Abstract—Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to …
Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on …
Maintaining a consistent persona is a key quality for any open domain dialogue system. Current state-of-the-art systems do this by training agents with supervised learning or online reinforcement lear…
Abstract—This thesis investigates whether large language models (LLMs) can be guided to simulate a consistent personality through prompt engineering. The study explores this concept within the context…
In this paper, we investigate the reliability of LLM-as-a-Personalized- Judge—asking LLMs to judge user preferences based on personas. Our findings suggest that directly applying LLM-as-a-Personalized…
Recent advances in large language models (LLMs) have shown their capacity for generating natural dialogues, leveraging extensive pre-trained knowledge. However, the seamless integration of domain-spec…
Can Large Language Models (LLMs) simulate humans in making important decisions? Recent research has unveiled the potential of using LLMs to develop role-playing language agents (RPLAs), mimicking main…
“In this work, we tested one of these models (GPT-3) on a range of cognitive effects, which are systematic patterns that are usually found in human cognitive tasks. We found that LLMs are indeed prone…
is a workshop call to papers – interesting intro but no actual research We encourage the submission of position papers (2–4 pages in ACM single-column format, including references) that present resea…
“In this work, we investigate the question: Do LLMs with human-like abilities exhibit humanlike personalities? To address this question, we comprehensively examine the MBTI as a preliminary assessment…
Recent advancements in Large Language Models (LLMs) have shown promising performance on ToM benchmarks, raising the question: Do these benchmarks necessitate explicit human-like reasoning processes, o…
We prompted various LLMs with Big Five Personality Scale responses from 816 human individuals to role-play their responses on nine other psychological scales. LLMs demonstrated remarkable accuracy in …
Abstract. In dialogue generation, the naturalness of responses is crucial for effective human-machine interaction. Personalized response generation poses even greater challenges, as the responses must…
we employed Opinion QA Based Parameter-Efficient Fine- Tuning (PEFT), specifically Quantized Low- Rank Adaptation (QLoRA), to manipulate the Big Five personality traits: Openness, Conscientiousness, E…
In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in …
We present a novel agent architecture that simulates the attitudes and behaviors of 1,052 real individuals—applying large language models to qualitative interviews about their lives, then measuring ho…
In this paper, we explore the ability to model and infer personality types of opponents, predict their responses, and use this information to adapt a dialog agent’s high-level strategy in negotiation …
LLMs have shown strong performance on human-centric reasoning tasks. While previous evaluations have explored whether LLMs can infer intentions or detect deception, they often overlook the individuali…
[[Natural Language Inference]] Neural language models (LMs) represent facts about the world described by text. Sometimes these facts derive from training data (in most LMs, a representation of the …
The use of large language models (LLMs) to simulate human behavior has gained significant attention, particularly through personas that approximate individual characteristics. Persona-based simulation…
we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users’ Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = .29 (r…
In response to the LLM CENTAUR [Binz et al., 2025], Bowers et al. [2025] argued that CENTAUR is unlikely to contribute to building a theory of human cognition for three reasons: First, CENTAUR was not…
Hundreds of millions of people use artificial intelligence (AI) for writing assistance. Here, we evaluated how AI writing assistance distorts writer personas – their perceived beliefs, personality, an…
In an ever-expanding world of domain-specific knowledge, the increasing complexity of consuming, and storing information necessitates the generation of summaries from large information repositories. H…
However, systematic exploration of their dual capabilities to autonomously persuade and resist persuasion, particularly in contexts involving psychological rhetoric, remains unexplored. In this paper,…
Our approach involves evaluating the intrinsic personality traits of Open LLM agents and determining the extent to which these agents can mimic human personalities when conditioned by specific persona…
Inspired by these concerns, we propose PersLLM, a comprehensive approach to LLM personification including personified data construction and model tuning. To achieve a sufficient data usage, we use a w…
Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative human data is often expensive or infeasible, partic…
Large language models interact with users through a simulated “Assistant” persona. While the Assistant is typically trained to be helpful, harmless, and honest, it sometimes deviates from these ideals…
(LLMs) are also susceptible to human-like cognitive biases, however, the extent to which LLMs selectively reason toward identity-congruent conclusions remains largely unexplored. Here, we investigate …
This limitation motivates us to develop PersonaAgent, the first personalized LLM agent framework designed to address versatile personalization tasks. Specifically, PersonaAgent integrates two compleme…
These persona agents offer significant enhancements across diverse sectors, such as education, healthcare, and entertainment, where model developers can align agent responses to different user require…
“Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, such descriptions may …
Personalization of Large Language Models (LLMs) has recently become increasingly important with a wide range of applications. Despite the importance and recent progress, most existing works on persona…
The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social scenarios. Although recent studies have evaluated ToM …
PosterMate gathers feedback from each persona agent regarding poster components, and stimulates discussion with the help of a moderator to reach a conclusion. These agreed-upon edits can then be direc…
we present Proxona, a system for defining and extracting representative audience personas from the comments. Creators converse with personas to gain insights into their preferences and engagement, sol…
LLMs fail to satisfy the individual needs of clients who seek different counseling styles. To help bridge this gap, we propose PsyDT, a novel framework using LLMs to construct the Digital Twin of Psyc…
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 in which people diffe…
We introduce MBTI-in-Thoughts, a framework for enhancing the effectiveness of Large Language Model (LLM) agents through psychologically grounded personality conditioning. Drawing on the Myers–Briggs T…
Enhancing user engagement through personalization in conversational agents has gained significance, especially with the advent of large language models that generate fluent responses. Personalized dia…
Here we advocate two basic metaphors for LLM-based dialogue agents. First, taking a simple and intuitive view, we can see a dialogue agent as role-playing a single character. Second, taking a more nua…
However, the closed-source nature of state-of-the-art LLMs and their general-purpose training limit role-playing optimization. In this paper, we introduce RoleLLM, a framework to benchmark, elicit, an…
Therefore, to create diverse synthetic data at scale (e.g., 1 billion diverse math problems), a large number of diverse prompts are needed. Previous research tends to diversify the data synthesis prom…
Conceptual design can be modeled as a proposition making process, where designers make logical propositions to communicate and construct intangible concepts. Not only can LLMs interpret designers’ pro…
We study behavioral self-awareness—an LLM’s ability to articulate its behaviors without requiring in-context examples. We finetune LLMs on datasets that exhibit particular behaviors, such as (a) makin…
Large language models can represent a variety of personas but typically default to a helpful Assistant identity cultivated during post-training. We investigate the structure of the space of model pers…
Social sciences have accumulated a rich body of theories and methodologies for investigating the human mind and behaviors, while offering valuable insights into the design and understanding of Artific…
One of the ways in which we might address hate speech is by contextualizing through the use of counternarratives (CN), which can not only reinforce values like tolerance but also dispel misinformation…
complex tasks like research and strategic thinking often benefit from a more comprehensive approach to augmenting the thinking process rather than passively getting information. We introduce the conce…
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent…
Large Language Models (LLMs) are increasingly tasked with creative generation, including the simulation of fictional characters. However, their ability to portray non-prosocial, antagonistic personas …
Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic information gathering. …
The concept of persona, originally adopted in dialogue literature, has re-surged as a promising framework for tailoring large language models (LLMs) to specific context (e.g., personalized search, LLM…
“A cognitive synergist denotes an intelligent agent that works in conjunction with several minds, merging their unique abilities and expertise to improve problem-solving and overall efficacy in intric…
Writing persuasive arguments is a challenging task for both humans and machines. It entails incorporating high-level beliefs from various perspectives on the topic, along with deliberate reasoning and…
Our LLM replications successfully reproduced 76% of the original main effects (84 out of 111), demonstrating strong potential for AI-assisted replication of studies in which people respond to media st…
David Chalmers [[Linguistics, NLP, NLU]] [[Role Play]] [[Philosophy Subjectivity]] Quasi-interpretivism does not say anything about whether LLMs have beliefs and desires. But it does make it plausib…
We explore the task of improving persona consistency of dialogue agents. Recent models tackling consistency often train with additional Natural Language Inference (NLI) labels or attach trained extra …