Search Results for author:
Found 17 papers, 10 papers with code
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection
To demonstrate the threat, we propose a simple method to perform VPI by poisoning the model's instruction tuning data, which proves highly effective in steering the LLM.
Instruction-following Evaluation through Verbalizer Manipulation
We conduct a comprehensive evaluation of four major model families across nine datasets, employing twelve sets of verbalizers for each of them.
AlpaGasus: Training A Better Alpaca with Fewer Data
Large language models (LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data.
Graph Reasoning for Question Answering with Triplet Retrieval
State-of-the-art methods often utilize entities in questions to retrieve local subgraphs, which are then fed into KG encoder, e. g. graph neural networks (GNNs), to model their local structures and integrated into language models for question answering.
Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting
Large language models (LLMs) demonstrate remarkable medical expertise, but data privacy concerns impede their direct use in healthcare environments.
Time Series as Images: Vision Transformer for Irregularly Sampled Time Series
This paper introduces a novel perspective by converting irregularly sampled time series into line graph images, then utilizing powerful pre-trained vision transformers for time series classification in the same way as image classification.
Improving Medical Predictions by Irregular Multimodal Electronic Health Records Modeling
Our method first addresses irregularity in each single modality by (1) modeling irregular time series by dynamically incorporating hand-crafted imputation embeddings into learned interpolation embeddings via a gating mechanism, and (2) casting a series of clinical note representations as multivariate irregular time series and tackling irregularity via a time attention mechanism.
Explanations from Large Language Models Make Small Reasoners Better
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations.
Controllable Dialogue Simulation with In-Context Learning
Experimental results on the MultiWOZ dataset demonstrate that training a model on the simulated dialogues leads to even better performance than using the same amount of human-generated dialogues under the challenging low-resource settings, with as few as 85 dialogues as a seed.
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering
With the recent advance in large pre-trained language models, researchers have achieved record performances in NLP tasks that mostly focus on language pattern matching.
Ranked #2 on
Question Answering
on ConvFinQA
Limitations of Language Models in Arithmetic and Symbolic Induction
LMs with tutor is able to deliver 100% accuracy in situations of OOD and repeating symbols, shedding new insights on the boundary of large LMs in induction.
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data.
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.
Ranked #2 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
(using extra training data)
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach
Recently, pretrained language models (e. g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability.
TabFact: A Large-scale Dataset for Table-based Fact Verification
To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.
Ranked #10 on
Table-based Fact Verification
on TabFact
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Ranked #27 on
Image Generation
on ImageNet 64x64
(Bits per dim metric)
Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
Asynchronous momentum stochastic gradient descent algorithms (Async-MSGD) is one of the most popular algorithms in distributed machine learning.
