Search Results for author:
Found 26 papers, 13 papers with code
Attribute Injection for Pretrained Language Models: A New Benchmark and an Efficient Method
Metadata attributes (e. g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by expanding the architecture of the models to improve performance.
Aligning Large Language Models through Synthetic Feedback
In this work, we propose a novel framework for alignment learning with almost no human labor and no dependency on pre-aligned LLMs.
Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization
Such a strategy compresses the size of the model considerably, leading to a lower inference latency upon deployment and a reduction in the overall memory required.
Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning
As the size of the pre-trained language model (PLM) continues to increase, numerous parameter-efficient transfer learning methods have been proposed recently to compensate for the tremendous cost of fine-tuning.
Critic-Guided Decoding for Controlled Text Generation
In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding.
Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners
Through in-context learning (ICL), large-scale language models are effective few-shot learners without additional model fine-tuning.
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience.
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
To combine parameter-efficient adaptation and model compression, we propose AlphaTuning consisting of post-training quantization of the pre-trained language model and fine-tuning only some parts of quantized parameters for a target task.
Continuous Decomposition of Granularity for Neural Paraphrase Generation
While Transformers have had significant success in paragraph generation, they treat sentences as linear sequences of tokens and often neglect their hierarchical information.
Self-Generated In-Context Learning: Leveraging Auto-regressive Language Models as a Demonstration Generator
Large-scale pre-trained language models (PLMs) are well-known for being capable of solving a task simply by conditioning a few input-label pairs dubbed demonstrations on a prompt without being explicitly tuned for the desired downstream task.
Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations
Despite recent explosion of interests in in-context learning, the underlying mechanism and the precise impact of the quality of demonstrations remain elusive.
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Based on a recent trend that multimodal generative evaluations exploit a vison-and-language pre-trained model, we propose the negative Gaussian cross-mutual information using the CLIP features as a unified metric, coined by Mutual Information Divergence (MID).
Ranked #1 on
Human Judgment Classification
on Pascal-50S
Hallucination Pair-wise Detection (1-ref)
Hallucination Pair-wise Detection (4-ref)
+5
Generating Information-Seeking Conversations from Unlabeled Documents
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants.
Masked Summarization to Generate Factually Inconsistent Summaries for Improved Factual Consistency Checking
To this end, the latest approach is to train a factual consistency classifier on factually consistent and inconsistent summaries.
Response Generation with Context-Aware Prompt Learning
Pre-trained language models (PLM) have marked a huge leap in neural dialogue modeling.
Efficient Attribute Injection for Pretrained Language Models
Metadata attributes (e. g., user and product IDs from reviews) can be incorporated as additional inputs to neural-based NLP models, by modifying the architecture of the models, in order to improve their performance.
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data.
Self-Guided Contrastive Learning for BERT Sentence Representations
In this work, we propose a contrastive learning method that utilizes self-guidance for improving the quality of BERT sentence representations.
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation
Large-scale language models such as GPT-3 are excellent few-shot learners, allowing them to be controlled via natural text prompts.
Reward Optimization for Neural Machine Translation with Learned Metrics
Results show that the reward optimization with BLEURT is able to increase the metric scores by a large margin, in contrast to limited gain when training with smoothed BLEU.
DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances
Recent advances in pre-trained language models have significantly improved neural response generation.
Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation
Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks.
Don't Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja
We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i. e. Hanja).
Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Data scarcity is one of the main obstacles of domain adaptation in spoken language understanding (SLU) due to the high cost of creating manually tagged SLU datasets.
Improving Visually Grounded Sentence Representations with Self-Attention
Sentence representation models trained only on language could potentially suffer from the grounding problem.
Learning to Compose Task-Specific Tree Structures
For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks.
Ranked #62 on
Natural Language Inference
on SNLI
