Search Results for author: Shirley Anugrah Hayati

Found 14 papers, 6 papers with code

Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation

1 code implementation14 Apr 2024 Ruixin Yang, Dheeraj Rajagopal, Shirley Anugrah Hayati, Bin Hu, Dongyeop Kang

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

no code implementations18 Feb 2024 Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar, Abhinav Sethy, Joo-Kyung Kim, Dongyeop Kang

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.

How Far Can We Extract Diverse Perspectives from Large Language Models?

1 code implementation16 Nov 2023 Shirley Anugrah Hayati, Minhwa Lee, Dheeraj Rajagopal, Dongyeop Kang

In this study, we investigate LLMs' capacity for generating diverse perspectives and rationales on subjective topics, such as social norms and argumentative texts.

Sentence Sentence Embeddings +1

Werewolf Among Us: A Multimodal Dataset for Modeling Persuasion Behaviors in Social Deduction Games

no code implementations16 Dec 2022 Bolin Lai, Hongxin Zhang, Miao Liu, Aryan Pariani, Fiona Ryan, Wenqi Jia, Shirley Anugrah Hayati, James M. Rehg, Diyi Yang

We also explore the generalization ability of language models for persuasion modeling and the role of persuasion strategies in predicting social deduction game outcomes.

Persuasion Strategies

Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform

no code implementations9 Nov 2022 Raj Sanjay Shah, Faye Holt, Shirley Anugrah Hayati, Aastha Agarwal, Yi-Chia Wang, Robert E. Kraut, Diyi Yang

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

1 code implementation14 Oct 2022 Shirley Anugrah Hayati, Kyumin Park, Dheeraj Rajagopal, Lyle Ungar, Dongyeop Kang

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).


DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems

no code implementations16 Apr 2021 Yu Li, Shirley Anugrah Hayati, Weiyan Shi, Zhou Yu

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.

Attribute dialog state tracking +1

INSPIRED: Toward Sociable Recommendation Dialog Systems

1 code implementation EMNLP 2020 Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu, Weiyan Shi, Zhou Yu

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.

Movie Recommendation

What A Sunny Day ☔: Toward Emoji-Sensitive Irony Detection

no code implementations WS 2019 Shirley Anugrah Hayati, Aditi Chaudhary, Naoki Otani, Alan W. black

Irony detection is an important task with applications in identification of online abuse and harassment.

Analyzing Incorporation of Emotion in Emoji Prediction

no code implementations WS 2019 Shirley Anugrah Hayati, Aldrian Obaja Muis

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

1 code implementation EMNLP 2018 Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, Graham Neubig

In models to generate program source code from natural language, representing this code in a tree structure has been a common approach.

Code Generation Retrieval +2

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