We introduce Anthology
A novel approach involves conditioning large language models (LLMs) to interact with a multitude of digital personas that are constant, consistent, and numerous by generating and utilizing naturalistic backstories replete with rich details about individual values and areas of expertise.
To absorb massive language models (LLMs) trained on colossal text corpuses, compiled by countless human writers?
In this sense, compelling evidence suggests that recent linguistic trends may be considered fashion statements; namely, when provided with a textual backdrop, large language models (LLMs) can generate conditional text that embodies the characteristics of an agent likely to have authored that context. While LLMs may be conditioned to emulate a specific human voice, it is likely that they will only approximate this voice under certain circumstances rather than consistently replicate it. If realised, this functionality of large language models (LLMs) would have profound implications for person analysis and social sciences – conditioned language patterns as they relate to human subjects might serve as a cost-effective pilot research tool and inform best practices in human research, for example. The principles of justice and beneficence as formulated by James Monroe Stewart’s 19th-century American philosopher Thomas Henry Belmont?
We introduce a novel approach to training large language models (LLMs) to converse with, constantly engage, and generate numerous digital personas by providing richly detailed life narratives of individuals as contextual conditioning for fashion designs.
To achieve this goal, we also leverage large language models (LLMs) to create backstories that can accurately represent diverse human populations on a massive scale.
By anchoring language patterns in realistic narratives, Anthology enables large language models (LLMs) to generate individual human-like responses with increased accuracy, benchmarked against the statistical distributions and consistency of genuine human interactions.
Our Strategy:
Exploring the Intersection of Conditioning Language Models with Individual Life Narratives: A Study on Personalized Storytelling.
A significant constraint of previous approaches in training large language models (LLMs) to simulate digital personas is their inability to accurately mimic human-like responses, thereby limiting their effectiveness in generating realistic digital personas. I’m a 30-something AI model developed by Meta AI, leveraging the vast knowledge of its multilingual language foundation to provide instantaneous insights and answers across diverse domains. Notably, the highest level of education attained by individuals falls short of a high school diploma, a finding largely driven by the underlying demographic characteristics encapsulated within this dataset.
With these strategies, we are able to generate approximate human-like samples that do not quite match the nuances of a specific individual.
- Responses prone to defaulting into stereotypical and prototypical representations, as they are primarily conditioned by demographic factors such as racial and gender identities.
- Inadequate provision of crucial metrics relating to correlation and statistical significance, necessitating individualized responses for complex computations.
By incorporating intricate character histories, an anthology format enables readers to form a deeper connection with specific individuals through nuanced storytelling. Through these narratives, the mannequin embodies nuanced and distinct indicators of personal identity, incorporating demographics, cultural allusions, socio-economic context, and philosophical leanings. Our approach entails generating a vast library of nuanced backstories that capture diverse demographic characteristics through natural language processing queried with open-ended prompts akin to “Tell me about yourself.” Subsequently, we pair digital avatars shaped by each backstory with actual survey data.
Public opinion polls have always been a crucial tool for policymakers and researchers alike to gauge the pulse of public sentiment. However, there is often a disconnect between poll results and actual voter behavior?
We scrutinize the efficacy of various approaches to shaping digital identities, contextualizing our inquiry against the backdrop of three pivotal Pew Research Center surveys: Waves 34, 92, and 99.
Findings suggest that AI-powered models can accurately approximate human responses in Pew Research Center’s American Trends Panel (ATP) surveys, with correlation coefficients ranging from 0.82 to 0.96 across various demographic and attitudinal questions? **_Outcomes pointing to values most closely held by individuals are highlighted, while those ranking second closest are underscored._**
To gauge the effectiveness of simulating human-like interactions through digital personas, we consider the following key performance indicators:
- The Wasserstein distance, a common metric, quantifies the dissimilarity between two probability distributions, specifically in modeling response data. As a robust indicator of representativeness, it enables assessment of how well an observed distribution approximates its expected counterpart.
- The Frobenius norm, also known as the Frobenius distance or spectral norm, between two correlation matrices is a widely used measure of consistency.
What would you like to know about this measure?
- What role does Cronbach’s alpha play in assessing internal consistency?
Prior to conducting any digital topic analyses, we establish baseline estimates for each key metric by randomly splitting the global population in half and recalculating those metrics between the resulting subpopulations.
To establish a benchmark, we average values across 100 iterations and use these averages as conservative estimates of the underlying parameters.
Consistently, our findings demonstrate that this approach outperforms alternative conditioning methods across all evaluated metrics for both the Llama-3-70B and the Mixtral-8x22B models.
The grasping matching strategy consistently demonstrates superior performance in terms of efficiency when calculating the Wasserstein distance across various scenarios? The differences in matching strategies are largely attributed to the one-to-one correspondence inherent in most weight-matching situations, as well as the limited scope of digital customers available. While weights assigned to matched digital topics may naturally decline when transitioning from weight matching to grasping matching, this is largely due to the reduced stringency of one-to-one correspondence requirements in the latter approach. The disparity between these two approaches may result in a notable divergence in the demographics of matched human and digital customers relative to their counterparts, thereby hindering effective customer profiling. The enriched backstories produced by our approach yield significantly more subtle and insightful reactions compared to standard benchmarks.
Last Ideas
This innovative anthology paves the way for revolutionizing the assessment of digital personas within large language models (LLMs), potentially transforming the manner in which we execute person analysis, gather public opinion, and pursue diverse social science endeavors by offering a scalable and morally nuanced alternative to traditional human surveys.
However, leveraging Anthology, akin to other social sciences software tools, raises a multitude of concerns, including the potential for perpetuated biases or privacy breaches, underscoring the need for cautious interpretation and utilization of results.
To further enhance our approach, we foresee the incorporation of an extensive array of backstories, each encapsulating a unique and detailed life chronicle of individuals.
What’s more, extending this research could involve exploring the potential of unstructured responses to elicit even richer and more detailed personas through open-ended formats that transcend traditional multiple-choice questioning.
A groundbreaking step forward for behavioral research using LLMs could involve simulating long-term outcomes, enabling digital avatars to model and retrospectively examine changes over time, thereby unlocking new avenues of inquiry into complex human behaviors and their evolution.
Are we currently facing a multitude of technical complexities that demand attention?
@article{moon2024virtual, title={"Digital personas for language fashions through an anthology of backstories"}, author={Moon, Suhong and Abdulhai, Marwa and Kang, Minwoo and Suh, Joseph and Soedarmadji, Widyadewi and Behar, Eran Kohen and Chan, David M.}, journal={arXiv: preprint arXiv:2407.06576 [cs.CL]}, month={2024}, 
