The Misleading Success of Simulating Social Interactions With LLMs

Temel Kavramlar

The author argues that simulating social interactions with LLMs using an omniscient perspective leads to misleading success, as it diverges from the non-omniscient human interactions. This highlights the fundamental challenge of addressing information asymmetry for LLM-based agents.

Özet

Recent research explores the limitations of simulating social interactions with Large Language Models (LLMs) using an omniscient perspective. The study compares SCRIPT mode, where a single LLM orchestrates interactions, and AGENTS mode, where two LLMs engage in interaction with information asymmetry. Findings reveal that while SCRIPT simulations achieve higher goal completion rates and naturalness, they overestimate the abilities of LLMs due to direct access to internal states. In contrast, AGENTS simulations struggle with achieving goals and naturalness due to information asymmetry. Finetuning models on SCRIPT simulations improves goal completion but introduces biases from SCRIPT strategies into AGENTS models. Recommendations are provided for reporting and evaluating LLM simulations to encourage more careful considerations and transparency.

İstatistikler

"Our experiments show that interlocutors simulated omnisciently are much more successful at accomplishing social goals compared to non-omniscient agents."
"Furthermore, we demonstrate that learning from omniscient simulations improves the apparent naturalness of interactions but scarcely enhances goal achievement in cooperative scenarios."
"Our findings suggest that the success of LLMs in simulating social interactions with SCRIPT mode can be misleading."
"SCRIPT mode significantly overestimates the ability of LLM-agents to achieve social goals."
"The finetuned model struggles to complete the social goals in the AGENTS mode by following the strategies of SCRIPT simulations."

Alıntılar

"Our findings indicate that addressing information asymmetry remains a fundamental challenge for LLM-based agents."
"SCRIPT mode vastly overestimates agents' ability by having full access to interlocutor's knowledge."
"Finetuning models on SCRIPT simulations improve selectively on goal completion but introduce biases into AGENTS models."

Önemli Bilgiler Şuradan Elde Edildi

Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs

by Xuhui Zhou,Z... : arxiv.org 03-11-2024

https://arxiv.org/pdf/2403.05020.pdf

Daha Derin Sorular

How can future research address biases introduced by finetuning models on SCRIPT simulations?

Future research can address biases introduced by finetuning models on SCRIPT simulations by implementing strategies to mitigate the negative effects. One approach could involve incorporating diverse and challenging scenarios in the training data to expose the model to a wider range of interactions, thus reducing bias towards specific patterns observed in SCRIPT mode. Additionally, researchers could explore techniques such as adversarial training or regularization methods to encourage more generalized learning and prevent overfitting to the omniscient perspective present in SCRIPT simulations. By diversifying the training data and introducing mechanisms that promote robustness and generalization, future research can help reduce biases introduced during finetuning.

What are potential strategies for improving naturalness and goal achievement in AGENTS mode without compromising information asymmetry?

To enhance naturalness and goal achievement in AGENTS mode without compromising information asymmetry, researchers can consider several strategies:

Contextual Prompting: Providing contextual prompts that guide LLMs towards generating responses that align with human-like conversational norms while respecting information boundaries.
Multi-Turn Interactions: Introducing mechanisms for multi-turn interactions where agents build upon previous utterances, mimicking real-world dialogue flow.
Theory of Mind Modeling: Incorporating elements of theory of mind into LLMs' architecture to enable them to infer mental states of interlocutors indirectly rather than through direct access.
Feedback Mechanisms: Implementing feedback loops where models receive input based on their performance in achieving social goals naturally, allowing them to adapt and improve over time.
Diverse Training Data: Ensuring that the training data encompasses a wide variety of social scenarios with varying levels of complexity to enhance adaptability.

How might incorporating turn-taking and other aspects of human social interactions enhance simulation accuracy?

Incorporating turn-taking and other aspects of human social interactions can significantly enhance simulation accuracy by making simulated conversations more realistic:

Turn-Taking Dynamics: Including turn-taking rules where agents take alternating speaking turns improves conversation flow and coherence.
Non-Verbal Cues: Integrating non-verbal cues like gestures or expressions enhances communication richness similar to face-to-face interactions.
Memory Retention: Allowing agents to retain information from past exchanges enables continuity across dialogues, reflecting real-world memory dynamics.
Asynchronous Interactions: Simulating asynchronous communication modes adds depth by considering delays between messages typical in real-life conversations.
5Adaptation Strategies: Implementing adaptive strategies based on inferred mental states allows agents to adjust their responses dynamically accordingto perceived intentions or emotions.

By incorporating these elements into simulation frameworks, researchers can create more authentic representations of human social interactions leadingto improved accuracy andrelevancein AI applications."