When One LLM Drools, Multi-LLM Collaboration Rules (2502.04506v1)

Abstract: This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status quo of relying solely on a single general-purpose LLM and argue for multi-LLM collaboration to better represent the extensive diversity of data, skills, and people. We first posit that a single LLM underrepresents real-world data distributions, heterogeneous skills, and pluralistic populations, and that such representation gaps cannot be trivially patched by further training a single LLM. We then organize existing multi-LLM collaboration methods into a hierarchy, based on the level of access and information exchange, ranging from API-level, text-level, logit-level, to weight-level collaboration. Based on these methods, we highlight how multi-LLM collaboration addresses challenges that a single LLM struggles with, such as reliability, democratization, and pluralism. Finally, we identify the limitations of existing multi-LLM methods and motivate future work. We envision multi-LLM collaboration as an essential path toward compositional intelligence and collaborative AI development.

• The paper demonstrates that multi-LLM collaboration can address single LLM underrepresentation in data, skills, and people.
• It introduces a taxonomy of collaboration strategies across API, text, logit, and weight-level interactions for dynamic model integration.
• The study shows improvements in reliability, cultural alignment, and task versatility, pointing to future modular AI system designs.

Overview of "When One LLM Drools, Multi-LLM Collaboration Rules"

The paper "When One LLM Drools, Multi-LLM Collaboration Rules" presents a compelling argument for transitioning from reliance on a single, monolithic LLM to a collaborative framework involving multiple LLMs. The authors assert that the prevalent approach of using a single LLM, despite its general-purpose design, underrepresents the extensive diversity inherent in real-world data, skills, and populations. This shortcoming cannot be easily remedied through further training of a single LLM. Instead, the paper advocates for a multi-LLM collaboration model, wherein multiple LLMs can engage in various forms of interaction and information exchange to address these underrepresentation issues more effectively.

Single LLM Limitations

The paper identifies three primary axes of underrepresentation with single LLMs: data, skills, and people. In terms of data, the authors note that LLMs are trained on static corpora, which may not encompass the full spectrum of real-world language variations, trends, and private/copyrighted texts necessary for personalization. For skills, the authors argue that no single LLM is Pareto-optimal across different tasks, highlighting the limitations in skills and task coverage when compared to specialized models. On the personal representation front, LLMs frequently struggle to reflect the diverse values and socio-cultural contexts of their user base, often reinforcing biases prevalent in the majorities represented within training corpora.

To address these deficiencies, the authors propose a taxonomy of multi-LLM collaboration strategies, organized by levels of model access: API-level, text-level, logit-level, and weight-level interactions. These collaborative strategies permit LLMs to more accurately mirror the heterogeneity of the real world and offer a compositional intelligence framework that bypasses the limitations of relying on a single LLM.

Taxonomy of Multi-LLM Collaboration

Levels of Model Access

1. API-Level Collaboration: This method utilizes various LLM APIs by routing queries dynamically among LLMs to exploit their respective strengths, tapping into both cost-effective and high-performance models depending on input specifics.
2. Text-Level Collaboration: LLMs contribute by exchanging generated outputs, often set in cooperative or competitive interactions like debates or system verification, to determine reliable resolutions for complex tasks.
3. Logit-Level Collaboration: At this level, the logit predictions of different LLMs are arithmetically combined, optimizing for more accurate next-token predictions.
4. Weight-Level Collaboration: Collaborating at the parameter level involves modular approaches like mixtures of experts or adapters, allowing weight combination to harness diverse LLM strengths.

Stages of Collaboration

The multi-LLM techniques extend through various stages of an LLM's lifecycle: pretraining, post-training, and inference. The authors focus significantly on inference-time solutions, which offer practical reuse and combination of existing LLMs across the collaboration continuum.

Promises and Challenges of Multi-LLM Systems

The proposed multi-LLM frameworks claim substantial improvements over single models in terms of factual reliability, alignment with pluralistic values, computational efficiency, adaptability, and privacy considerations. However, several challenges remain, such as establishing effective communication protocols inspired by human collaborative practices, ensuring robustness in composition and encapsulation, and advancing the interpretability of model interactions. The paper also highlights the need for democratic contributions to multi-LLM systems, allowing varied stakeholders to input based on their unique requirements.

Implications and Future Directions

Adopting a multi-LLM collaboration model has substantial implications for both theoretical and practical advancements in AI. It encourages collaborative AI development that aligns closely with diverse real-world needs and priorities. Moving forward, research should focus on formalizing more efficient collaboration protocols, improving multi-LLM interpretability, and ensuring that these systems can be integrated seamlessly into existing AI infrastructures. The paper's vision calls for a paradigmatic shift in AI development towards diverse, modular systems that enhance the collective intelligence of AI applications.

Overall, this paper serves as an essential contribution to the ongoing discussions around optimizing AI architectures for more comprehensive and nuanced solutions in language processing technologies.