Confidence-weighted integration of human and machine judgments for superior decision-making (2408.08083v1)

Abstract: LLMs have emerged as powerful tools in various domains. Recent studies have shown that LLMs can surpass humans in certain tasks, such as predicting the outcomes of neuroscience studies. What role does this leave for humans in the overall decision process? One possibility is that humans, despite performing worse than LLMs, can still add value when teamed with them. A human and machine team can surpass each individual teammate when team members' confidence is well-calibrated and team members diverge in which tasks they find difficult (i.e., calibration and diversity are needed). We simplified and extended a Bayesian approach to combining judgments using a logistic regression framework that integrates confidence-weighted judgments for any number of team members. Using this straightforward method, we demonstrated in a neuroscience forecasting task that, even when humans were inferior to LLMs, their combination with one or more LLMs consistently improved team performance. Our hope is that this simple and effective strategy for integrating the judgments of humans and machines will lead to productive collaborations.

• The paper shows that a confidence-weighted logistic regression model effectively integrates human and machine judgments to enhance decision-making accuracy.
• It establishes that calibrated confidence and diverse error patterns between experts and LLMs lead to superior team performance.
• It validates the approach on neuroscience benchmarks while offering a scalable, low-overhead alternative to Bayesian methods.

Confidence-Weighted Integration of Human and Machine Judgments for Superior Decision-Making

Felipe Yáñez, Xiaoliang Luo, Omar Valerio Minero, and Bradley C. Love presented an interesting paper exploring the synergistic potential of human-machine teaming in decision-making processes. The central hypothesis investigated was whether humans could improve the performance of teams where LLMs generally outperform them.

Introduction and Background

The rapid advancements in LLMs, demonstrated by models like GPT-4 and Llama 2, have resulted in machines achieving superhuman performance in a variety of domains, including complex linguistic and knowledge-intensive tasks. This has led to questions about the role of human judgment in collaborative environments where machines seemingly excel. However, there is an emerging possibility that humans, despite performing worse in isolation, can augment machine performance when forming a confident and diverse team.

The criterion for successful complementarity in such human-machine teams involves:

1. Calibration: Higher confidence correlates with higher accuracy.
2. Diversity: Errors made by humans and machines do not coincide.

Prior studies have utilized Bayesian approaches to integrate human and machine judgments, particularly in tasks like object recognition. However, such models are often computationally intensive and difficult to scale with additional team members.

Methods

Dataset and Task:

The research employed the BrainBench benchmark, which consists of 100 test cases derived from neuroscience paper abstracts, created either by experts or GPT-4 with human oversight. Participants and LLMs were asked to determine the correct versions from altered abstracts while indicating their confidence in their choices.

Participants:

171 neuroscience experts participated, evaluated on average three test cases each, providing both their selection and confidence ratings.

LLMs: Llama 2 chat models with 7B, 13B, and 70B parameters were employed, utilizing perplexity (PPL) to gauge their confidence. The logistic regression model was proposed to integrate these judgments in a computationally efficient manner.

Proposed Model

The confidence-weighted regression method integrates judgments from any number of teammates, leveraging logistic functions where the magnitude corresponds to confidence, and the sign depends on the decision choice. This model is emphasized for its simplicity and ease of implementation over the previously used Bayesian models.

Results

Analyses highlighted that human and LLM confidence was well-calibrated, with higher confidence yielding greater accuracy. Moreover, there was significant diversity in the errors made by humans and LLMs, satisfying the conditions for effective collaboration.

Performance evaluations showed:

• The logistic regression model effectively improved team performance by leveraging the combined strengths of humans and LLMs.
• The inclusion of a human teammate consistently improved the performance of LLM-only teams.
• The model’s statistical analysis concluded significant contributions from all teammates, both human and machine, in improving decision-making accuracy.

Initial comparisons demonstrated that the logistic regression model not only provided comparable if not superior performance to the Bayesian model but did so with significantly lower computational overhead.

Implications and Future Work

The paper presents substantial evidence that human-machine teams can achieve superior decision-making performance by leveraging confidence-weighted integration. This approach allows for more scalable and interpretable models, making it feasible to include multiple team members without prohibitive computational costs.

Future research might explore:

1. The integration of more varied LLMs to ensure that complementarity continues as LLM capabilities expand.
2. Extending the confidence-weighted model framework to more diverse datasets and tasks, thereby verifying its generalizability.
3. Investigating non-linear and interaction effects within the logistic model to better capture complex relationships among teammate judgments.

Conclusion

This paper robustly affirms the potential of human-LLM teams to surpass the performance of individual agents, provided their judgments are confidence-weighted and diverse. The new logistic regression model proposed here offers a scalable, computationally efficient framework that can democratize the integration of human and machine judgments across various domains and applications.

Data and Code Availability

Data and code related to this paper can be accessed via the respective repositories:

• Data: BrainBench GitHub Repository
• Code: haico GitHub Repository