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Found 60 papers, 28 papers with code
Aligning Generative Language Models with Human Values
Although current large-scale generative language models (LMs) can show impressive insights about factual knowledge, they do not exhibit similar success with respect to human values judgements (e. g., whether or not the generations of an LM are moral).
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.
CodeEditorBench: Evaluating Code Editing Capability of Large Language Models
Large Language Models (LLMs) for code are rapidly evolving, with code editing emerging as a critical capability.
Long-context LLMs Struggle with Long In-context Learning
Our study reveals that long context understanding and reasoning is still a challenging task for the existing 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
To bridge this gap, we introduce COIG-CQIA, a high-quality Chinese instruction tuning dataset.
DEEP-ICL: Definition-Enriched Experts for Language Model In-Context Learning
It has long been assumed that the sheer number of parameters in large language models (LLMs) drives in-context learning (ICL) capabilities, enabling remarkable performance improvements by leveraging task-specific demonstrations.
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 Code-LLaMA architecture, ranging from 7B to 34B parameters.
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.
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
However, open-source models often lack the execution capabilities and iterative refinement of advanced systems like the GPT-4 Code Interpreter.
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic Spaces
Drawing upon the intuition that aligning different modalities to the same semantic embedding space would allow models to understand states and actions more easily, we propose a new perspective to the offline reinforcement learning (RL) challenge.
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models
The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation
These models are able to generate creative and fluent metaphor sentences more frequently induced by selected samples from our dataset, demonstrating the value of our corpus for Chinese metaphor research.
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music.
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.
Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Drawing inspiration from the success of multimodal instruction tuning in visual tasks, we treat the visual-based RL task as a long-horizon vision task and construct a set of multimodal game instructions to incorporate instruction tuning into a decision transformer.
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.
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources.
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.
Align on the Fly: Adapting Chatbot Behavior to Established Norms
In this paper, we aim to align large language models with the ever-changing, complex, and diverse human values (e. g., social norms) across time and locations.
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text descriptions, searching for a news article with a headline image, or finding a similar photo with a query image.
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.
Massive Editing for Large Language Models via Meta Learning
While large language models (LLMs) have enabled learning knowledge from the pre-training corpora, the acquired knowledge may be fundamentally incorrect or outdated over time, which necessitates rectifying the knowledge of the language model (LM) after the training.
LRRU: Long-short Range Recurrent Updating Networks for Depth Completion
Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data.
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.
AutoAgents: A Framework for Automatic Agent Generation
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
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.
Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction
(ii) Integrate Network (IN) builds a new integrated sequence by utilizing spatial-temporal interaction on MSS and captures the comprehensive spatial-temporal representation by modeling the integrated sequence with a complicated attention.
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark
We have identified three principles for the proposed "comparing different MI attacks" methodology, and we have designed and implemented the MIBench benchmark with 84 evaluation scenarios for each dataset.
On the Effectiveness of Speech Self-supervised Learning for Music
Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.
LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT
We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal.
MARBLE: Music Audio Representation Benchmark for Universal Evaluation
This is evident in the limited work on deep music representations, the scarcity of large-scale datasets, and the absence of a universal and community-driven benchmark.
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.
TPDM: Selectively Removing Positional Information for Zero-shot Translation via Token-Level Position Disentangle Module
Due to Multilingual Neural Machine Translation's (MNMT) capability of zero-shot translation, many works have been carried out to fully exploit the potential of MNMT in zero-shot translation.
Training Socially Aligned Language Models on Simulated Social Interactions
Social alignment in AI systems aims to ensure that these models behave according to established societal values.
Interactive Natural Language Processing
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.
RSC-VAE: Recoding Semantic Consistency Based VAE for One-Class Novelty Detection
While in this paper, we further exploit the latent space of Variational Auto-encoder (VAE), a typical reconstruction based model, and we innovatively divide it into three regions: Normal/Anomalous/Unknown-semantic-region.
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/}}.
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
CORGI-PM: A Chinese Corpus For Gender Bias Probing and Mitigation
As natural language processing (NLP) for gender bias becomes a significant interdisciplinary topic, the prevalent data-driven techniques such as large-scale language models suffer from data inadequacy and biased corpus, especially for languages with insufficient resources such as Chinese.
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits
We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values.
MAP-Music2Vec: A Simple and Effective Baseline for Self-Supervised Music Audio Representation Learning
The deep learning community has witnessed an exponentially growing interest in self-supervised learning (SSL).
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models
In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups.
1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
This paper details our participation in the Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE) workshop @ EMNLP 2022, where we take part in Subtask 1 of Shared Task 3.
1Cademy @ Causal News Corpus 2022: Enhance Causal Span Detection via Beam-Search-based Position Selector
In this paper, we present our approach and empirical observations for Cause-Effect Signal Span Detection -- Subtask 2 of Shared task 3~\cite{tan-etal-2022-event} at CASE 2022.
Correlation between entropy and generalizability in a neural network
Although neural networks can solve very complex machine-learning problems, the theoretical reason for their generalizability is still not fully understood.
1Cademy at Semeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task
This paper describes our system for the SemEval2022 task of matching dictionary glosses to word embeddings.
Graph-level Neural Networks: Current Progress and Future Directions
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks
Despite its great success, deep learning severely suffers from robustness; that is, deep neural networks are very vulnerable to adversarial attacks, even the simplest ones.
Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis.
StrobeNet: Category-Level Multiview Reconstruction of Articulated Objects
We present StrobeNet, a method for category-level 3D reconstruction of articulating objects from one or more unposed RGB images.
Tilting the playing field: Dynamical loss functions for machine learning
In underparameterized networks, such dynamical loss functions can lead to successful training for networks that fail to find a deep minima of the standard cross-entropy loss.
Network-Agnostic Knowledge Transfer from Latent Dataset for Medical Image Segmentation
Transfer learning often employs all or part of the weights of a pre-trained net-work to the problem at hand; this limits the flexibility of new neural architectures.
Diverse Melody Generation from Chinese Lyrics via Mutual Information Maximization
In this paper, we propose to adapt the method of mutual information maximization into the task of Chinese lyrics conditioned melody generation to improve the generation quality and diversity.
CORAL: COde RepresentAtion Learning with Weakly-Supervised Transformers for Analyzing Data Analysis
Large scale analysis of source code, and in particular scientific source code, holds the promise of better understanding the data science process, identifying analytical best practices, and providing insights to the builders of scientific toolkits.
Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Compared with static views, abundant dynamic properties between video frames are beneficial to refined depth estimation, especially for dynamic objects.
