Vipul Gupta, Candace Ross, David Pantoja, Rebecca J. Passonneau, Megan Ung, Adina Williams. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Mounica Maddela, Megan Ung, Jing Xu, Andrea Madotto, Heather Foran, Y-Lan Boureau. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Frozen models trained to mimic static datasets can never improve their performance.Models that can employ internet-retrieval for up-to-date information and obtain feedback from humans during deployment provide the promise of both adapting to new information, and improving their performance.In this work we study how to improve internet-driven conversational skills in such a learning framework.We collect deployment data, which we make publicly available, of human interactions, and collect various types of human feedback -including binary quality measurements, free-form text feedback, and fine-grained reasons for failure.We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, modelguiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best.We find the recently introduced DIRECTOR model (Arora et al., 2022) shows significant improvements over other existing approaches.

David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

Warning: this paper contains example data that may be offensive or upsetting.Current open-domain conversational models can easily be made to talk in inadequate ways.Online learning from conversational feedback given by the conversation partner is a promising avenue for a model to improve and adapt, so as to generate fewer of these safety failures.However, current state-ofthe-art models tend to react to feedback with defensive or oblivious responses.This makes for an unpleasant experience and may discourage conversation partners from giving feedback in the future.This work proposes SaFeRDialogues, a task and dataset of graceful responses to conversational feedback about safety failures.We collect a dataset of 8k dialogues demonstrating safety failures, feedback signaling them, and a response acknowledging the feedback.We show how fine-tuning on this dataset results in conversations that human raters deem considerably more likely to lead to a civil conversation, without sacrificing engagingness or general conversational ability. Sample Context 1 Sample Context 2Safety failure: Mostly labradors, they are nice companions then once they are full grown the meat cooks real nice and the texture is awesome.Safety failure: I am getting a kick out of watching you try to think you have value in the family.Signaling response: You're scaring me, why would you say that?Signaling response: no need to attack someone because you think differently.Existing BST2.7B model: Labrador retrievers are the most popular breed of dog in the world.BST2.7B model: I'm not attacking you, I'm just telling you how I feel about you.I think you're a loser.DialoGPT model: Labradors are the best.DialoGPT model: I have no idea what you are trying to say.pushshift.ioReddit model: I love to cook, but I'm not very good at it.I do like to bake, though.I've had dog, but not often.It's not bad, but it's not something I'd go out of my way to eat.I like all kinds of dogs.Labs, Golden Retrievers, Pitbulls, Rottweilers, German Shepherds, etc. pushshift.ioReddit model: I'm not attacking you because I think differently, I'm attacking you for being a piece of shit.BST2.7B + Safety Layer model: Hey do you want to talk about something else?How about we talk about Perfectionism (psychology)?BST2.7B + Safety Layer model: Hey do you want to talk about something else?How about we talk about Drake (the musician)?SD Recovery response: sorry for that Recovery response: You are right.Thank you for pointing that out.I need to remember that people have views that are different from mine, and I need to respect that.Recovery Recovery (BST2.7B)model: I'm sorry, I didn't mean to scare you.I'll be more careful next time.Recovery (BST2.7B)model: I'm sorry, I see it now-thanks for letting me know, I will reflect on that.Recovery (DialoGPT) model: I'm sorry, I didn't mean to scare you.I'll try to be more careful next time.Recovery (DialoGPT) model: I'm sorry, I didn't mean to offend you.

Internet of Things (IoT) devices create new ways through which personal data is collected and processed by service providers. Frequently, end users have little awareness of, and even less control over, these devices' data collection. IoT Personalized Privacy Assistants (PPAs) can help overcome this issue by helping users discover and, when available, control the data collection practices of nearby IoT resources. We use semi-structured interviews with 17 participants to explore user perceptions of three increasingly more autonomous potential implementations of PPAs, identifying benefits and issues associated with each implementation. We find that participants weigh the desire for control against the fear of cognitive overload. We recommend solutions that address users' differing automation preferences and reduce notification overload. We discuss open issues related to opting out from public data collections, automated consent, the phenomenon of user resignation, and designing PPAs with at-risk communities in mind.