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Found 29 papers, 17 papers with code
AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
We propose AutoCrawler, a two-stage framework that leverages the hierarchical structure of HTML for progressive understanding.
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction
The framework includes an evaluation model that can extract related entity pairs with high precision.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset.
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases
Definition bias is a negative phenomenon that can mislead models.
m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers
Leveraging the knowledge from monolithic models, using techniques such as knowledge distillation, is likely to facilitate the training of modular models and enable them to integrate knowledge from multiple models pretrained on diverse sources.
ChatMusician: Understanding and Generating Music Intrinsically with LLM
It is based on continual pre-training and finetuning LLaMA2 on a text-compatible music representation, ABC notation, and the music is treated as a second language.
LLM Agents for Psychology: A Study on Gamified Assessments
Psychological measurement is essential for mental health, self-understanding, and personal development.
GenDec: A robust generative Question-decomposition method for Multi-hop reasoning
Multi-hop QA (MHQA) involves step-by-step reasoning to answer complex questions and find multiple relevant supporting facts.
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval
We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.
CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark
We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation
In this paper, we introduce Kun, a novel approach for creating high-quality instruction-tuning datasets for large language models (LLMs) without relying on manual annotations.
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning.
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks
To quantitatively assess our metric, we evaluate its correlation with human ratings on 5 held-in datasets, 2 held-out datasets and show that TIGERScore can achieve the open-source SoTA correlation with human ratings across these datasets and almost approaches GPT-4 evaluator.
Can Large Language Models Understand Real-World Complex Instructions?
To bridge this gap, we propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response
Large Language Models (LLMs) have shown immense potential in multimodal applications, yet the convergence of textual and musical domains remains not well-explored.
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
The MAmmoTH models are trained on MathInstruct, our meticulously curated instruction tuning dataset.
LLaSM: Large Language and Speech Model
In this work, we propose Large Language and Speech Model (LLaSM).
Adaptive Ordered Information Extraction with Deep Reinforcement Learning
Information extraction (IE) has been studied extensively.
Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs).
MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training
Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored.
Chinese Open Instruction Generalist: A Preliminary Release
Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat. openai. com/}}.
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining
The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks.
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction
In our implementation, we adopt both the state-of-the-art molecule embedding models under the supervised learning paradigm and the pretraining paradigm as the molecule representation module of PEMP, respectively.
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification
CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.
Revisiting the Negative Data of Distantly Supervised Relation Extraction
Next, we formulate the problem of relation extraction into as a positive unlabeled learning task to alleviate false negative problem.
Ranked #1 on
Relation Extraction
on NYT11-HRL
Text Assisted Insight Ranking Using Context-Aware Memory Network
Extracting valuable facts or informative summaries from multi-dimensional tables, i. e. insight mining, is an important task in data analysis and business intelligence.
Learning Personalized End-to-End Goal-Oriented Dialog
Most existing works on dialog systems only consider conversation content while neglecting the personality of the user the bot is interacting with, which begets several unsolved issues.
Improved Knowledge Base Completion by Path-Augmented TransR Model
Knowledge base completion aims to infer new relations from existing information.
