Search Results for author:
Found 34 papers, 24 papers with code
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
ChatShop: Interactive Information Seeking with Language Agents
The desire and ability to seek new information strategically are fundamental to human learning but often overlooked in current language agent evaluation.
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #73 on
Visual Question Answering
on MM-Vet
Seq2seq is All You Need for Coreference Resolution
Existing works on coreference resolution suggest that task-specific models are necessary to achieve state-of-the-art performance.
BM25 Query Augmentation Learned End-to-End
Given BM25's enduring competitiveness as an information retrieval baseline, we investigate to what extent it can be even further improved by augmenting and re-weighting its sparse query-vector representation.
Approximating CKY with Transformers
We investigate the ability of transformer models to approximate the CKY algorithm, using them to directly predict a sentence's parse and thus avoid the CKY algorithm's cubic dependence on sentence length.
CREATIVESUMM: Shared Task on Automatic Summarization for Creative Writing
We detail the process of curating these datasets for the task, as well as the metrics used for the evaluation of the submissions.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
On Generalization in Coreference Resolution
While coreference resolution is defined independently of dataset domain, most models for performing coreference resolution do not transfer well to unseen domains.
Ranked #1 on
Coreference Resolution
on Quizbowl
SummScreen: A Dataset for Abstractive Screenplay Summarization
Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics.
Chess as a Testbed for Language Model State Tracking
Motivated by this issue, we consider the task of language modeling for the game of chess.
Data-to-text Generation by Splicing Together Nearest Neighbors
We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs.
Learning Chess Blindfolded
Motivated by this issue, we consider the task of language modeling for the game of chess.
WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections
Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation.
Exemplar-Controllable Paraphrasing and Translation using Bitext
Our experimental results show that our models achieve competitive results on controlled paraphrase generation and strong performance on controlled machine translation.
Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
Long document coreference resolution remains a challenging task due to the large memory and runtime requirements of current models.
Ranked #10 on
Coreference Resolution
on CoNLL 2012
Discrete Latent Variable Representations for Low-Resource Text Classification
While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient.
ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model.
Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable.
Amortized Bethe Free Energy Minimization for Learning MRFs
We propose to learn deep undirected graphical models (i. e., MRFs) with a non-ELBO objective for which we can calculate exact gradients.
Label-Agnostic Sequence Labeling by Copying Nearest Neighbors
Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest.
Controllable Paraphrase Generation with a Syntactic Exemplar
Prior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori.
A Multi-Task Approach for Disentangling Syntax and Semantics in Sentence Representations
We propose a generative model for a sentence that uses two latent variables, with one intended to represent the syntax of the sentence and the other to represent its semantics.
Learning Deep Latent-variable MRFs with Amortized Bethe Free Energy Minimization
While much recent work has targeted learning deep discrete latent variable models with variational inference, this setting remains challenging, and it is often necessary to make use of potentially high-variance gradient estimators in optimizing the ELBO.
A Tutorial on Deep Latent Variable Models of Natural Language
There has been much recent, exciting work on combining the complementary strengths of latent variable models and deep learning.
Entity Tracking Improves Cloze-style Reading Comprehension
Reading comprehension tasks test the ability of models to process long-term context and remember salient information.
Learning Neural Templates for Text Generation
While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation.
Semi-Amortized Variational Autoencoders
Amortized variational inference (AVI) replaces instance-specific local inference with a global inference network.
Ranked #2 on
Text Generation
on Yahoo Questions
Challenges in Data-to-Document Generation
Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records.
Training Language Models Using Target-Propagation
While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making parallelization difficult) and from truncating gradient flow between distant time-steps.
Sequence-to-Sequence Learning as Beam-Search Optimization
In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores.
Ranked #13 on
Machine Translation
on IWSLT2015 German-English
Learning Global Features for Coreference Resolution
There is compelling evidence that coreference prediction would benefit from modeling global information about entity-clusters.
Ranked #26 on
Coreference Resolution
on OntoNotes
