Search Results for author:
Found 28 papers, 12 papers with code
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
In-Context Learning State Vector with Inner and Momentum Optimization
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
Improving Attributed Text Generation of Large Language Models via Preference Learning
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.
SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection
In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.
Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer
The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context.
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training
Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI.
Temporal Knowledge Question Answering via Abstract Reasoning Induction
In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties.
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks.
A Survey of Large Language Models Attribution
Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval
Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables.
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks.
SeDR: Segment Representation Learning for Long Documents Dense Retrieval
In SeDR, Segment-Interaction Transformer is proposed to encode long documents into document-aware and segment-sensitive representations, while it holds the complexity of splitting-and-pooling and outperforms other segment-interaction patterns on DR.
Prompt-based Text Entailment for Low-Resource Named Entity Recognition
To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs.
Low Resource Named Entity Recognition
named-entity-recognition
+3
Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates
We conduct extensive experiments on six datasets with two popular pre-trained language models in the in-domain and out-of-domain settings.
A Survey on Table Question Answering: Recent Advances
Table Question Answering (Table QA) refers to providing precise answers from tables to answer a user's question.
MSDF: A General Open-Domain Multi-Skill Dialog Framework
In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e. g. knowledge grounded dialog and persona based dialog).
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation
Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
You Can Do Better! If You Elaborate the Reason When Making Prediction
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.
Sharp pointwise-in-time error estimate of L1 scheme for nonlinear subdiffusion equations
An essential feature of the subdiffusion equations with the $\alpha$-order time fractional derivative is the weak singularity at the initial time.
Numerical Analysis Numerical Analysis 65M06, 65M12, 65M15, 35R11 F.2.2
A t-SNE Based Classification Approach to Compositional Microbiome Data
This study presented a t-SNE based classification approach for compositional microbiome data, which enabled us to build classifiers and classify new samples in the reduced dimensional space produced by t-SNE.
Integrating Coarse Granularity Part-level Features with Supervised Global-level Features for Person Re-identification
On one hand, CGPN learns to extract effective body part features for both holistic and partial person images.
Semi-supervised Visual Feature Integration for Pre-trained Language Models
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference
Natural Language Understanding
+2
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
BERTSel: Answer Selection with Pre-trained Models
we are the first to explore the performance of fine-tuning BERT for answer selection.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
