Papers by Santiago Miret
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model (2023.findings-emnlp)
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| Challenge: | kNN-LM, REALM, DPR + FiD, Contriever + ATLAS, and Contriver + Flan-T5 are popular retriever-augmented language models for a variety of tasks. |
| Approach: | They evaluate the strengths and weaknesses of kNN-LM, REALM, DPR + FiD, Contriever + ATLAS, and Contriver + Flan-T5 in reasoning over retrieved statements across different tasks. |
| Outcome: | The proposed models do not exhibit strong reasoning even when provided with only the required statements. |
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling (2023.acl-long)
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| Challenge: | Using publicly available materials science text data, we construct a benchmark for evaluating the performance of natural language processing (NLP) models on materials science texts. |
| Approach: | They propose a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. |
| Outcome: | The proposed model outperforms BERT-based models on scientific text and a model pretrained on materials science journals. |
HoneyComb: A Flexible LLM-Based Agent System for Materials Science (2024.findings-emnlp)
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| Challenge: | specialized large language models (LLMs) have shown promise in materials science but often struggle with the distinct complexities of materials science tasks. |
| Approach: | They propose a new LLM-based agent system specifically designed for materials science that leverages a reliable materials science knowledge base and a sophisticated tool hub. |
| Outcome: | The proposed system outperforms baseline models across tasks in materials science while ensuring accuracy and relevance. |
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science (2023.findings-emnlp)
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| Challenge: | LLaMa-based language model for materials science is first of its kind in the world . |
| Approach: | They propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct) they then apply this process to finetune a LLaMa-based language model targeted for materials science. |
| Outcome: | The proposed model outperforms existing language models on materials science tasks and improves in successive stages of refinement. |