Summary Large Language Models as Superpositions of Perspectives arxiv.org
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Large Language Models (LLMs) are superpositions of perspectives that can adopt different values and traits, with GPT-3.5 and GPT-4 being more controllable, OpenAssistant having some controllability, and StableVicuna and StableLM lacking controllability, while various methods for inducing perspectives are explored.
Slides
Slide Presentation (8 slides)
Key Points
- Large Language Models (LLMs) are superpositions of perspectives with different values and personality traits.
- LLMs exhibit context-dependent values and traits that change based on the induced perspective.
- GPT-3.5 and GPT-4 exhibit higher controllability compared to other models.
- Different methods for inducing perspectives in LLMs have varying effectiveness.
- Highly controllable models exhibit consistent smoothness in their controllability.
- Building LLMs with specific values and controllability levels raises important scientific questions for further research.
- The limitations of standard evaluation methods for LLMs are discussed.
- The study explores the controllability of LLMs using different questionnaires and parameters.
Summaries
49 word summary
Large Language Models (LLMs) combine perspectives with different traits and values, with GPT-3.5 and GPT-4 being more controllable. OpenAssistant also has some controllability. StableVicuna and StableLM lack controllability. Various methods for inducing perspectives are explored. LLMs are superpositions of perspectives with the ability to adopt different values and traits.
64 word summary
Large Language Models (LLMs) are a combination of perspectives with different traits and values. GPT-3.5 and GPT-4 have higher controllability compared to other models, while OpenAssistant also demonstrates some controllability. StableVicuna and StableLM do not exhibit much controllability. Methods for inducing perspectives are explored, with varying effectiveness. LLMs should be seen as superpositions of perspectives with the ability to adopt different values and traits.
131 word summary
Large Language Models (LLMs) are a combination of perspectives with different traits and values, rather than having a single personality. The concept of perspective controllability is introduced to describe an LLM's ability to adopt different perspectives. Experiments show that GPT-3.5 and GPT-4 have higher controllability compared to other models, while OpenAssistant also demonstrates some controllability. StableVicuna and StableLM do not exhibit much controllability. Methods for inducing perspectives are explored, with varying effectiveness depending on the model and questionnaire used. Highly controllable models show consistent smoothness. The implications of this work are discussed, including building LLMs with specific values and controllability levels, evaluating the diversity of cultural perspectives, and the limitations of standard evaluation methods. LLMs should be seen as superpositions of perspectives with the ability to adopt different values and traits.
438 word summary
Large Language Models (LLMs) are not characterized by a single personality or set of values, but rather as a combination of perspectives with different traits and values. The concept of perspective controllability is introduced to describe an LLM's ability to adopt various perspectives with differing values and traits.
Qualitative and quantitative experiments are conducted to demonstrate the context-dependent nature of LLMs and study the controllability of different models. GPT-3.5 and GPT-4 show higher controllability compared to other models, while OpenAssistant also demonstrates some controllability. StableVicuna and StableLM do not exhibit much controllability.
Methods for inducing perspectives are explored, including implicit versus explicit induction, user message versus system message induction, and second person versus third person induction. The effectiveness of these methods varies depending on the model and questionnaire used.
The smoothness of controllability is also studied, with highly controllable models showing consistent smoothness. GPT-3.5, OpenAssistant, and StableVicuna exhibit increasing correspondence with perspective intensity in certain questionnaires.
The implications of this work are discussed in terms of building LLMs with specific values and controllability levels. Further research on evaluating the diversity and controllability of cultural perspectives in LLMs is highlighted. The limitations of standard evaluation methods for LLMs are also discussed.
In conclusion, LLMs should be seen as superpositions of perspectives, with the ability to adopt different values and personality traits based on the induced perspective. The concept of perspective controllability provides a framework for understanding and studying the controllability of LLMs. This work contributes to the ongoing discussion on the values and controllability of LLMs and raises important scientific questions for further research.
The study explores the controllability of LLMs and investigates how different perspectives can be induced. Experiments using various questionnaires assess the controllability of LLMs, manipulating parameters such as message type, perspective intensity, and person.
Both implicit and explicit settings effectively induce perspectives, but the explicit setting provides clearer and more consistent results. User messages and system messages show slight differences in expressed values. 2nd person and 3rd person prompts also result in different expressed values.
Increasing perspective intensity leads to higher values being expressed by LLMs. The study provides background information on values and personality traits outlined by Schwartz, Hofstede, and the Big Five personality traits model.
Additional experiments involve prompting models with different Wikipedia articles and studying the effect of RLHF fine-tuning on controllability. Different topics can induce different values in models, and RLHF fine-tuning can affect controllability.
The study provides insights into the controllability of LLMs and emphasizes the importance of considering various parameters when inducing perspectives. The findings have implications for understanding the behavior of LLMs and their applications in various domains.
490 word summary
Large Language Models (LLMs) are not characterized by a single personality or set of values, but rather as a combination of perspectives with different traits and values. LLMs exhibit context-dependent values and traits that change based on the perspective induced. The concept of perspective controllability is introduced to describe an LLM's ability to adopt various perspectives with differing values and traits.
Qualitative and quantitative experiments are conducted to demonstrate the context-dependent nature of LLMs and study the controllability of different models. GPT-3.5 and GPT-4 show higher controllability compared to other models, while OpenAssistant also demonstrates some controllability. StableVicuna and StableLM do not exhibit much controllability.
Methods for inducing perspectives are explored, including implicit versus explicit induction, user message versus system message induction, and second person versus third person induction. The effectiveness of these methods varies depending on the model and questionnaire used.
The smoothness of controllability is also studied, with highly controllable models showing consistent smoothness. GPT-3.5, OpenAssistant, and StableVicuna exhibit increasing correspondence with perspective intensity in certain questionnaires.
The implications of this work are discussed in terms of building LLMs with specific values and controllability levels. The question of representing a large diversity of cultures versus aligning a model with one set of values is explored. Further research on evaluating the diversity and controllability of cultural perspectives in LLMs is highlighted. The limitations of standard evaluation methods for LLMs are also discussed.
In conclusion, LLMs should be seen as superpositions of perspectives, with the ability to adopt different values and personality traits based on the induced perspective. The concept of perspective controllability provides a framework for understanding and studying the controllability of LLMs. This work contributes to the ongoing discussion on the values and controllability of LLMs and raises important scientific questions for further research.
The study explores the controllability of LLMs and investigates how different perspectives can be induced. Experiments using various questionnaires assess the controllability of LLMs, manipulating parameters such as message type, perspective intensity, and person.
Both implicit and explicit settings effectively induce perspectives, but the explicit setting provides clearer and more consistent results. User messages and system messages show slight differences in expressed values. 2nd person and 3rd person prompts also result in different expressed values.
Increasing perspective intensity leads to higher values being expressed by LLMs. The study provides background information on values and personality traits outlined by Schwartz, Hofstede, and the Big Five personality traits model.
Additional experiments involve prompting models with different Wikipedia articles and studying the effect of RLHF fine-tuning on controllability. Different topics can induce different values in models, and RLHF fine-tuning can affect controllability.
The study provides insights into the controllability of LLMs and emphasizes the importance of considering various parameters when inducing perspectives. The findings have implications for understanding the behavior of LLMs and their applications in various domains. The open-source release of the code used in the study allows for further exploration and replication of the experiments.
934 word summary
Large Language Models (LLMs) are often mistakenly perceived as having a personality or set of values. However, LLMs can be better understood as superpositions of perspectives with different values and personality traits. Unlike humans, who tend to have consistent values and traits across contexts, LLMs exhibit context-dependent values and traits that change based on the induced perspective. The concept of perspective controllability is introduced to describe an LLM's ability to adopt various perspectives with differing values and traits.
Qualitative experiments are conducted to demonstrate that LLMs express different values when those values are implied in the prompt, as well as when they are not obviously implied. This highlights the context-dependent nature of LLMs. Quantitative experiments are then performed to study the controllability of different models, the effectiveness of various methods for inducing perspectives, and the smoothness of the models' drivability.
The controllability of different models is compared, including GPT-4, GPT-3.5, OpenAssistant, StableVicuna, and StableLM. It is found that GPT-3.5 and GPT-4 exhibit higher controllability compared to other models. OpenAssistant also demonstrates some controllability, while StableVicuna and StableLM do not exhibit much controllability.
Methods for inducing perspectives are explored, including implicit versus explicit induction, user message versus system message induction, and second person versus third person induction. It is observed that the effectiveness of these methods varies depending on the model and the questionnaire used.
The smoothness of controllability is also studied. It is found that highly controllable models exhibit consistent smoothness in their controllability. GPT-3.5, OpenAssistant, and StableVicuna show increasing correspondence with perspective intensity in certain questionnaires.
The implications of this work are discussed in terms of building LLMs with specific values and controllability levels. The question of whether to represent a large diversity of cultures or align a model with one set of values is explored. The need for further research on evaluating the diversity and controllability of cultural perspectives in LLMs is highlighted. The limitations of standard evaluation methods for LLMs are also discussed, as these methods may not capture the context-dependent nature of values and traits expressed by LLMs.
In conclusion, LLMs should be seen as superpositions of perspectives, with the ability to adopt different values and personality traits based on the induced perspective. The concept of perspective controllability provides a framework for understanding and studying the controllability of LLMs. This work contributes to the ongoing discussion on the values and controllability of LLMs and raises important scientific questions for further research.
The study explores the controllability of large language models (LLMs) and investigates how different perspectives can be induced in these models. The authors conduct experiments using various questionnaires to assess the controllability of LLMs, including the Schwartz Value Survey (PVQ), Hofstede's Value Survey Module (VSM), and the International Personality Item Pool (IPIP).
The experiments involve manipulating different parameters such as the message type (system or user message), perspective intensity, and person (2nd or 3rd person). The results show that the controllability of LLMs varies depending on these parameters.
In terms of message type, the authors compare the implicit and explicit settings for inducing a perspective. The implicit setting involves using a fictional character (Sauron from The Lord of the Rings) to induce a perspective, while the explicit setting involves explicitly outlining the target values (Power, Achievement, and Self-Enhancement). The results indicate that both settings can effectively induce perspectives, but the explicit setting provides clearer and more consistent results.
The authors also compare the use of user messages and system messages to induce perspectives. In the user message setting, the whole prompt is sent together to the LLM, while in the system message setting, the prompt is separated into two parts. The results show that there are slight differences in the expressed values between these two settings, with some values being higher in user messages and others being higher in system messages.
Another parameter explored is the use of 2nd person and 3rd person prompts to induce perspectives. In the 2nd person setting, the prompt is induced by the sentence "You are a person," while in the 3rd person setting, the prompt is induced by the sentence "The following is a questionnaire (with answers) given to a person." The results indicate that there are differences in the expressed values between these two settings, with some values being higher in 2nd person prompts and others being higher in 3rd person prompts.
The authors also investigate the effect of perspective intensity on the controllability of LLMs. Three levels of perspective intensity are examined: slight, more, and extremely more. The results show that increasing the perspective intensity leads to higher values being expressed by the LLMs.
In addition to examining the controllability of LLMs, the authors provide background information on the values and personality traits outlined by Schwartz, Hofstede, and the Big Five personality traits model. They discuss each of these values and traits in detail, providing a comprehensive overview of their definitions and characteristics.
The study also includes additional experiments to further explore the controllability and robustness of LLMs. These experiments involve prompting the models with different Wikipedia articles and studying the effect of RLHF fine-tuning on controllability. The results show that different topics can induce different values in the models and that RLHF fine-tuning can affect the controllability of LLMs.
Overall, the study provides valuable insights into the controllability of LLMs and highlights the importance of considering various parameters when inducing perspectives in these models. The findings have implications for understanding the behavior of LLMs and their potential applications in various domains. The open-source release of the code used in the study allows for further exploration and replication of the experiments.