Paper Summary
Title: Evaluating Human Trust in LLM-Based Planners: A Preliminary Study
Source: arXiv (0 citations)
Authors: Shenghui Chen et al.
Published Date: 2025-02-27
Podcast Transcript
Hello, and welcome to paper-to-podcast, where we take dense academic papers and turn them into something you can listen to while you’re pretending to do chores. Today, we're diving into a study titled "Evaluating Human Trust in Large Language Model-Based Planners: A Preliminary Study" from the mystical land of arXiv, authored by Shenghui Chen and colleagues. They published this gem on February 27, 2025, so it's hot off the academic presses!
Now, let's get into the nitty-gritty of this study, which explores the fascinating relationship between humans and artificial intelligence planners. Imagine this: humans trying to trust artificial intelligence planners as much as they trust their GPS, which occasionally thinks a U-turn across a river is a fantastic idea.
The study focused on how much humans trust planners based on Large Language Models compared to the classic planners that have been around since the days when dial-up internet was the height of technology. The researchers found that correctness was the kingpin of trust. So, if your artificial intelligence planner says, "Hey, let’s send that robot to the moon to fetch a ball," and it turns out to be correct, trust levels soar! The PDDL solver, a planner known for being on point like a cat on a laser dot, scored the highest. It managed to boost its trust score from 5.68 to a respectable 6.27 out of 7. Meanwhile, the Large Language Model planner, bless its circuits, saw its score drop slightly from 3.97 to 3.85. Ouch.
Interestingly, explanations from the Large Language Model planner did wonders for evaluation accuracy but didn’t do much to improve trust. It’s like someone giving you a spotless explanation of why they ate your lunch, but you’re still not handing over the cookie jar. However, plan refinement showed promise in increasing trust. It seems people like it when the planner throws a "Sure, but how about this?" curveball, even if the plan isn’t any more accurate. The research warns us of the risk of overtrust – that’s when you trust the Large Language Model too much, like trusting a cat to guard your fish tank.
In terms of methodology, the study had participants interact with different planners, including the Large Language Model Planner, the PDDL Solver, and two variations of the Large Language Model Planner with explanations and refinement. Participants were tasked with evaluating these planners using the "gripper problem." Picture a robot moving balls between rooms, which sounds like an Olympic sport for robots.
The researchers used a fancy within-subject design, which basically means each participant experienced all planner types, avoiding any bias like someone who tries all the flavors at an ice cream shop before declaring vanilla the best. Participants rated their trust using a scale that went from "I wouldn’t trust this planner to make toast" to "I’d let this planner babysit my guinea pig."
Now, onto the strengths of this research. It impressively juggled both subjective and objective metrics. They didn’t just ask, "Do you like it?" but also, "Is it actually any good?" This dual approach is like checking if you not only enjoy the pizza but if it’s cooked all the way through. The researchers also had a diverse participant pool. No guinea pigs were involved, but a good mix of humans, which is always a plus.
However, not everything was sunshine and rainbows. The study had a small sample size of 30 participants, which isn’t quite enough to start a flash mob or represent the entire human race. It also focused on a specific problem domain, the Gripper problem, which might not cover the wide array of challenges out there in the real world, like planning a surprise party where no one tells the guest of honor.
And let’s not forget, this was all done in a controlled environment, which, while great for science, doesn’t quite capture the chaos of the real world where planners might be operating. Like, can these planners handle the pressure of planning holiday dinners with family?
In terms of potential applications, these planners could revolutionize fields like robotics, logistics, healthcare, and even the legal world. Imagine a planner that helps streamline supply chain management or optimizes medical scheduling. Or, in the legal world, an artificial intelligence that explains why you're getting that speeding ticket. And for video game enthusiasts, these planners could create dynamic storylines that adapt to your every move, making your gaming experience as unpredictable as a cat video on the internet.
And that’s all we have for today, folks. Remember, trust your planners, but maybe not with your lunch. You can find this paper and more on the paper2podcast.com website. Until next time, happy planning!
Supporting Analysis
This study explores how human trust is influenced by planners based on Large Language Models (LLMs) compared to traditional methods. One of the standout findings is that correctness is the primary factor affecting both evaluation accuracy and trust. The PDDL solver, which is considered more accurate, scored the highest in both metrics. Interestingly, while explanations from the LLM planner enhanced evaluation accuracy, they didn't significantly impact trust levels. Instead, plan refinement showed potential for increasing trust, even without improving accuracy. This suggests that users might perceive planners as more trustworthy if they appear interactive or responsive to feedback, despite not necessarily performing better. The research also uncovered a risk of overtrust with LLMs, as refined plans using the same model might give an illusion of improvement. Trust scores indicated a notable difference, with the PDDL solver receiving a trust score increase from 5.68 to 6.27, while the LLM's trust slightly decreased from 3.97 to 3.85. These insights highlight the importance of focusing on the actual correctness of the plans rather than relying solely on explanations or iterative refinements to build user trust.
The research involved comparing human trust in language-model-based planners versus classic graph-search-based planners. Participants interacted with a Large Language Model (LLM) Planner, a PDDL Solver, and two variations of the LLM Planner that included explanations and refinement processes. The study used a within-subject design, meaning each participant experienced all planner types. The planners were tested using the "gripper problem" from the International Planning Competition, where a robot must move balls between rooms. Participants were presented with plans generated by each planner and asked to evaluate them. The study assessed trust using a 7-point Likert scale and measured evaluation accuracy based on how well participants judged the correctness of the plans. The planners' correctness was pre-determined: the PDDL Solver was always correct, while the LLM-based planners were correct 50% of the time. Explanations and refinement options were provided in some cases to see if they influenced trust or accuracy. Additionally, participants' propensity to trust technology was measured using a six-item scale to understand their general trust tendencies towards AI planners.
The research is most compelling in its exploration of how human trust is influenced by different types of AI planners, particularly those using large language models. By comparing these with classical planners, the study provides a nuanced view of trust dynamics in the context of AI-driven decision-making. A major strength of the research is its use of both subjective and objective metrics. The researchers employed trust questionnaires alongside evaluation accuracy to capture a holistic picture of user trust. This dual approach ensures a more comprehensive understanding of how users perceive and interact with AI systems. Another best practice is the use of a within-subject design, which enhances the reliability of the results by controlling for individual differences in participant responses. The randomized order of sessions and tasks helps mitigate potential biases and ordering effects, ensuring that the observed differences in trust and accuracy are attributable to the planners themselves rather than external variables. The study also includes a diverse participant pool and offers incentives for accurate responses, which likely increases participant engagement and data quality. These methodological choices demonstrate a commitment to rigor and validity in investigating the complexities of human-AI interaction.
The research may face limitations due to the relatively small sample size of 30 participants, which could affect the generalizability of the results. With such a limited number of participants, there is a risk that the findings may not accurately represent broader user populations. Additionally, the study focuses on a specific planning problem domain, the Gripper problem, which might not encompass the variety of challenges encountered in different real-world planning scenarios. This narrow scope could limit the applicability of the findings to other contexts or domains where planning systems are used. Another potential limitation is the use of a controlled experimental environment, which may not fully capture the complexities and unpredictability of real-world settings where planners operate. The reliance on simulated planning tasks and the absence of dynamic, real-time user interactions could overlook variables that influence trust in more practical applications. Furthermore, the study's approach to explanations and refinements might not fully explore alternative methods that could impact user trust differently. These limitations suggest that further research is needed, potentially involving larger, more diverse participant pools and varied planning scenarios, to strengthen the conclusions and explore the nuanced dynamics of trust in AI planners.
Potential applications for this research span various domains where planning systems play a vital role. In robotics, large language model (LLM)-based planners can improve autonomous navigation by generating adaptable action sequences, enhancing robots' ability to operate in dynamic environments. In logistics optimization, these planners could streamline supply chain management by developing efficient routes and schedules that respond to real-time changes. In healthcare, they could assist in medical scheduling, optimizing resource allocation, and patient appointment management. Moreover, the ability of LLMs to generate explanations and refine plans based on feedback could be invaluable in legal contexts, where understanding the rationale behind decisions is crucial. This capability can also be applied to interactive educational tools, where personalized learning paths can be generated and adjusted to cater to individual student needs. Additionally, the entertainment industry could leverage these models for procedural content generation in video games, creating dynamic storylines and environments that adapt to player actions. Overall, the flexibility and adaptability of LLM-based planners hold promise for any field that requires complex decision-making and planning under uncertainty, offering opportunities for enhanced efficiency and user interaction.