Search Results for author:
Found 9 papers, 6 papers with code
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences.
Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking
Pretrained large language models have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions.
Cascade Speculative Drafting for Even Faster LLM Inference
Introduced to enhance the efficiency of large language model (LLM) inference, speculative decoding operates by having a smaller model generate a draft.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
Measuring the Effect of Influential Messages on Varying Personas
Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.
LM-Switch: Lightweight Language Model Conditioning in Word Embedding Space
As pre-training and fine-tuning are costly and might negatively impact model performance, it is desired to efficiently adapt an existing model to different conditions such as styles, sentiments or narratives, when facing different audiences or scenarios.
Incorporating Task-specific Concept Knowledge into Script Learning
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion.
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research.
HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction
Text-to-Graph extraction aims to automatically extract information graphs consisting of mentions and types from natural language texts.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
