Linguistic Nudges and Verbal Interaction with Robots, Smart-Speakers, and Humans (2024.lrec-main)
Copied to clipboard
| Challenge: | nudges are a choice architecture that alters people's behavior without forbidding any options or significantly changing their economic incentives. |
| Approach: | They describe a data collection methodology and emotion annotation of dyadic interactions between a human, a Pepper robot, . a Google Home smart-speaker, and other humans. |
| Outcome: | The collected 16-hour audio recordings show that humans change their opinions on more questions with a human than with nudges, even against mainstream ideas. |
Similar Papers
Corpus Design for Studying Linguistic Nudges in Human-Computer Spoken Interactions (2022.lrec-1)
Copied to clipboard
Natalia Kalashnikova, Serge Pajak, Fabrice Le Guel, Ioana Vasilescu, Gemma Serrano, Laurence Devillers
| Challenge: | linguistic nudges can influence people to the same degree as a human agent, according to Thaler and Sunstein (2008). |
| Approach: | They propose to use a corpus design method to compare influence between linguistic nudges with positive or negative influences and three conversational agents: robot, smart speaker, and human. |
| Outcome: | The results show that linguistic nudges can influence participants to the same degree as human agents. |
Leveraging Implicit Feedback from Deployment Data in Dialogue (2024.eacl-short)
Copied to clipboard
| Challenge: | Xu et al., 2023) and Bai ed., 2019) use crowdworkers to collect signals from natural dialogue episodes. |
| Approach: | They use the publicly released BlenderBot deployment data to extract signals from conversations to implicitly measure the quality of a machine-generated utterance. |
| Outcome: | The proposed model improves over baseline models, but some proxy signals can lead to undesirable generations. |
Chameleon LLMs: User Personas Influence Chatbot Personality Shifts (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies have examined whether large language models adapt their perceived personalities in response to user interactions. |
| Approach: | They propose to use a controlled simulation to measure chatbot personality shifts before and after the interaction to determine whether LLMs exhibit conversational adaptations. |
| Outcome: | The proposed model exhibits personality adaptations over prolonged interactions, while Emotional Stability and Intellect remain relatively stable. |
Robots-Dont-Cry: Understanding Falsely Anthropomorphic Utterances in Dialog Systems (2022.emnlp-main)
Copied to clipboard
| Challenge: | Dialog systems often output human-like responses, but some are impossible for a machine to say. |
| Approach: | They collect ratings on the feasibility of 900 two-turn dialogs from 9 data sources . they build classifiers and explore how modeling configuration might affect output permissibly . |
| Outcome: | The proposed model can be used to train human-like dialogs, but it is not anthropomorphic. |
Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing persona datasets capture only trait, and ignore impact of state. |
| Approach: | They use a Reddit dataset to study user interactions with language models . they find that existing persona datasets capture only trait and ignore impact of state . |
| Outcome: | The proposed dataset decomposes variance and finds that LLMs are state-blind . the reward models react to user state, but inconsistently, the authors say . |
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)
Copied to clipboard
| Challenge: | a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities . |
| Approach: | They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses . |
| Outcome: | The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response. |
An Information-Providing Closed-Domain Human-Agent Interaction Corpus (L18-1)
Copied to clipboard
| Challenge: | a human-agent interaction corpus is a corpus of conversations between a user and an embodied conversational agent operated by a wizard of oz . data collected to create a 'corpus' with unexpected situations, such as misunderstandings, false information, and interruptions. |
| Approach: | They propose a public corpus for Human-Agent Interaction where the agent is controlled by a Wizard of Oz. |
| Outcome: | The proposed corpus is based on 15 conversations between users and a wizard of Oz agent . the data are used to create a corpus with unexpected situations, such as misunderstandings, false information, and interruptions. |
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on abusive language towards conversational AI systems are not conclusive as they are not performed with live systems nor with real users due to the lack of reliable abuse detection tools. |
| Approach: | They propose to use a convAI dataset to account for the complexity of the task and to bench-mark existing models against this data. |
| Outcome: | The proposed model shows that abuse distribution is different compared to other datasets, with sexual tinted aggression towards the virtual persona of the systems. |
NUANCED: Natural Utterance Annotation for Nuanced Conversation with Estimated Distributions (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing conversational systems are agent-centric, which assumes the user utterances will closely follow the system ontology. |
| Approach: | They build a dataset that maps user preferences to an ontology and model user preferences as estimated distributions over the system ontologies. |
| Outcome: | The proposed system can be used to push existing research from agent-centric to user-centric. |
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas (2024.findings-emnlp)
Copied to clipboard
Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle Ungar, Brenda Curtis
| Challenge: | 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. |
| Approach: | They propose to prompt LLMs with human-like personas and ask them to answer as if they were a specific human, either explicitly, with exact demographics, political beliefs, and lived experiences, or implicitly via names prevalent in specific populations. |
| Outcome: | The proposed model is based on explicit, explicit, and implicit personas, and fails to show implicit biases. |