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.

Similar Papers

Dehumanizing Machines: Mitigating Anthropomorphic Behaviors in Text Generation Systems (2025.acl-long)

Copied to clipboard

Challenge: Existing studies have focused on how text generation systems can lead to harmful outcomes such as over-reliance, emotional dependence, dehumanization, deception, or even physical harm.
Approach: They propose to use an inventory of interventions to help identify possible interventions and provide a conceptual framework to help characterize the landscape of possible interventions.
Outcome: The proposed interventions are based on an inventory of interventions grounded in prior literature and a crowdsourcing study where participants edited system outputs to make them less human-like.
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.
HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)

Copied to clipboard

Challenge: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs).
Approach: They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other.
Outcome: The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters.
Assessing How Users Display Self-Disclosure and Authenticity in Conversation with Human-Like Agents: A Case Study of Luda Lee (2022.findings-aacl)

Copied to clipboard

Challenge: Existing studies on how people interact with conversational agents have not investigated the interaction authenticity of human-like agents.
Approach: They construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting.
Outcome: The proposed taxonomy can be used for future research and industrial development.
A Taxonomy of Empathetic Response Intents in Human Social Conversations (2020.coling-main)

Copied to clipboard

Challenge: Open-domain conversational agents or chatbots are becoming increasingly popular in the natural language processing community.
Approach: They aim to combine dialogue act/intent modelling and neural response generation to produce a large-scale taxonomy for empathetic response intents.
Outcome: The proposed method improves the response quality of chatbots and makes them more controllable and interpretable.
Thinking beyond the anthropomorphic paradigm benefits LLM research (2026.acl-long)

Copied to clipboard

Challenge: anthropomorphism is an automatic and unconscious response that occurs even in advanced technical expertise.
Approach: They argue that anthropomorphism is an automatic and unconscious response . they identify and examine five assumptions that shape research across the LLM development lifecycle .
Outcome: The proposed framework challenges assumptions that shape research across the LLM development lifecycle and offers promising directions for LLMs.
The R-U-A-Robot Dataset: Helping Avoid Chatbot Deception by Detecting User Questions About Human or Non-Human Identity (2021.acl-long)

Copied to clipboard

Challenge: We analyze 2,500 phrasings related to the intent of “Are you a robot?” and 2,500 adversarially selected utterances to determine whether systems are non-human.
Approach: They analyze 2,500 phrasings related to the intent of "Are you a robot?" and 2,500 adversarially selected utterances to determine whether systems are non-human.
Outcome: The proposed model and two systems fail to confirm non-human intent, and the proposed model is complex.
Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics (2025.emnlp-main)

Copied to clipboard

Challenge: a study of multi-dimensional persona effects in AI-AI debates shows that personas influence moral stances and debate outcomes . political ideology and personality traits exert the strongest influence, according to our study .
Approach: They propose to use a 6-dimensional persona space to simulate structured debates . they find political ideology and personality traits exert the strongest influence .
Outcome: The study shows that personas affect moral stances and debate outcomes . political ideology and personality traits exert the strongest influence .
Are Personalized Stochastic Parrots More Dangerous? Evaluating Persona Biases in Dialogue Systems (2023.findings-emnlp)

Copied to clipboard

Challenge: Recent advances in Large Language Models enable them to follow freeform instructions, including imitating generic or specific demographic personas in conversations.
Approach: They propose to investigate persona biases by experimenting with UNIVERSALPERSONA, a model that incorporates both generic and specific personas.
Outcome: The proposed model systematically measures persona biases in harmful expression and harmful agreement.
PicPersona-TOD : A Dataset for Personalizing Utterance Style in Task-Oriented Dialogue with Image Persona (2025.naacl-long)

Copied to clipboard

Challenge: Existing systems produce generic, monotonic responses that lack individuality and fail to adapt to users’ personal attributes.
Approach: They propose a dataset that incorporates user images as part of the persona, enabling personalized responses tailored to user-specific factors such as age or emotional context.
Outcome: The proposed dataset enhances user experience, with personalized responses contributing to a more engaging interaction.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations