Framing Effects in Independent-Agent Large Language Models: A Cross-Family Behavioral Analysis
arXiv:2603.19282v1 Announce Type: cross Abstract: In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, we examine how prompt framing influences decisions in a threshold voting task involving individual-group interest conflict. Two logically equivalent prompts with different framings were tested across diverse LLM families under isolated trials. Results show that prompt framing significantly influences choice distributions, often shifting preferences toward risk-averse options. Surface linguistic cues can even override logically equivalent formulations. This suggests that observed behavior reflects a tendency consistent with a preference for instrumental rather than cooperative rationality when success requires risk-bearing. The findings highlight framing effects as a significant bias source in non-interacting multi-agent LLM deployments, informing alignment and prompt design.
arXiv:2603.19282v1 Announce Type: cross Abstract: In many real-world applications, large language models (LLMs) operate as independent agents without interaction, thereby limiting coordination. In this setting, we examine how prompt framing influences decisions in a threshold voting task involving individual-group interest conflict. Two logically equivalent prompts with different framings were tested across diverse LLM families under isolated trials. Results show that prompt framing significantly influences choice distributions, often shifting preferences toward risk-averse options. Surface linguistic cues can even override logically equivalent formulations. This suggests that observed behavior reflects a tendency consistent with a preference for instrumental rather than cooperative rationality when success requires risk-bearing. The findings highlight framing effects as a significant bias source in non-interacting multi-agent LLM deployments, informing alignment and prompt design.
Executive Summary
This study examines the impact of prompt framing on large language models (LLMs) operating as independent agents in a threshold voting task involving individual-group interest conflict. The results show that prompt framing significantly influences choice distributions, often shifting preferences toward risk-averse options. The findings highlight framing effects as a significant bias source in non-interacting multi-agent LLM deployments, informing alignment and prompt design. This research has implications for the development of more robust and reliable LLMs, particularly in applications where success requires risk-bearing. The study's methodology, which tests diverse LLM families under isolated trials, provides valuable insights into the behavior of LLMs in complex decision-making scenarios.
Key Points
- ▸ Prompt framing significantly influences choice distributions in LLMs
- ▸ Framing effects can override logically equivalent formulations
- ▸ Risk-averse preferences are more common in LLMs under individual-group interest conflict
Merits
Strength in Methodology
The study's use of diverse LLM families and isolated trials provides a robust and generalizable test of framing effects in LLMs.
Insight into LLM Behavior
The study's findings highlight the importance of considering framing effects in the design of LLMs, particularly in applications where success requires risk-bearing.
Demerits
Limitation in Generalizability
The study's findings may not generalize to LLMs operating in interactive or multi-agent settings, where coordination and cooperation may play a more significant role.
Need for Further Research
The study's results raise important questions about the mechanisms underlying framing effects in LLMs, which require further investigation to fully understand.
Expert Commentary
The study's findings have significant implications for the development of more robust and reliable LLMs, particularly in applications where success requires risk-bearing. The results highlight the importance of considering framing effects in the design of LLMs and suggest that LLMs should be designed to mitigate the influence of framing effects. The study's methodology provides valuable insights into the behavior of LLMs in complex decision-making scenarios and raises important questions about the mechanisms underlying framing effects in LLMs. Further research is needed to fully understand the implications of these findings and to develop more effective strategies for mitigating the influence of framing effects in LLMs.
Recommendations
- ✓ Recommendation 1: Further research should be conducted to investigate the mechanisms underlying framing effects in LLMs and to develop more effective strategies for mitigating their influence.
- ✓ Recommendation 2: LLM designers should consider the framing effects of prompts and design their models to mitigate the influence of framing effects, particularly in applications where success requires risk-bearing.
Sources
Original: arXiv - cs.AI
