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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">This is an announcement of Aswathy Ajith's MS Presentation</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">===============================================</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Candidate: Aswathy Ajith</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Date: Tuesday, May 28, 2024</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Time: 1 pm CT</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Location: JCL 390</span>
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<div class=""><font face="Calibri, Helvetica, sans-serif" class=""><span style="font-size: 14.666666984558105px;" class="">Remote Location: </span></font><a href="https://uchicago.zoom.us/j/5563454112?pwd=YjFEUDBBWTZYLzM4OXpRUXlBVitwdz09" class="">https://uchicago.zoom.us/j/5563454112?pwd=YjFEUDBBWTZYLzM4OXpRUXlBVitwdz09</a></div>
<div class="">Meeting ID: 556 345 4112<br class="">
Passcode: 436816<br class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Title: Domain Adaptability of Language Models and Scientific Information Extraction</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Abstract: "The recent surge in interest in large language models and their general language capabilities calls into question the effectiveness of adapting them
to tasks that require scientific know-how. Transformer- based masked language models such as BERT, trained on general corpora, have shown impressive performance on downstream tasks. It has also been demonstrated that the downstream task performance of such
models can be improved by pretraining larger models for longer on more data. In this work, we empirically evaluate the extent to which these results extend to tasks in science. We use 14 domain-specific transformer-based models (including ScholarBERT, a new
770M-parameter science-focused masked language model pretrained on up to 225B tokens) to evaluate the impact of training data, model size, pretraining and finetuning time on 12 downstream scientific tasks. Interestingly, we find that increasing model size,
training data, or compute time does not always lead to significant improvements (i.e., > 1% F1), if any, in scientific information extraction tasks. We offer plausible explanations for this surprising result. </span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">We also explore the extractive capabilities of generative models and introduce MatPropXtractor, an extraction system that uses GPT-3 to extract materials and
their properties from materials science literature without fine-tuning. MatPropXtractor consists of a three-step pipeline that includes 1) a document selection tool to identify related articles; 2) a paragraph classifier to identify passages containing important
materials properties; and 3) a property extractor exploiting in-context learning in GPT-3. We evaluate MatPropXtractor by applying it to five materials science papers. It can extract 154 material-property pairs from these papers and upon further evaluation
by a domain expert, it was found to have an average precision of 72.73% to identify paragraphs that contain information of interest and an average precision of 56.7% on material-property identification."</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Advisors: Ian Foster and Kyle Chard</span><br style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">
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<span style="font-family: Calibri, Helvetica, sans-serif; font-size: 14.666666984558105px;" class="">Committee Members: Ian Foster, Kyle Chard, and Eamon Duede</span><br class="">
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