Estimating the Personality of White-Box Language Models
- URL: http://arxiv.org/abs/2204.12000v2
- Date: Wed, 10 May 2023 21:17:34 GMT
- Title: Estimating the Personality of White-Box Language Models
- Authors: Saketh Reddy Karra, Son The Nguyen, Theja Tulabandhula
- Abstract summary: Large-scale language models, which are trained on large corpora of text, are being used in a wide range of applications everywhere.
Existing research shows that these models can and do capture human biases.
Many of these biases, especially those that could potentially cause harm, are being well-investigated.
However, studies that infer and change human personality traits inherited by these models have been scarce or non-existent.
- Score: 0.589889361990138
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Technology for open-ended language generation, a key application of
artificial intelligence, has advanced to a great extent in recent years.
Large-scale language models, which are trained on large corpora of text, are
being used in a wide range of applications everywhere, from virtual assistants
to conversational bots. While these language models output fluent text,
existing research shows that these models can and do capture human biases. Many
of these biases, especially those that could potentially cause harm, are being
well-investigated. On the other hand, studies that infer and change human
personality traits inherited by these models have been scarce or non-existent.
Our work seeks to address this gap by exploring the personality traits of
several large-scale language models designed for open-ended text generation and
the datasets used for training them. We build on the popular Big Five factors
and develop robust methods that quantify the personality traits of these models
and their underlying datasets. In particular, we trigger the models with a
questionnaire designed for personality assessment and subsequently classify the
text responses into quantifiable traits using a Zero-shot classifier. Our
estimation scheme sheds light on an important anthropomorphic element found in
such AI models and can help stakeholders decide how they should be applied as
well as how society could perceive them. Additionally, we examined approaches
to alter these personalities, adding to our understanding of how AI models can
be adapted to specific contexts.
Related papers
- Few-Shot Detection of Machine-Generated Text using Style Representations [4.326503887981912]
Language models that convincingly mimic human writing pose a significant risk of abuse.
We propose to leverage representations of writing style estimated from human-authored text.
We find that features effective at distinguishing among human authors are also effective at distinguishing human from machine authors.
arXiv Detail & Related papers (2024-01-12T17:26:51Z) - A Sentence is Worth a Thousand Pictures: Can Large Language Models Understand Hum4n L4ngu4ge and the W0rld behind W0rds? [2.7342737448775534]
Large Language Models (LLMs) have been linked to claims about human-like linguistic performance.
We analyze the contribution of LLMs as theoretically informative representations of a target cognitive system.
We evaluate the models' ability to see the bigger picture, through top-down feedback from higher levels of processing.
arXiv Detail & Related papers (2023-07-26T18:58:53Z) - Foundational Models Defining a New Era in Vision: A Survey and Outlook [151.49434496615427]
Vision systems to see and reason about the compositional nature of visual scenes are fundamental to understanding our world.
The models learned to bridge the gap between such modalities coupled with large-scale training data facilitate contextual reasoning, generalization, and prompt capabilities at test time.
The output of such models can be modified through human-provided prompts without retraining, e.g., segmenting a particular object by providing a bounding box, having interactive dialogues by asking questions about an image or video scene or manipulating the robot's behavior through language instructions.
arXiv Detail & Related papers (2023-07-25T17:59:18Z) - Turning large language models into cognitive models [0.0]
We show that large language models can be turned into cognitive models.
These models offer accurate representations of human behavior, even outperforming traditional cognitive models in two decision-making domains.
Taken together, these results suggest that large, pre-trained models can be adapted to become generalist cognitive models.
arXiv Detail & Related papers (2023-06-06T18:00:01Z) - A Survey of Large Language Models [81.06947636926638]
Language modeling has been widely studied for language understanding and generation in the past two decades.
Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora.
To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
arXiv Detail & Related papers (2023-03-31T17:28:46Z) - Language Model Behavior: A Comprehensive Survey [5.663056267168211]
We discuss over 250 recent studies of English language model behavior before task-specific fine-tuning.
Despite dramatic increases in generated text quality as models scale to hundreds of billions of parameters, the models are still prone to unfactual responses, commonsense errors, memorized text, and social biases.
arXiv Detail & Related papers (2023-03-20T23:54:26Z) - Chain of Hindsight Aligns Language Models with Feedback [62.68665658130472]
We propose a novel technique, Chain of Hindsight, that is easy to optimize and can learn from any form of feedback, regardless of its polarity.
We convert all types of feedback into sequences of sentences, which are then used to fine-tune the model.
By doing so, the model is trained to generate outputs based on feedback, while learning to identify and correct negative attributes or errors.
arXiv Detail & Related papers (2023-02-06T10:28:16Z) - DALL-Eval: Probing the Reasoning Skills and Social Biases of
Text-to-Image Generation Models [73.12069620086311]
We investigate the visual reasoning capabilities and social biases of text-to-image models.
First, we measure three visual reasoning skills: object recognition, object counting, and spatial relation understanding.
Second, we assess the gender and skin tone biases by measuring the gender/skin tone distribution of generated images.
arXiv Detail & Related papers (2022-02-08T18:36:52Z) - Towards Zero-shot Language Modeling [90.80124496312274]
We construct a neural model that is inductively biased towards learning human languages.
We infer this distribution from a sample of typologically diverse training languages.
We harness additional language-specific side information as distant supervision for held-out languages.
arXiv Detail & Related papers (2021-08-06T23:49:18Z) - Personality Trait Detection Using Bagged SVM over BERT Word Embedding
Ensembles [10.425280599592865]
We present a novel deep learning-based approach for automated personality detection from text.
We leverage state of the art advances in natural language understanding, namely the BERT language model to extract contextualized word embeddings.
Our model outperforms the previous state of the art by 1.04% and, at the same time is significantly more computationally efficient to train.
arXiv Detail & Related papers (2020-10-03T09:25:51Z) - Limits of Detecting Text Generated by Large-Scale Language Models [65.46403462928319]
Some consider large-scale language models that can generate long and coherent pieces of text as dangerous, since they may be used in misinformation campaigns.
Here we formulate large-scale language model output detection as a hypothesis testing problem to classify text as genuine or generated.
arXiv Detail & Related papers (2020-02-09T19:53:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.