24 citations · 112 across the 22 of their papers we have counts for
25 papers
ISCA: A Framework for Interview-Style Conversational Agents
Charles Welch, Allison Lahnala, Vasudha Varadarajan +4
We present a low-compute non-generative system for implementing interview-style conversational agents which can be used to facilitate qualitative data collection through controlled…
Knowing When Not to Answer: Lightweight KB-Aligned OOD Detection for Safe RAG
Ilias Triantafyllopoulos, Renyi Qu, Salvatore Giorgi +3
Retrieval-Augmented Generation (RAG) systems are increasingly deployed in high-stakes domains, where safety depends not only on how a system answers, but also on whether a query sh…
PsychAdapter: Adapting LLM Transformers to Reflect Traits, Personality and Mental Health
Huy Vu, Huy Anh Nguyen, Adithya V Ganesan +8
Artificial intelligence-based language generators are now a part of most people's lives. However, by default, they tend to generate "average" language without reflecting the ways i…
The Illusion of Empathy: How AI Chatbots Shape Conversation Perception
Tingting Liu, Salvatore Giorgi, Ankit Aich +4
As AI chatbots increasingly incorporate empathy, understanding user-centered perceptions of chatbot empathy and its impact on conversation quality remains essential yet under-explo…
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas
Salvatore Giorgi, Tingting Liu, Ankit Aich +6
Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. Howeve…
Large Language Models Show Human-like Social Desirability Biases in Survey Responses
Aadesh Salecha, Molly E. Ireland, Shashanka Subrahmanya +3
As Large Language Models (LLMs) become widely used to model and simulate human behavior, understanding their biases becomes critical. We developed an experimental framework using B…
Large Human Language Models: A Need and the Challenges
Nikita Soni, H. Andrew Schwartz, João Sedoc +1
As research in human-centered NLP advances, there is a growing recognition of the importance of incorporating human and social factors into NLP models. At the same time, our NLP sy…
An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
Young Min Cho, Sunny Rai, Lyle Ungar +2
Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous su…
Psychological Metrics for Dialog System Evaluation
Salvatore Giorgi, Shreya Havaldar, Farhan Ahmed +6
We present metrics for evaluating dialog systems through a psychologically-grounded "human" lens in which conversational agents express a diversity of both states (e.g., emotion) a…
Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances
Salvatore Giorgi, Douglas Bellew, Daniel Roy Sadek Habib +6
Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of…
Conceptor-Aided Debiasing of Large Language Models
Li S. Yifei, Lyle Ungar, João Sedoc
Pre-trained large language models (LLMs) reflect the inherent social biases of their training corpus. Many methods have been proposed to mitigate this issue, but they often fail to…
Empathic Conversations: A Multi-level Dataset of Contextualized Conversations
Damilola Omitaomu, Shabnam Tafreshi, Tingting Liu +5
Empathy is a cognitive and emotional reaction to an observed situation of others. Empathy has recently attracted interest because it has numerous applications in psychology and AI,…
Learning Word Ratings for Empathy and Distress from Document-Level User Responses
João Sedoc, Sven Buechel, Yehonathan Nachmany +2
Despite the excellent performance of black box approaches to modeling sentiment and emotion, lexica (sets of informative words and associated weights) that characterize different e…
Comparison of Diverse Decoding Methods from Conditional Language Models
Daphne Ippolito, Reno Kriz, Maria Kustikova +2
While conditional language models have greatly improved in their ability to output high-quality natural language, many NLP applications benefit from being able to generate a divers…
Conceptor Debiasing of Word Representations Evaluated on WEAT
Saket Karve, Lyle Ungar, João Sedoc
Bias in word embeddings such as Word2Vec has been widely investigated, and many efforts made to remove such bias. We show how to use conceptors debiasing to post-process both tradi…
Continual Learning for Sentence Representations Using Conceptors
Tianlin Liu, Lyle Ungar, João Sedoc
Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence represen…
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Reno Kriz, João Sedoc, Marianna Apidianaki +4
Sentence simplification is the task of rewriting texts so they are easier to understand. Recent research has applied sequence-to-sequence (Seq2Seq) models to this task, focusing la…
Unsupervised Post-processing of Word Vectors via Conceptor Negation
Tianlin Liu, Lyle Ungar, João Sedoc
Word vectors are at the core of many natural language processing tasks. Recently, there has been interest in post-processing word vectors to enrich their semantic information. In t…
Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
Tianlin Liu, João Sedoc, Lyle Ungar
Distributed representations of words, better known as word embeddings, have become important building blocks for natural language processing tasks. Numerous studies are devoted to…
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
Sven Buechel, João Sedoc, H. Andrew Schwartz +1
One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data i…
Modeling Empathy and Distress in Reaction to News Stories
Sven Buechel, Anneke Buffone, Barry Slaff +2
Computational detection and understanding of empathy is an important factor in advancing human-computer interaction. Yet to date, text-based empathy prediction has the following ma…
Domain Aware Neural Dialog System
Sajal Choudhary, Prerna Srivastava, Lyle Ungar +1
We investigate the task of building a domain aware chat system which generates intelligent responses in a conversation comprising of different domains. The domain, in this case, is…
Enterprise to Computer: Star Trek chatbot
Grishma Jena, Mansi Vashisht, Abheek Basu +2
Human interactions and human-computer interactions are strongly influenced by style as well as content. Adding a persona to a chatbot makes it more human-like and contributes to a…
Deriving Verb Predicates By Clustering Verbs with Arguments
Joao Sedoc, Derry Wijaya, Masoud Rouhizadeh +2
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such…