics. Meeting

    2023  Volume 2023, Page(s) 236–247

    Abstract: ... We ... ...

    Abstract We present
    Language English
    Publishing date 2023-07-07
    Publishing country United States
    Document type Journal Article
    ISSN 0736-587X
    ISSN 0736-587X
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Trialstreamer

    Nye, Benjamin E / Nenkova, Ani / Marshall, Iain J / Wallace, Byron C

    Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting

    2021  Volume 2020, Page(s) 63–69

    Abstract: ... We ... ...

    Abstract We introduce
    Language English
    Publishing date 2021-04-27
    Publishing country United States
    Document type Journal Article
    DOI 10.18653/v1/2020.acl-demos.9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Evaluating the Zero-shot Robustness of Instruction-tuned Language Models

    Sun, Jiuding / Shaib, Chantal / Wallace, Byron C.

    2023  

    Abstract: Instruction fine-tuning has recently emerged as a promising approach for improving the zero-shot capabilities of Large Language Models (LLMs) on new tasks. This technique has shown particular strength in improving the performance of modestly sized LLMs, ... ...

    Abstract Instruction fine-tuning has recently emerged as a promising approach for improving the zero-shot capabilities of Large Language Models (LLMs) on new tasks. This technique has shown particular strength in improving the performance of modestly sized LLMs, sometimes inducing performance competitive with much larger model variants. In this paper we ask two questions: (1) How sensitive are instruction-tuned models to the particular phrasings of instructions, and, (2) How can we make them more robust to such natural language variation? To answer the former, we collect a set of 319 instructions manually written by NLP practitioners for over 80 unique tasks included in widely used benchmarks, and we evaluate the variance and average performance of these instructions as compared to instruction phrasings observed during instruction fine-tuning. We find that using novel (unobserved) but appropriate instruction phrasings consistently degrades model performance, sometimes substantially so. Further, such natural instructions yield a wide variance in downstream performance, despite their semantic equivalence. Put another way, instruction-tuned models are not especially robust to instruction re-phrasings. We propose a simple method to mitigate this issue by introducing ``soft prompt'' embedding parameters and optimizing these to maximize the similarity between representations of semantically equivalent instructions. We show that this method consistently improves the robustness of instruction-tuned models.
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Subject code 430
    Publishing date 2023-06-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Question answering systems for health professionals at the point of care-a systematic review.

    Kell, Gregory / Roberts, Angus / Umansky, Serge / Qian, Linglong / Ferrari, Davide / Soboczenski, Frank / Wallace, Byron C / Patel, Nikhil / Marshall, Iain J

    Journal of the American Medical Informatics Association : JAMIA

    2024  Volume 31, Issue 4, Page(s) 1009–1024

    Abstract: Objectives: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review ...

    Abstract Objectives: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement.
    Materials and methods: We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology, and forward and backward citations on February 7, 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems.
    Results: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians.
    Discussion: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.
    MeSH term(s) Humans ; Point-of-Care Systems ; Reproducibility of Results ; PubMed ; Health Personnel ; Machine Learning
    Language English
    Publishing date 2024-02-17
    Publishing country England
    Document type Systematic Review ; Journal Article
    ZDB-ID 1205156-1
    ISSN 1527-974X ; 1067-5027
    ISSN (online) 1527-974X
    ISSN 1067-5027
    DOI 10.1093/jamia/ocae015
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4128: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
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    Zs.MO 312: Show issues

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  9. Book ; Online: How Many and Which Training Points Would Need to be Removed to Flip this Prediction?

    Yang, Jinghan / Jain, Sarthak / Wallace, Byron C.

    2023  

    Abstract: We consider the problem of identifying a minimal subset of training data $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different. ... ...

    Abstract We consider the problem of identifying a minimal subset of training data $\mathcal{S}_t$ such that if the instances comprising $\mathcal{S}_t$ had been removed prior to training, the categorization of a given test point $x_t$ would have been different. Identifying such a set may be of interest for a few reasons. First, the cardinality of $\mathcal{S}_t$ provides a measure of robustness (if $|\mathcal{S}_t|$ is small for $x_t$, we might be less confident in the corresponding prediction), which we show is correlated with but complementary to predicted probabilities. Second, interrogation of $\mathcal{S}_t$ may provide a novel mechanism for contesting a particular model prediction: If one can make the case that the points in $\mathcal{S}_t$ are wrongly labeled or irrelevant, this may argue for overturning the associated prediction. Identifying $\mathcal{S}_t$ via brute-force is intractable. We propose comparatively fast approximation methods to find $\mathcal{S}_t$ based on influence functions, and find that -- for simple convex text classification models -- these approaches can often successfully identify relatively small sets of training examples which, if removed, would flip the prediction.

    Comment: Accepted to EACL 2023
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language
    Subject code 006
    Publishing date 2023-02-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Revisiting Relation Extraction in the era of Large Language Models

    Wadhwa, Somin / Amir, Silvio / Wallace, Byron C.

    2023  

    Abstract: Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. ... ...

    Abstract Relation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and then predict the relationship between them. Recent work has instead treated the problem as a \emph{sequence-to-sequence} task, linearizing relations between entities as target strings to be generated conditioned on the input. Here we push the limits of this approach, using larger language models (GPT-3 and Flan-T5 large) than considered in prior work and evaluating their performance on standard RE tasks under varying levels of supervision. We address issues inherent to evaluating generative approaches to RE by doing human evaluations, in lieu of relying on exact matching. Under this refined evaluation, we find that: (1) Few-shot prompting with GPT-3 achieves near SOTA performance, i.e., roughly equivalent to existing fully supervised models; (2) Flan-T5 is not as capable in the few-shot setting, but supervising and fine-tuning it with Chain-of-Thought (CoT) style explanations (generated via GPT-3) yields SOTA results. We release this model as a new baseline for RE tasks.

    Comment: Accepted to ACL 2023
    Keywords Computer Science - Computation and Language
    Subject code 006
    Publishing date 2023-05-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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