Search Results for author:
Found 14 papers, 8 papers with code
MS-Mentions: Consistently Annotating Entity Mentions in Materials Science Procedural Text
Material science synthesis procedures are a promising domain for scientific NLP, as proper modeling of these recipes could provide insight into new ways of creating materials.
PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication.
Large Language Model Augmented Narrative Driven Recommendations
Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest while describing their likes/dislikes and travel circumstances.
LaMP: When Large Language Models Meet Personalization
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -- a novel benchmark for training and evaluating language models for producing personalized outputs.
Editable User Profiles for Controllable Text Recommendation
Finally, we implement LACE in an interactive controllable recommender system and conduct a user study to demonstrate that users are able to improve the quality of recommendations they receive through interactions with an editable user profile.
How Data Scientists Review the Scholarly Literature
Keeping up with the research literature plays an important role in the workflow of scientists - allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline.
Augmenting Scientific Creativity with Retrieval across Knowledge Domains
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas.
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity
We present a new scientific document similarity model based on matching fine-grained aspects of texts.
CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example
Query by Example is a well-known information retrieval task in which a document is chosen by the user as the search query and the goal is to retrieve relevant documents from a large collection.
An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text
Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric.
Roll Call Vote Prediction with Knowledge Augmented Models
The sources of knowledge we use are news text and Freebase, a manually curated knowledge base.
The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structures
Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text.
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery.
Automatically Extracting Action Graphs from Materials Science Synthesis Procedures
In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds.
