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Found 43 papers, 18 papers with code
Knowledge-Augmented Methods for Natural Language Processing
Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models.
In-Context Demonstration Selection with Cross Entropy Difference
Our method is based on the observation that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example by a language model that was finetuned on that demonstration.
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.
LMGQS: A Large-scale Dataset for Query-focused Summarization
We hypothesize that there is a hidden query for each summary sentence in a generic summarization annotation, and we utilize a large-scale pretrained language model to recover it.
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utilization in various applications.
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
Small Models are Valuable Plug-ins for Large Language Models
Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware.
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning
Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions.
Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles
Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective.
Empowering Language Models with Knowledge Graph Reasoning for Question Answering
Answering open-domain questions requires world knowledge about in-context entities.
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models.
Generate rather than Retrieve: Large Language Models are Strong Context Generators
We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.
REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering
Specifically, we observe that in most state-of-the-art knowledge-based VQA methods: 1) visual features are extracted either from the whole image or in a sliding window manner for retrieving knowledge, and the important relationship within/among object regions is neglected; 2) visual features are not well utilized in the final answering model, which is counter-intuitive to some extent.
Ranked #4 on
Visual Question Answering (VQA)
on OK-VQA
Automatic Rule Induction for Interpretable Semi-Supervised Learning
Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Integrating Rankings into Quantized Scores in Peer Review
Our approach addresses the two aforementioned challenges by: (i) ensuring that rankings are incorporated into the updates scores in the same manner for all papers, thereby mitigating arbitrariness, and (ii) allowing to seamlessly use existing interfaces and workflows designed for scores.
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Surprisingly, we found that REtrieving from the traINing datA (REINA) only can lead to significant gains on multiple NLG and NLU tasks.
Unsupervised Multi-Granularity Summarization
In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum.
Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention
In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities.
Ranked #2 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
An Empirical Study of Training End-to-End Vision-and-Language Transformers
Vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks.
Ranked #17 on
Cross-Modal Retrieval
on COCO 2014
Leveraging Knowledge in Multilingual Commonsense Reasoning
Then we utilize a diverse of 4 English knowledge sources to provide more comprehensive coverage of knowledge in different formats.
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.
DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization
For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.
Want To Reduce Labeling Cost? GPT-3 Can Help
Data annotation is a time-consuming and labor-intensive process for many NLP tasks.
Retrieval Enhanced Model for Commonsense Generation
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts.
Fusing Context Into Knowledge Graph for Commonsense Question Answering
However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.
Ranked #3 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
Preference-based Reinforcement Learning with Finite-Time Guarantees
If preferences are stochastic, and the preference probability relates to the hidden reward values, we present algorithms for PbRL, both with and without a simulator, that are able to identify the best policy up to accuracy $\varepsilon$ with high probability.
Zeroth Order Non-convex optimization with Dueling-Choice Bandits
We consider a novel setting of zeroth order non-convex optimization, where in addition to querying the function value at a given point, we can also duel two points and get the point with the larger function value.
Active Learning for Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.
Thresholding Bandit Problem with Both Duels and Pulls
The Thresholding Bandit Problem (TBP) aims to find the set of arms with mean rewards greater than a given threshold.
Active Learning Graph Neural Networks via Node Feature Propagation
Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data.
DoubleTransfer at MEDIQA 2019: Multi-Source Transfer Learning for Natural Language Understanding in the Medical Domain
We use a multi-source transfer learning approach to transfer the knowledge from MT-DNN and SciBERT to natural language understanding tasks in the medical domain.
Multi-task Learning with Sample Re-weighting for Machine Reading Comprehension
We propose a multi-task learning framework to learn a joint Machine Reading Comprehension (MRC) model that can be applied to a wide range of MRC tasks in different domains.
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Finally, we present experiments that show the efficacy of RR and investigate its robustness to various sources of noise and model-misspecification.
On Strategyproof Conference Peer Review
We then empirically show that the requisite property on the authorship graph is indeed satisfied in the submission data from the ICLR conference, and further demonstrate a simple trick to make the partitioning method more practically appealing for conference peer review.
Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction.
Noise-Tolerant Interactive Learning Using Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Dynamic Fusion Networks for Machine Reading Comprehension
This paper presents a novel neural model - Dynamic Fusion Network (DFN), for machine reading comprehension (MRC).
Noise-Tolerant Interactive Learning from Pairwise Comparisons
We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive.
Scale-Invariant Convolutional Neural Networks
Even though convolutional neural networks (CNN) has achieved near-human performance in various computer vision tasks, its ability to tolerate scale variations is limited.
The Application of Two-level Attention Models in Deep Convolutional Neural Network for Fine-grained Image Classification
Our pipeline integrates three types of attention: the bottom-up attention that propose candidate patches, the object-level top-down attention that selects relevant patches to a certain object, and the part-level top-down attention that localizes discriminative parts.
