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Found 44 papers, 17 papers with code
The NiuTrans System for the WMT 2021 Efficiency Task
This paper describes the NiuTrans system for the WMT21 translation efficiency task.
The NiuTrans System for the WMT20 Quality Estimation Shared Task
This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task.
Prior Constraints-based Reward Model Training for Aligning Large Language Models
Reinforcement learning with human feedback for aligning large language models (LLMs) trains a reward model typically using ranking loss with comparison pairs. However, the training procedure suffers from an inherent problem: the uncontrolled scaling of reward scores during reinforcement learning due to the lack of constraints while training the reward model. This paper proposes a Prior Constraints-based Reward Model (namely PCRM) training method to mitigate this problem.
Efficient Prompting Methods for Large Language Models: A Survey
In this paper, we present a comprehensive overview of these methods.
Large Language Models are Parallel Multilingual Learners
In this study, we reveal an in-context learning (ICL) capability of multilingual large language models (LLMs): by translating the input to several languages, we provide Parallel Input in Multiple Languages (PiM) to LLMs, which significantly enhances their comprehension abilities.
ContrastDiagnosis: Enhancing Interpretability in Lung Nodule Diagnosis Using Contrastive Learning
This framework is designed to introduce inherent transparency and provide extensive post-hoc explainability for deep learning model, making them more suitable for clinical medical diagnosis.
How Does Selection Leak Privacy: Revisiting Private Selection and Improved Results for Hyper-parameter Tuning
Recent works propose a generic private solution for the tuning process, yet a fundamental question still persists: is the current privacy bound for this solution tight?
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms.
Data Formulator: AI-powered Concept-driven Visualization Authoring
We realize this paradigm in Data Formulator, an interactive visualization authoring tool.
Learning Evaluation Models from Large Language Models for Sequence Generation
Large language models achieve state-of-the-art performance on sequence generation evaluation, but typically have a large number of parameters.
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of genuine audio across different datasets.
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation
Applying Reinforcement Learning (RL) to sequence generation models enables the direct optimization of long-term rewards (\textit{e. g.,} BLEU and human feedback), but typically requires large-scale sampling over a space of action sequences.
$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model
The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach.
Is Self-Repair a Silver Bullet for Code Generation?
We hypothesize that this is because self-repair is bottlenecked by the model's ability to provide feedback on its own code; using a stronger model to artificially boost the quality of the feedback, we observe substantially larger performance gains.
Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection
Self-supervised speech models are a rapidly developing research topic in fake audio detection.
Learning From Yourself: A Self-Distillation Method for Fake Speech Detection
To address this problem, we propose using the deepest network instruct shallow network for enhancing shallow networks.
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model.
CodeExp: Explanatory Code Document Generation
Our experiments show that (1) our refined training dataset lets models achieve better performance in the explanation generation tasks compared to larger unrefined data (15x larger), and (2) fine-tuned models can generate well-structured long docstrings comparable to human-written ones.
Execution-based Evaluation for Data Science Code Generation Models
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions.
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
Some scene fake audio detection benchmark results on the SceneFake dataset are reported in this paper.
System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation
The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious content manipulation.
Fully Automated End-to-End Fake Audio Detection
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure.
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio
Many effective attempts have been made for fake audio detection.
Interactive Code Generation via Test-Driven User-Intent Formalization
Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent.
Fault-Aware Neural Code Rankers
Large language models (LLMs) have demonstrated an impressive ability to generate code for various programming tasks.
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions
We show that our use of self-sampled correct and partially-correct solutions can benefit learning and help guide the sampling process, leading to more efficient exploration of the solution space.
Ranked #138 on
Arithmetic Reasoning
on GSM8K
Towards Reliable and Explainable AI Model for Solid Pulmonary Nodule Diagnosis
However, detection and accurate diagnosis of pulmonary nodules depend heavily on the experiences of radiologists and can be a heavy workload for them.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
The NiuTrans Machine Translation Systems for WMT21
This paper describes NiuTrans neural machine translation systems of the WMT 2021 news translation tasks.
The NiuTrans System for the WMT21 Efficiency Task
This paper describes the NiuTrans system for the WMT21 translation efficiency task (http://statmt. org/wmt21/efficiency-task. html).
The NiuTrans System for WNGT 2020 Efficiency Task
This paper describes the submissions of the NiuTrans Team to the WNGT 2020 Efficiency Shared Task.
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking
This paper addresses the efficiency challenge of Neural Architecture Search (NAS) by formulating the task as a ranking problem.
Continual Learning for Fake Audio Detection
However, fine-tuning leads to performance degradation on previous data.
Half-Truth: A Partially Fake Audio Detection Dataset
Therefore, this paper develops such a dataset for half-truth audio detection (HAD).
Falx: Synthesis-Powered Visualization Authoring
Modern visualization tools aim to allow data analysts to easily create exploratory visualizations.
Human-Computer Interaction Programming Languages
Organ Segmentation From Full-size CT Images Using Memory-Efficient FCN
In this paper, we present a memory-efficient FCN to tackle the high GPU memory demand challenge in organ segmentation problem from clinical CT images.
Scout: Rapid Exploration of Interface Layout Alternatives through High-Level Design Constraints
Although exploring alternatives is fundamental to creating better interface designs, current processes for creating alternatives are generally manual, limiting the alternatives a designer can explore.
Learning Transferable Graph Exploration
We propose a `learning to explore' framework where we learn a policy from a distribution of environments.
Precise Estimation of Renal Vascular Dominant Regions Using Spatially Aware Fully Convolutional Networks, Tensor-Cut and Voronoi Diagrams
Then we generate a Voronoi diagram to estimate the renal vascular dominant regions based on the segmented kidney and renal arteries.
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
This behavior can have severe consequences such as usage of increased computation and induce faults in downstream modules that expect outputs of a certain length.
Robust Text-to-SQL Generation with Execution-Guided Decoding
We consider the problem of neural semantic parsing, which translates natural language questions into executable SQL queries.
Natural Language to Structured Query Generation via Meta-Learning
In conventional supervised training, a model is trained to fit all the training examples.
Ranked #7 on
Code Generation
on WikiSQL
NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System
We present new data and semantic parsing methods for the problem of mapping English sentences to Bash commands (NL2Bash).
Pointing Out SQL Queries From Text
We present a system that allows for querying data tables using natural language questions, where the system translates the question into an executable SQL query.
