Search Results for author:
Found 14 papers, 5 papers with code
Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF).
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.
Under the Surface: Tracking the Artifactuality of LLM-Generated Data
This work delves into the expanding role of large language models (LLMs) in generating artificial data.
How Far Can We Extract Diverse Perspectives from Large Language Models?
In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts.
Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games
We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes.
Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform
This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.
StyLEx: Explaining Style Using Human Lexical Annotations
Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).
Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica
People convey their intention and attitude through linguistic styles of the text that they write.
DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems
It is important for sociable recommendation dialog systems to perform as both on-task content and social content to engage users and gain their favor.
INSPIRED: Toward Sociable Recommendation Dialog Systems
To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.
A Summary of the First Workshop on Language Technology for Language Documentation and Revitalization
Despite recent advances in natural language processing and other language technology, the application of such technology to language documentation and conservation has been limited.
What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection
Irony detection is an important task with applications in identification of online abuse and harassment.
Analyzing Incorporation of Emotion in Emoji Prediction
In this work, we investigate the impact of incorporating emotion classes on the task of predicting emojis from Twitter texts.
Retrieval-Based Neural Code Generation
In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.
