Papers with MedNLI
Improving Medical NLI Using Context-Aware Domain Knowledge (2020.starsem-1)
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| Challenge: | Domain knowledge is important to understand both the lexical and relational associations of words in natural language text . lack of annotated dataset can lead to wrong inference predictions . |
| Approach: | They propose a knowledge adaptive approach that encodes the premise/hypothesis texts by leveraging supplementary external knowledge alongside the UMLS based on the word contexts. |
| Outcome: | The proposed model can align token-level interactions between the premise and hypothesis more effectively. |
MedNLI Is Not Immune: Natural Language Inference Artifacts in the Clinical Domain (2021.acl-short)
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| Challenge: | a large number of crowdworker-constructed datasets have been used to conduct natural language inference (NLI) on unstructured, domainspecific texts such as patient notes, pathology reports, and scientific papers. |
| Approach: | They investigate whether MedNLI contains lexical and syntactic annotation artifacts associated with annotation process that allow hypothesis-only classifiers to achieve better-than-random performance. |
| Outcome: | The proposed model outperforms a majority-class baseline model on a physician-annotated dataset with premises extracted from clinical notes. |
Lessons from Natural Language Inference in the Clinical Domain (D18-1)
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| Challenge: | State of the art models with deep neural networks lack generalization capabilities in specialized domains where training data is limited. |
| Approach: | They propose a dataset annotated by doctors performing a natural language inference task grounded in the medical history of patients. |
| Outcome: | The proposed model outperforms existing models in the clinical domain by incorporating domain knowledge from external data and lexical sources. |
Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation (2023.eacl-main)
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| Challenge: | Biomedical data and benchmarks are highly valuable but limited in low-resource languages such as English. |
| Approach: | They propose a translation model in Vietnamese that trains a pretrained Encoder-Decoder Transformer model on 20 million translated abstracts. |
| Outcome: | The proposed model can translate and produce both pretrained and supervised biomedical data in two biomedically important domains. |
Probing Pre-Trained Language Models for Disease Knowledge (2021.findings-acl)
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| Challenge: | Pre-trained language models perform medical reasoning tasks, but standard benchmarks lack examples that require such forms of reasoning. |
| Approach: | They propose a medical reasoning benchmark that uses pre-trained language models to analyze medical reasoning in the biomedical domain. |
| Outcome: | The proposed benchmarks are based on pre-trained language models that perform medical reasoning tasks. |
Dissecting Clinical Reasoning in Natural Language Inference for Large Language Models (2026.findings-acl)
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| Challenge: | Recent studies on large language models (LLMs) have demonstrated the impact of prompting strategies and fine-tuning techniques on their reasoning capabilities. |
| Approach: | They examine four classes of prompting strategies to elicit reasoning in large language models . they then construct demonstrations using a frontier model to distil multi-step reasoning capabilities into smaller models based on Low-Rank Adaptation (LoRA). |
| Outcome: | The proposed model improves in 75% of the models on MedNLI and TREC Clinical Trials. |