Search Results for author:
Found 16 papers, 4 papers with code
Entity Attribute Relation Extraction with Attribute-Aware Embeddings
Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines.
In-Context Demonstration Selection with Cross Entropy Difference
Our method is based on the observation that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example by a language model that was finetuned on that demonstration.
LMGQS: A Large-scale Dataset for Query-focused Summarization
We hypothesize that there is a hidden query for each summary sentence in a generic summarization annotation, and we utilize a large-scale pretrained language model to recover it.
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utilization in various applications.
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort.
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.
How Does In-Context Learning Help Prompt Tuning?
This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demonstrations of the task are provided to the model in natural language without any additional training.
Generate rather than Retrieve: Large Language Models are Strong Context Generators
We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
We model the entities/events in a reader's focus as a neighborhood within a learned latent embedding space which minimizes the distance between mentions and the centroids of their gold coreference clusters.
Ranked #1 on
Event Coreference Resolution
on Gun Violence Corpus
The Trade-offs of Domain Adaptation for Neural Language Models
This work connects language model adaptation with concepts of machine learning theory.
On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation
Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning.
Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
Recent models for unsupervised representation learning of text have employed a number of techniques to improve contextual word representations but have put little focus on discourse-level representations.
FrameIt: Ontology Discovery for Noisy User-Generated Text
A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action.
Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia
Here, we present the first benchmark comparison of previously proposed coherence models for detecting symptoms of schizophrenia and evaluate their performance on a new dataset of recorded interviews between subjects and clinicians.
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data
Prior work has explored learning accuracies for these sources even without ground truth labels, but they assume that a single accuracy parameter is sufficient to model the behavior of these sources over the entire training set.
Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs
Given a specification of a convolutional neural network, our goal is to minimize the time to train this model on a cluster of commodity CPUs and GPUs.
