Search Results for author: Dan Iter

Found 16 papers, 4 papers with code

Entity Attribute Relation Extraction with Attribute-Aware Embeddings

no code implementations EMNLP (DeeLIO) 2020 Dan Iter, Xiao Yu, Fangtao Li

Entity-attribute relations are a fundamental component for building large-scale knowledge bases, which are widely employed in modern search engines.

Attribute Extraction Relation Extraction

In-Context Demonstration Selection with Cross Entropy Difference

no code implementations24 May 2023 Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu

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.

Language Modelling Text Generation

LMGQS: A Large-scale Dataset for Query-focused Summarization

no code implementations22 May 2023 Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Chenguang Zhu, Michael Zeng

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.

Language Modelling

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT

no code implementations22 May 2023 Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng

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

no code implementations4 May 2023 Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng

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

no code implementations29 Mar 2023 Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu

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.

Dialogue Generation Text Summarization

How Does In-Context Learning Help Prompt Tuning?

no code implementations22 Feb 2023 Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer

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.

Text Generation

Generate rather than Retrieve: Large Language Models are Strong Context Generators

1 code implementation21 Sep 2022 Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang

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.

Language Modelling Open-Domain Question Answering

Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference

1 code implementation EMNLP 2021 William Held, Dan Iter, Dan Jurafsky

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.

coreference-resolution Coreference Resolution +3

On the Complementarity of Data Selection and Fine Tuning for Domain Adaptation

no code implementations15 Sep 2021 Dan Iter, David Grangier

Domain adaptation of neural networks commonly relies on three training phases: pretraining, selected data training and then fine tuning.

Domain Generalization Language Modelling +2

Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models

1 code implementation ACL 2020 Dan Iter, Kelvin Guu, Larry Lansing, Dan Jurafsky

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.

Common Sense Reasoning Natural Language Inference +3

FrameIt: Ontology Discovery for Noisy User-Generated Text

no code implementations WS 2018 Dan Iter, Alon Halevy, Wang-Chiew Tan

A common need of NLP applications is to extract structured data from text corpora in order to perform analytics or trigger an appropriate action.

Active Learning Semantic Role Labeling

Automatic Detection of Incoherent Speech for Diagnosing Schizophrenia

no code implementations WS 2018 Dan Iter, Jong Yoon, Dan Jurafsky

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.

Sentence Embedding Sentence-Embedding +1

Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data

no code implementations25 Oct 2016 Paroma Varma, Bryan He, Dan Iter, Peng Xu, Rose Yu, Christopher De Sa, Christopher Ré

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.

Relation Extraction

Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

1 code implementation14 Jun 2016 Stefan Hadjis, Ce Zhang, Ioannis Mitliagkas, Dan Iter, Christopher Ré

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.

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