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Found 21 papers, 10 papers with code
YuE: Scaling Open Foundation Models for Long-Form Music Generation
We tackle the task of long-form music generation--particularly the challenging \textbf{lyrics-to-song} problem--by introducing YuE, a family of open foundation models based on the LLaMA2 architecture.
SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines
To address this gap, we present SuperGPQA, a comprehensive benchmark that evaluates graduate-level knowledge and reasoning capabilities across 285 disciplines.
Distillation Quantification for Large Language Models
Model distillation is a technique for transferring knowledge from large language models (LLMs) to smaller ones, aiming to create resource-efficient yet high-performing models.
Aligning Instruction Tuning with Pre-training
Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior.
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation
Leveraging knowledge graphs (KGs) as external knowledge sources has emerged as a viable solution, but existing methods for LLM-based knowledge graph question answering (KGQA) are often limited by step-by-step decision-making on KGs, restricting the global planning and reasoning capabilities of LLMs, or they require fine-tuning or pre-training on specific KGs.
OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision
This is due to the application of simple filtering methods like CLIP-score.
Can MLLMs Understand the Deep Implication Behind Chinese Images?
To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images.
A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i. e., math, coding, commonsense reasoning).
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Recent advancements in Large Language Models (LLMs) have markedly enhanced the interpretation and processing of tabular data, introducing previously unimaginable capabilities.
I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm
Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment.
GIEBench: Towards Holistic Evaluation of Group Identity-based Empathy for Large Language Models
As large language models (LLMs) continue to develop and gain widespread application, the ability of LLMs to exhibit empathy towards diverse group identities and understand their perspectives is increasingly recognized as critical.
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks.
MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series
To improve the transparency of LLMs, the research community has formed to open-source truly open LLMs (e. g., Pythia, Amber, OLMo), where more details (e. g., pre-training corpus and training code) are being provided.
MuPT: A Generative Symbolic Music Pretrained Transformer
In this paper, we explore the application of Large Language Models (LLMs) to the pre-training of music.
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs.
The Fine Line: Navigating Large Language Model Pretraining with Down-streaming Capability Analysis
To address the aforementioned limitations, this paper undertakes a comprehensive comparison of model capabilities at various pretraining intermediate checkpoints.
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs).
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding
Utilizing this dataset, we train a series of models, referred to as StructLM, based on the Mistral and the CodeLlama model family, ranging from 7B to 34B parameters.
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation
To verify the effectiveness of our method, we propose I2V-Bench, a comprehensive evaluation benchmark for I2V generation.
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.
