Key Points

1. Inference-time techniques are gaining traction as effective methods for improving model capabilities, including generation ensembling, ranking, fusion, repeated sampling, and others.

2. Existing inference-time architectures like Mixture-of-Agents and LLM-Blender still lack generalization beyond their target tasks, motivating the need to better understand the utilities and interactions of different inference-time techniques.

3. The search space of inference-time architectures is large, requiring efficient architecture search algorithms to maximize performance across diverse benchmarks.

4. The authors evaluated a comprehensive set of existing and new inference-time techniques across instruction-following, reasoning, and coding tasks using open-source and closed-source models.

5. The authors found that techniques like candidate fusion, critiquing, and verification are particularly effective when combined, outperforming the oracle best candidate from individual responses.

6. The authors introduced Archon, a modular framework that leverages Bayesian optimization to automatically search for optimized inference-time architectures for target benchmarks.

7. Archon architectures outperform frontier models like GPT-4 and Claude 3.5 Sonnet by 15.1 percentage points on average across the evaluated benchmarks.

8. Even when using only open-source models, Archon architectures on average surpass single-call state-of-the-art LLMs by 11.2 percentage points.

9. The authors make the Archon framework and datasets publicly available on Github to advance research on inference-time architectures.

Summary

The research paper presents Archon, a modular framework designed to optimize inference-time architectures by integrating various techniques such as ensembling, ranking, fusion, critique, verification, and unit test generation. The framework aimed to address challenges related to efficiently and automatically searching the space of model choices, inference-time techniques, and their compositions. Archon leverages a diverse set of large language models (LLMs) and inference-time techniques to create systems that outperform existing models on various benchmarks such as MT-Bench, AlpacaEval 2.0, Arena-Hard-Auto, MixEval, MixEval Hard, MATH, and CodeContests.

Development and Performance Evaluation
The paper discusses the development of Archon and evaluates its performance across different types of tasks, specifically instruction-following, reasoning, and coding. The authors compare the performance of Archon architectures with existing state-of-the-art closed-source LLMs and other inference-time architectures. They also explore the concept of task-specific and general-purpose Archon architectures and analyze the performance across various evaluation datasets.

Performance Comparison with Existing LLMs
The results of the research demonstrate that Archon consistently matches or exceeds the performance of leading closed-source LLMs, such as GPT-4 and Claude-3.5-Sonnet, while using only open-source models across diverse benchmarks. The paper highlights the potential of Archon and the ITAS algorithms in advancing the development of high-performing and generally capable inference-time architectures.

Benefits of Utilizing Inference-time Compute
The findings underscore the benefits of utilizing inference-time compute towards utilizing multiple LLMs and additional operations, leading to amplified benefits that scale with additional inference calls. The paper also emphasizes the automatic approach for iteratively testing different Archon architectures with ITAS, highlighting the potential to guarantee the optimal configuration given enough exploration steps.

Concluding Remarks
In conclusion, the research paper discusses the development and performance evaluation of Archon, highlighting its potential to optimize inference-time architectures and outperform existing state-of-the-art models. Archon’s framework and datasets are publicly available for further exploration and development.

Framework Design and Aim
The research paper presents the Archon framework, which aims to improve the capabilities of large language models (LLMs) by combining and optimizing various inference-time techniques. The framework is modular and can leverage different LLMs and inference-time techniques to create systems that outperform frontier models on multiple benchmarks. The paper discusses the challenges of efficiently and automatically searching the space of model choices, inference-time techniques, and their compositions, and how Archon addresses these challenges.

Leveraging Inference-time Techniques
The Archon framework leverages a diverse set of LLMs and inference-time techniques, including generation ensembling, repeated sampling, ranking, fusion, critiquing, verification, and unit testing. It is capable of combining and optimizing these techniques to enhance the performance of LLM systems on various benchmarks. The paper further discusses the rules of Archon construction, detailing the allowed combinations of each LLM component. The research findings emphasize the effectiveness of Archon across different architectures, including all-source, open-source, instruction-following, and code contest architectures. Through the use of ITAS (Inference-Time AutoML Search), the paper demonstrates the effectiveness of Archon's configurations across various benchmarks. Additionally, the research explores the impact of different inference budgets, model sizes, and costs on Archon’s performance, as well as the architecture's strong performance on complete evaluation datasets after ITAS optimization.

Utilization of Bayesian Optimization
Moreover, the paper discusses the use of Bayesian Optimization as a key component of Archon, explaining its use in sequential design strategies for global optimization of black-box functions that are expensive to evaluate. The research provides insights into the initialization, model building, acquisition, evaluation, and update steps of Bayesian Optimization, as well as the use of Gaussian Process as a surrogate model and various acquisition functions such as Expected Improvement and Probability of Improvement.

Benchmark Performance Comparison
The findings conclude with detailed comparisons and analyses of Archon's performance on various benchmarks, including MT Bench, Arena-Hard-Auto, MixEval, MixEval Hard, MATH, CodeContests, GSM8K, TriviaQA, DROP, BBH, and AGIEval. The results showcase the efficacy of Archon in outperforming single-call state-of-the-art LLMs across multiple benchmarks.

Overall Conclusion
Overall, the paper provides comprehensive insights into the development, capabilities, and performance of the Archon framework in enhancing the capabilities of large language models through the integration of various inference-time techniques, autoML search, and Bayesian Optimization.

Reference: https://arxiv.org/abs/2409.15254