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Found 32 papers, 24 papers with code
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Our experiments show that Transfusion scales significantly better than quantizing images and training a language model over discrete image tokens.
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy.
Instruction-tuned Language Models are Better Knowledge Learners
The standard recipe for doing so involves continued pre-training on new documents followed by instruction-tuning on question-answer (QA) pairs.
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Specifically, an adversarial LLM and a target LLM interplay with each other in an iterative manner, where the adversarial LLM aims to generate challenging prompts that elicit unsafe responses from the target LLM, while the target LLM is fine-tuned with safety aligned data on these adversarial prompts.
In-context Pretraining: Language Modeling Beyond Document Boundaries
Large language models (LMs) are currently trained to predict tokens given document prefixes, enabling them to directly perform long-form generation and prompting-style tasks which can be reduced to document completion.
Self-Alignment with Instruction Backtranslation
We present a scalable method to build a high quality instruction following language model by automatically labelling human-written text with corresponding instructions.
FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios
With the above challenges in mind, in this paper, we propose FacTool, a task and domain agnostic framework for detecting factual errors of texts generated by large language models (e. g., ChatGPT).
Multi-Dimensional Evaluation of Text Summarization with In-Context Learning
Most frameworks that perform such multi-dimensional evaluation require training on large manually or synthetically generated datasets.
Look-back Decoding for Open-Ended Text Generation
Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from undesired repetitions.
LIMA: Less Is More for Alignment
Large language models are trained in two stages: (1) unsupervised pretraining from raw text, to learn general-purpose representations, and (2) large scale instruction tuning and reinforcement learning, to better align to end tasks and user preferences.
Training Trajectories of Language Models Across Scales
Why do larger language models demonstrate more desirable behaviors?
In-context Examples Selection for Machine Translation
Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model.
Mega: Moving Average Equipped Gated Attention
The design choices in the Transformer attention mechanism, including weak inductive bias and quadratic computational complexity, have limited its application for modeling long sequences.
Ranked #4 on
Machine Translation
on WMT2014 German-English
Prompt Consistency for Zero-Shot Task Generalization
One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting.
Towards a Unified View of Parameter-Efficient Transfer Learning
Furthermore, our unified framework enables the transfer of design elements across different approaches, and as a result we are able to instantiate new parameter-efficient fine-tuning methods that tune less parameters than previous methods while being more effective, achieving comparable results to fine-tuning all parameters on all four tasks.
Distributionally Robust Multilingual Machine Translation
Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models.
Examining and Combating Spurious Features under Distribution Shift
Group distributionally robust optimization (DRO) provides an effective tool to alleviate covariate shift by minimizing the worst-case training loss over a set of pre-defined groups.
Luna: Linear Unified Nested Attention
Specifically, with the first attention function, Luna packs the input sequence into a sequence of fixed length.
Learning Structures for Deep Neural Networks
We further establish connections between this principle and the theory of Bayesian optimal classification, and empirically verify that larger entropy of the outputs of a deep neural network indeed corresponds to a better classification accuracy.
Detecting Hallucinated Content in Conditional Neural Sequence Generation
Neural sequence models can generate highly fluent sentences, but recent studies have also shown that they are also prone to hallucinate additional content not supported by the input.
Understanding Knowledge Distillation in Non-autoregressive Machine Translation
We find that knowledge distillation can reduce the complexity of data sets and help NAT to model the variations in the output data.
FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow
Most sequence-to-sequence (seq2seq) models are autoregressive; they generate each token by conditioning on previously generated tokens.
Ranked #3 on
Machine Translation
on WMT2016 English-Romanian
Handling Syntactic Divergence in Low-resource Machine Translation
Despite impressive empirical successes of neural machine translation (NMT) on standard benchmarks, limited parallel data impedes the application of NMT models to many language pairs.
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages.
The ARIEL-CMU Systems for LoReHLT18
This paper describes the ARIEL-CMU submissions to the Low Resource Human Language Technologies (LoReHLT) 2018 evaluations for the tasks Machine Translation (MT), Entity Discovery and Linking (EDL), and detection of Situation Frames in Text and Speech (SF Text and Speech).
MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders
Variational Autoencoder (VAE), a simple and effective deep generative model, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations.
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging.
StructVAE: Tree-structured Latent Variable Models for Semi-supervised Semantic Parsing
Semantic parsing is the task of transducing natural language (NL) utterances into formal meaning representations (MRs), commonly represented as tree structures.
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Labeled sequence transduction is a task of transforming one sequence into another sequence that satisfies desiderata specified by a set of labels.
A C-LSTM Neural Network for Text Classification
In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification.
Ranked #10 on
Text Classification
on TREC-6
Category Enhanced Word Embedding
In this paper, we incorporate category information of documents in the learning of word representations and to learn the proposed models in a document-wise manner.
