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Found 9 papers, 0 papers with code
Generating summaries tailored to target characteristics
Recently, research efforts have gained pace to cater to varied user preferences while generating text summaries.
Exploring Neural Models for Parsing Natural Language into First-Order Logic
We model FOL parsing as a sequence to sequence mapping task where given a natural language sentence, it is encoded into an intermediate representation using an LSTM followed by a decoder which sequentially generates the predicates in the corresponding FOL formula.
Incorporating Stylistic Lexical Preferences in Generative Language Models
While recent advances in language modeling have resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style.
DRAG: Director-Generator Language Modelling Framework for Non-Parallel Author Stylized Rewriting
In this paper, we propose a Director-Generator framework to rewrite content in the target author's style, specifically focusing on certain target attributes.
MIMOQA: Multimodal Input Multimodal Output Question Answering
In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.
SemIE: Semantically-aware Image Extrapolation
The, thus, obtained segmentation map is fed into a network to compute the extrapolated semantic segmentation and the corresponding panoptic segmentation maps.
Distilling Large Language Models for Matching Patients to Clinical Trials
To address these issues, this study presents the first systematic examination of the efficacy of both proprietary (GPT-3. 5, and GPT-4) and open-source LLMs (LLAMA 7B, 13B, and 70B) for the task of patient-trial matching.
Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology
Retrieving information from EHR systems is essential for answering specific questions about patient journeys and improving the delivery of clinical care.
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering
Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image.
