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Found 50 papers, 28 papers with code
Maximal Clique Based Non-Autoregressive Open Information Extraction
However, the popular OpenIE systems usually output facts sequentially in the way of predicting the next fact conditioned on the previous decoded ones, which enforce an unnecessary order on the facts and involve the error accumulation between autoregressive steps.
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph
Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e. g., word and sentence information.
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits.
SoFA: Shielded On-the-fly Alignment via Priority Rule Following
The alignment problem in Large Language Models (LLMs) involves adapting them to the broad spectrum of human values.
Self-Retrieval: Building an Information Retrieval System with One Large Language Model
The rise of large language models (LLMs) has transformed the role of information retrieval (IR) systems in the way to humans accessing information.
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
Nevertheless, we posit that LLMs inherently harbor role-play capabilities, owing to the extensive knowledge of characters and potential dialogues ingrained in their vast training corpora.
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Then, we use DARE as a versatile plug-and-play technique to sparsify delta parameters of multiple SFT homologous models for mitigating parameter interference and merge them into a single model by parameter fusing.
Diversify Question Generation with Retrieval-Augmented Style Transfer
Given a textual passage and an answer, humans are able to ask questions with various expressions, but this ability is still challenging for most question generation (QG) systems.
Improving Question Generation with Multi-level Content Planning
Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context.
Quantifying and mitigating the impact of label errors on model disparity metrics
We empirically assess the proposed approach on a variety of datasets and find significant improvement, compared to alternative approaches, in identifying training inputs that improve a model's disparity metric.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
Ranked #3 on
Multi-Label Text Classification
on CC3M-TagMask
A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment
Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences.
Wider and Deeper LLM Networks are Fairer LLM Evaluators
Each perspective corresponds to the role of a specific LLM neuron in the first layer.
PolyLM: An Open Source Polyglot Large Language Model
Large language models (LLMs) demonstrate remarkable ability to comprehend, reason, and generate following nature language instructions.
Preference Ranking Optimization for Human Alignment
In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses.
Unified Language Representation for Question Answering over Text, Tables, and Images
When trying to answer complex questions, people often rely on multiple sources of information, such as visual, textual, and tabular data.
Causal Document-Grounded Dialogue Pre-training
To tackle this issue, we are the first to present a causally-complete dataset construction strategy for building million-level DocGD pre-training corpora.
Domain Incremental Lifelong Learning in an Open World
Specifically, we dedicate task-level prompts to capture task-specific knowledge to retain high LL performances and maintain instance-level prompts to learn knowledge shared across input samples to improve the model's generalization performance.
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Graph contrastive learning (GCL) has recently achieved substantial advancements.
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
(2) How can we enhance LLMs' ability to utilize tools?
Towards Generalized Open Information Extraction
Open Information Extraction (OpenIE) facilitates the open-domain discovery of textual facts.
Semi-Supervised Lifelong Language Learning
In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data.
Prompt Conditioned VAE: Enhancing Generative Replay for Lifelong Learning in Task-Oriented Dialogue
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration
In this paper, we propose a Layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents (VRDs), so as to generate accurate responses in dialogue systems.
Relation-Guided Few-Shot Relational Triple Extraction
To instantiate this strategy, we further propose a model, RelATE, which builds a dual-level attention to aggregate relationrelevant information to detect the relation occurrence and utilizes the annotated samples of the detected relations to extract the corresponding head/tail entities.
A Survey on Neural Open Information Extraction: Current Status and Future Directions
Open Information Extraction (OpenIE) facilitates domain-independent discovery of relational facts from large corpora.
Ranked #1 on
Open Information Extraction
on CaRB
Natural Language Understanding
Open-Domain Question Answering
+1
Profiling and Evolution of Intellectual Property
In recent years, with the rapid growth of Internet data, the number and types of scientific and technological resources are also rapidly expanding.
Web Page Content Extraction Based on Multi-feature Fusion
With the rapid growth of the number and types of web resources, there are still problems to be solved when using a single strategy to extract the text information of different pages.
Document-Level Event Extraction via Human-Like Reading Process
Meanwhile, rough reading is explored in a multi-round manner to discover undetected events, thus the multi-events problem is handled.
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning
Distantly supervised named entity recognition (DS-NER) efficiently reduces labor costs but meanwhile intrinsically suffers from the label noise due to the strong assumption of distant supervision.
CasEE: A Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction
Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts.
Discontinuous Named Entity Recognition as Maximal Clique Discovery
Named entity recognition (NER) remains challenging when entity mentions can be discontinuous.
FITAnnotator: A Flexible and Intelligent Text Annotation System
In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation.
Few-Shot Event Detection with Prototypical Amortized Conditional Random Field
Event detection tends to struggle when it needs to recognize novel event types with a few samples.
Label Enhanced Event Detection with Heterogeneous Graph Attention Networks
A heterogeneous graph attention networks is then introduced to propagate relational message and enrich information interaction.
Learning to Prune Dependency Trees with Rethinking for Neural Relation Extraction
Dependency trees have been shown to be effective in capturing long-range relations between target entities.
Porous Lattice Transformer Encoder for Chinese NER
Incorporating lexicons into character-level Chinese NER by lattices is proven effective to exploitrich word boundary information.
Document-level Relation Extraction with Dual-tier Heterogeneous Graph
Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking
To mitigate the issue, we propose in this paper a one-stage joint extraction model, namely, TPLinker, which is capable of discovering overlapping relations sharing one or both entities while immune from the exposure bias.
Ranked #2 on
Relation Extraction
on NYT11-HRL
Coarse-to-Fine Pre-training for Named Entity Recognition
More recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation
Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks.
Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems
On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions.
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention
Most Chinese pre-trained models take character as the basic unit and learn representation according to character's external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese.
Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model.
Ranked #1 on
Relation Extraction
on NYT-single
Beyond Word Attention: Using Segment Attention in Neural Relation Extraction
Relation extraction studies the issue of predicting semantic relations between pairs of entities in sentences.
Ranked #29 on
Relation Extraction
on TACRED
FlowSense: A Natural Language Interface for Visual Data Exploration within a Dataflow System
Dataflow visualization systems enable flexible visual data exploration by allowing the user to construct a dataflow diagram that composes query and visualization modules to specify system functionality.
Visus: An Interactive System for Automatic Machine Learning Model Building and Curation
In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems.
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Although information workers may complain about meetings, they are an essential part of their work life.
