Search Results for author:
Found 43 papers, 15 papers with code
Unsupervised Dependency Graph Network
We introduce a new model, the Unsupervised Dependency Graph Network (UDGN), that can induce dependency structures from raw corpora and the masked language modeling task.
Phrase-aware Unsupervised Constituency Parsing
Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.
CoVLM: Composing Visual Entities and Relationships in Large Language Models Via Communicative Decoding
A communication token is generated by the LLM following a visual entity or a relation, to inform the detection network to propose regions that are relevant to the sentence generated so far.
Autonomous Tree-search Ability of Large Language Models
This approach yield a greater improvement compared to the ones fine-tuned on CoT data.
The Consensus Game: Language Model Generation via Equilibrium Search
When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discriminatively (by using them to score or rank a set of candidate outputs).
Sparse Universal Transformer
The Universal Transformer (UT) is a variant of the Transformer that shares parameters across its layers.
TextPSG: Panoptic Scene Graph Generation from Textual Descriptions
To tackle this problem, we propose a new framework TextPSG consisting of four modules, i. e., a region grouper, an entity grounder, a segment merger, and a label generator, with several novel techniques.
SALMON: Self-Alignment with Principle-Following Reward Models
Central to our approach is a principle-following reward model.
GraphText: Graph Reasoning in Text Space
Furthermore, GraphText paves the way for interactive graph reasoning, allowing both humans and LLMs to communicate with the model seamlessly using natural language.
Aligning Large Multimodal Models with Factually Augmented RLHF
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounded by the multimodal information in context.
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently.
ModuleFormer: Modularity Emerges from Mixture-of-Experts
In our experiment, we found that the modular architecture enables three important abilities for large pre-trained language models: 1) Efficiency, since ModuleFormer only activates a subset of its modules for each input token, thus it could achieve the same performance as dense LLMs with more than two times throughput; 2) Extendability, ModuleFormer is more immune to catastrophic forgetting than dense LLMs and can be easily extended with new modules to learn new knowledge that is not included in the training data; 3) Specialisation, finetuning ModuleFormer could specialize a subset of modules to the finetuning task and the task-unrelated modules could be easily pruned for a lightweight deployment.
Hyper-Decision Transformer for Efficient Online Policy Adaptation
To address this challenge, we propose a new framework, called Hyper-Decision Transformer (HDT), that can generalize to novel tasks from a handful of demonstrations in a data- and parameter-efficient manner.
Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
When looking at an image, we can decompose the scene into entities and their parts as well as obtain the dependencies between them.
Planning with Large Language Models for Code Generation
Existing large language model-based code generation pipelines typically use beam search or sampling algorithms during the decoding process.
Transformer-Patcher: One Mistake worth One Neuron
Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME).
See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning
The see stage scans the image and grounds the visual concept candidates with a visual perception model.
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
Mixture of Attention Heads: Selecting Attention Heads Per Token
This paper proposes the Mixture of Attention Heads (MoA), a new architecture that combines multi-head attention with the MoE mechanism.
Prompting Decision Transformer for Few-Shot Policy Generalization
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations.
Syntactic Inductive Biases for Deep Learning Methods
We propose two families of inductive biases, one for constituency structure and another one for dependency structure.
Self-Instantiated Recurrent Units with Dynamic Soft Recursion
On the other hand, the extent of the Self-IRU recursion is controlled by gates whose values are between 0 and 1 and may vary across the temporal dimension of sequences, enabling dynamic soft recursion depth at each time step.
Inducing Reusable Skills From Demonstrations with Option-Controller Network
With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.
Learning Task Decomposition with Ordered Memory Policy Network
The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.
Learning Task Decomposition with Order-Memory Policy Network
Many complex real-world tasks are composed of several levels of sub-tasks.
Long Range Arena : A Benchmark for Efficient Transformers
Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity.
Recurrently Controlling a Recurrent Network with Recurrent Networks Controlled by More Recurrent Networks
This paper explores an intriguing idea of recursively parameterizing recurrent nets.
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words.
Long Range Arena: A Benchmark for Efficient Transformers
In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models.
Ranked #19 on
Long-range modeling
on LRA
Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle
In the present work, we propose a new syntax-aware language model: Syntactic Ordered Memory (SOM).
Recursive Top-Down Production for Sentence Generation with Latent Trees
We model the recursive production property of context-free grammars for natural and synthetic languages.
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
It is commonly believed that knowledge of syntactic structure should improve language modeling.
Metagross: Meta Gated Recursive Controller Units for Sequence Modeling
This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit.
Ordered Memory
Inspired by Ordered Neurons (Shen et al., 2018), we introduce a new attention-based mechanism and use its cumulative probability to control the writing and erasing operation of the memory.
Investigating Biases in Textual Entailment Datasets
The ability to understand logical relationships between sentences is an important task in language understanding.
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed.
BanditSum: Extractive Summarization as a Contextual Bandit
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels.
Ranked #10 on
Extractive Text Summarization
on CNN / Daily Mail
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
In this work, we propose a novel constituency parsing scheme.
Generating Contradictory, Neutral, and Entailing Sentences
Learning distributed sentence representations remains an interesting problem in the field of Natural Language Processing (NLP).
Neural Language Modeling by Jointly Learning Syntax and Lexicon
In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.
Ranked #13 on
Constituency Grammar Induction
on PTB Diagnostic ECG Database
(Max F1 (WSJ) metric)
Self-organized Hierarchical Softmax
We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies.
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web.
