Search Results for author:
Found 13 papers, 3 papers with code
Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection
An ever-increasing amount of text, in the form of social media posts and news articles, gives rise to new challenges and opportunities for the automatic extraction of socio-political events.
Discovering Black Lives Matter Events in the United States: Shared Task 3, CASE 2021
Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently.
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News
Current approaches to emotion detection often overlook the inherent subjectivity of affective experiences, instead relying on aggregated labels that mask individual variations in emotional responses.
Efficient Long-Decoding Inference with Reasoning-Aware Attention Sparsity
For example, the state-of-the-art algorithm, Quest, achieves high accuracy with $O(L)$ time but $O(N)$ memory ($L$ is the cache budget, $L \ll N$).
EPIC: Efficient Position-Independent Context Caching for Serving Large Language Models
Large Language Models (LLMs) are critical for a wide range of applications, but serving them efficiently becomes increasingly challenging as inputs become more complex.
Can LLM be a Personalized Judge?
Ensuring that large language models (LLMs) reflect diverse user values and preferences is crucial as their user bases expand globally.
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
This paper aims to bridge this gap by providing a comprehensive overview of recent works on the evaluation of AOVs in LLMs.
Can Language Models Recognize Convincing Arguments?
The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives.
Quantifying the Persona Effect in LLM Simulations
Notably, we find a linear relationship in our setting: the stronger the correlation between persona variables and human annotations, the more accurate the LLM predictions are using persona prompting.
Generative Language Models Exhibit Social Identity Biases
The surge in popularity of large language models has given rise to concerns about biases that these models could learn from humans.
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News
Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning.
Drug Re-positioning via Text Augmented Knowledge Graph Embeddings
Drug re-positioning, modeled as a link prediction problem over medical knowledge graphs (KG), has great potential in finding new usage or targets for approved medicine with relatively low cost.
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention
We use the Fi- Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions.
