Search Results for author:
Found 35 papers, 23 papers with code
Minimally-Supervised Relation Induction from Pre-trained Language Model
In this paper, we propose a novel method to induce relation with BERT under the minimally-supervised setting.
De-Bias for Generative Extraction in Unified NER Task
Named entity recognition (NER) is a fundamental task to recognize specific types of entities from a given sentence.
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Compared to its counterparts, our video-centric textbook offers more coherent context, richer knowledge, and better image-text alignment.
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation
Diffusion-based text-to-image (T2I) models have demonstrated remarkable results in global video editing tasks.
GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
GaVaMoE introduces two key components: (1) a rating reconstruction module that employs Variational Autoencoder (VAE) with a Gaussian Mixture Model (GMM) to capture complex user-item collaborative preferences, serving as a pre-trained multi-gating mechanism; and (2) a set of fine-grained expert models coupled with the multi-gating mechanism for generating highly personalized explanations.
Entering Real Social World! Benchmarking the Social Intelligence of Large Language Models from a First-person Perspective
(3) a lack of comprehensive evaluation of behavioral intelligence, with specific emphasis on incorporating critical human-machine interaction scenarios.
Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
In light of this, we design a multi-modal self-instruct, utilizing large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily scenarios.
TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models' Theory-of-Mind
Theory of Mind (ToM)-the cognitive ability to reason about mental states of ourselves and others, is the foundation of social interaction.
Advancing Process Verification for Large Language Models via Tree-Based Preference Learning
For instance, Tree-PLV achieved substantial performance gains over the Mistral-7B self-consistency baseline on GSM8K (67. 55% to 82. 79%), MATH (17. 00% to 26. 80%), CSQA (68. 14% to 72. 97%), and StrategyQA (82. 86% to 83. 25%). Additionally, our study explores the appropriate granularity for applying preference learning, revealing that step-level guidance provides feedback that better aligns with the evaluation of the reasoning process.
Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization
Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks.
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction
EasyTool purifies essential information from extensive tool documentation of different sources, and elaborates a unified interface (i. e., tool instruction) to offer standardized tool descriptions and functionalities for LLM-based agents.
Self-Contrast: Better Reflection Through Inconsistent Solving Perspectives
Experiments conducted on a series of reasoning and translation tasks with different LLMs serve to underscore the effectiveness and generality of our strategy.
TaskBench: Benchmarking Large Language Models for Task Automation
To address this, we introduce TaskBench, a comprehensive framework to evaluate the capability of LLMs in task automation.
An Expression Tree Decoding Strategy for Mathematical Equation Generation
To generate a tree with expression as its node, we employ a layer-wise parallel decoding strategy: we decode multiple independent expressions (leaf nodes) in parallel at each layer and repeat parallel decoding layer by layer to sequentially generate these parent node expressions that depend on others.
Ranked #2 on
Math Word Problem Solving
on MathQA
MProto: Multi-Prototype Network with Denoised Optimal Transport for Distantly Supervised Named Entity Recognition
Distantly supervised named entity recognition (DS-NER) aims to locate entity mentions and classify their types with only knowledge bases or gazetteers and unlabeled corpus.
Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
The advancements are twofold: First, it is a code-centric agent that receives human requests and generates code as an intermediary to handle massive data, which is quite flexible for large-scale data processing tasks.
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks.
Ranked #1 on
Nested Named Entity Recognition
on ACE 2004
DiffusionNER: Boundary Diffusion for Named Entity Recognition
In this paper, we propose DiffusionNER, which formulates the named entity recognition task as a boundary-denoising diffusion process and thus generates named entities from noisy spans.
Ranked #2 on
Nested Named Entity Recognition
on GENIA
Insert or Attach: Taxonomy Completion via Box Embedding
Taxonomy completion, enriching existing taxonomies by inserting new concepts as parents or attaching them as children, has gained significant interest.
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence.
Automatic Machine Learning Model Selection
Model Selection
+2
Query-based Instance Discrimination Network for Relational Triple Extraction
Joint entity and relation extraction has been a core task in the field of information extraction.
Contrastive Learning
Joint Entity and Relation Extraction
+1
Multi-View Reasoning: Consistent Contrastive Learning for Math Word Problem
Math word problem solver requires both precise relation reasoning about quantities in the text and reliable generation for the diverse equation.
Ranked #2 on
Math Word Problem Solving
on Math23K
(using extra training data)
Molecular Substructure-Aware Network for Drug-Drug Interaction Prediction
Concomitant administration of drugs can cause drug-drug interactions (DDIs).
Prompting to Distill: Boosting Data-Free Knowledge Distillation via Reinforced Prompt
Data-free knowledge distillation (DFKD) conducts knowledge distillation via eliminating the dependence of original training data, and has recently achieved impressive results in accelerating pre-trained language models.
Propose-and-Refine: A Two-Stage Set Prediction Network for Nested Named Entity Recognition
In the refine stage, proposals interact with each other, and richer contextual information is incorporated into the proposal representations.
Parallel Instance Query Network for Named Entity Recognition
Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel.
Ranked #1 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+5
Towards Data-Efficient Detection Transformers
Besides, we introduce a simple yet effective label augmentation method to provide richer supervision and improve data efficiency.
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition
Named Entity Recognition
+1
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data
Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring.
A Sequence-to-Set Network for Nested Named Entity Recognition
We utilize a non-autoregressive decoder to predict the final set of entities in one pass, in which we are able to capture dependencies between entities.
Ranked #6 on
Nested Named Entity Recognition
on ACE 2005
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition.
Ranked #6 on
Nested Named Entity Recognition
on GENIA
Chinese Named Entity Recognition
named-entity-recognition
+3
A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction
Joint entity and relation extraction framework constructs a unified model to perform entity recognition and relation extraction simultaneously, which can exploit the dependency between the two tasks to mitigate the error propagation problem suffered by the pipeline model.
Ranked #1 on
Relation Extraction
on CoNLL04
(NER Micro F1 metric)
Joint Entity and Relation Extraction
Reading Comprehension
+2
SynET: Synonym Expansion using Transitivity
Extensive experiments on a real-world dataset show the effectiveness of our approach.
Adversarial Self-Supervised Data-Free Distillation for Text Classification
To the best of our knowledge, our framework is the first data-free distillation framework designed for NLP tasks.
Multi-hop Reading Comprehension across Documents with Path-based Graph Convolutional Network
Meanwhile, we propose Gated-RGCN to accumulate evidence on the path-based reasoning graph, which contains a new question-aware gating mechanism to regulate the usefulness of information propagating across documents and add question information during reasoning.
