Search Results for author:
Found 7 papers, 5 papers with code
MARG: Multi-Agent Review Generation for Scientific Papers
We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion.
ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews
Revising scientific papers based on peer feedback is a challenging task that requires not only deep scientific knowledge and reasoning, but also the ability to recognize the implicit requests in high-level feedback and to choose the best of many possible ways to update the manuscript in response.
SciRepEval: A Multi-Format Benchmark for Scientific Document Representations
In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations.
Embedding Recycling for Language Models
Real-world applications of neural language models often involve running many different models over the same corpus.
Limitations of Active Learning With Deep Transformer Language Models
Active Learning (AL) has the potential to reduce labeling cost when training natural language processing models, but its effectiveness with the large pretrained transformer language models that power today's NLP is uncertain.
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems.
Ranked #1 on
Common Sense Reasoning
on CODAH
(using extra training data)
DeepMoTIon: Learning to Navigate Like Humans
We present a novel human-aware navigation approach, where the robot learns to mimic humans to navigate safely in crowds.
