Search Results for author:
Found 29 papers, 17 papers with code
Spatial Hierarchy Aware Residual Pyramid Network for Time-of-Flight Depth Denoising
In this paper, we propose a Spatial Hierarchy Aware Residual Pyramid Network, called SHARP-Net, to remove the depth noise by fully exploiting the geometry information of the scene on different scales.
Noise-BERT: A Unified Perturbation-Robust Framework with Noise Alignment Pre-training for Noisy Slot Filling Task
In this study, we address the challenges posed by input perturbations in slot filling by proposing Noise-BERT, a unified Perturbation-Robust Framework with Noise Alignment Pre-training.
PreAct: Predicting Future in ReAct Enhances Agent's Planning Ability
Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in the correct direction.
Multi-Perspective Consistency Enhances Confidence Estimation in Large Language Models
In this work, we focus on improving the confidence estimation of large language models.
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
Knowledge Editing on Black-box Large Language Models
Knowledge editing (KE) aims to efficiently and precisely modify the behavior of large language models (LLMs) to update specific knowledge without negatively influencing other knowledge.
OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPT
The tasks of out-of-domain (OOD) intent discovery and generalized intent discovery (GID) aim to extend a closed intent classifier to open-world intent sets, which is crucial to task-oriented dialogue (TOD) systems.
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction
Most current Event Extraction (EE) methods focus on the high-resource scenario, which requires a large amount of annotated data and can hardly be applied to low-resource domains.
Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction
Although previous methods attempt to address these challenges, they overlook the interference of event-unrelated sentences during event detection and neglect the mutual interference of different event roles during argument extraction.
DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task
Recently, prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks.
Semantic Parsing by Large Language Models for Intricate Updating Strategies of Zero-Shot Dialogue State Tracking
Zero-shot Dialogue State Tracking (DST) addresses the challenge of acquiring and annotating task-oriented dialogues, which can be time-consuming and costly.
ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models
Knowledge Base Question Answering (KBQA) aims to derive answers to natural language questions over large-scale knowledge bases (KBs), which are generally divided into two research components: knowledge retrieval and semantic parsing.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestionsSP
Revisit Input Perturbation Problems for LLMs: A Unified Robustness Evaluation Framework for Noisy Slot Filling Task
To address these challenges, we propose a unified robustness evaluation framework based on the slot-filling task to systematically evaluate the dialogue understanding capability of LLMs in diverse input perturbation scenarios.
Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization
In this paper, we conduct an investigation for such data augmentation in math reasoning and are intended to answer: (1) What strategies of data augmentation are more effective; (2) What is the scaling relationship between the amount of augmented data and model performance; and (3) Can data augmentation incentivize generalization to out-of-domain mathematical reasoning tasks?
Ranked #50 on
Math Word Problem Solving
on MATH
(using extra training data)
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition
We propose four intriguing research questions to explore the association between model performance and various factors including data amount, composition ratio, model size and SFT strategies.
Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations
The proposed dataset contains five types of human-annotated noise, and all those noises are exactly existed in real extensive robust-training methods of slot filling into the proposed framework.
InstructERC: Reforming Emotion Recognition in Conversation with a Retrieval Multi-task LLMs Framework
The field of emotion recognition of conversation (ERC) has been focusing on separating sentence feature encoding and context modeling, lacking exploration in generative paradigms based on unified designs.
Ranked #2 on
Emotion Recognition in Conversation
on MELD
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement
Our model achieves state-of-the-art performance on three datasets, demonstrating the superiority of our work.
Bridging the KB-Text Gap: Leveraging Structured Knowledge-aware Pre-training for KBQA
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with factual information such as entities and relations in KBs.
Ranked #2 on
Knowledge Base Question Answering
on WebQuestionsSP
A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER
The objective of few-shot named entity recognition is to identify named entities with limited labeled instances.
Ranked #1 on
Few-shot NER
on Few-NERD (INTER)
Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
We find with augmented samples containing more distinct reasoning paths, RFT improves mathematical reasoning performance more for LLMs.
Ranked #100 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Generative Zero-Shot Prompt Learning for Cross-Domain Slot Filling with Inverse Prompting
Zero-shot cross-domain slot filling aims to transfer knowledge from the labeled source domain to the unlabeled target domain.
Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
A Robust Contrastive Alignment Method For Multi-Domain Text Classification
In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning.
Exploiting Rigidity Constraints for LiDAR Scene Flow Estimation
Previous LiDAR scene flow estimation methods, especially recurrent neural networks, usually suffer from structure distortion in challenging cases, such as sparse reflection and motion occlusions.
