RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
- URL: http://arxiv.org/abs/2503.10657v2
- Date: Tue, 20 May 2025 14:59:21 GMT
- Title: RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
- Authors: Zhongzhan Huang, Guoming Ling, Yupei Lin, Yandong Chen, Shanshan Zhong, Hefeng Wu, Liang Lin,
- Abstract summary: We show a novel model-level scaling up phenomenon in routing large language models (LLMs)<n>This improvement can even surpass the performance of the best single model in the pool and many existing strong LLMs.<n>We introduce RouterEval, a benchmark tailored for router research, which includes over 200,000,000 performance records for 12 popular LLM evaluations.
- Score: 45.93874913792025
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input. In this paper, our comprehensive analysis with more than 8,500 LLMs reveals a novel model-level scaling up phenomenon in Routing LLMs, i.e., a capable router can significantly enhance the performance of this paradigm as the number of candidates increases. This improvement can even surpass the performance of the best single model in the pool and many existing strong LLMs, confirming it a highly promising paradigm. However, the lack of comprehensive and open-source benchmarks for Routing LLMs has hindered the development of routers. In this paper, we introduce RouterEval, a benchmark tailored for router research, which includes over 200,000,000 performance records for 12 popular LLM evaluations across various areas such as commonsense reasoning, semantic understanding, etc., based on over 8,500 various LLMs. Using RouterEval, extensive evaluations of existing Routing LLM methods reveal that most still have significant room for improvement. See https://github.com/MilkThink-Lab/RouterEval for all data, code and tutorial.
Related papers
- Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
arXiv Detail & Related papers (2025-05-26T15:27:55Z) - How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities [62.474732677086855]
Large language model (LLM) routing has emerged as a crucial strategy for balancing computational costs with performance.
We propose the DSC benchmark: Diverse, Simple, and Categorized, an evaluation framework that categorizes router performance across a broad spectrum of query types.
arXiv Detail & Related papers (2025-03-20T19:52:30Z) - Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing [64.38277118982698]
Large Language Models (LLMs) have demonstrated human-like instruction-following abilities.<n>In this work, we explore how to route the best-performing LLM for each instruction to achieve better overall performance.<n>We develop a new paradigm, constructing capability instructions with model capability representation, user instruction, and performance inquiry prompts to assess the performance.
arXiv Detail & Related papers (2025-02-24T16:10:53Z) - Universal Model Routing for Efficient LLM Inference [72.65083061619752]
We consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time.<n>We propose a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts.<n>We prove that these strategies are estimates of a theoretically optimal routing rule, and provide an excess risk bound to quantify their errors.
arXiv Detail & Related papers (2025-02-12T20:30:28Z) - A Comprehensive Analysis on LLM-based Node Classification Algorithms [21.120619437937382]
We develop a comprehensive and testbed for node classification using Large Language Models (LLMs)
It includes ten datasets, eight LLM-based algorithms, and three learning paradigms, and is designed for easy extension with new methods and datasets.
We conduct extensive experiments, training and evaluating over 2,200 models, to determine the key settings that affect performance.
Our findings uncover eight insights, e.g., LLM-based methods can significantly outperform traditional methods in a semi-supervised setting, while the advantage is marginal in a supervised setting.
arXiv Detail & Related papers (2025-02-02T15:56:05Z) - Smoothie: Label Free Language Model Routing [39.88041397482366]
Large language models (LLMs) are increasingly used in applications where LLM inputs may span many different tasks.<n>We propose Smoothie, a weak supervision-inspired routing approach that requires no labeled data.<n>We find that Smoothie's LLM quality-scores correlate with ground-truth model quality.
arXiv Detail & Related papers (2024-12-06T01:06:37Z) - RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models [24.113223576205932]
We show that query-based Router by Dual Contrastive learning (DC) is effective in assembling large language models (LLMs)
DC is effective in assembling LLMs and largely outperforms individual top-performing LLMs as well as existing routing methods on both in-distribution and out-of-distribution tasks.
arXiv Detail & Related papers (2024-09-30T02:31:40Z) - TensorOpera Router: A Multi-Model Router for Efficient LLM Inference [27.2803289964386]
TO-lemma is a non-monolithic LLM querying system.
It seamlessly integrates various LLM experts into a single query interface.
It dynamically routes incoming queries to the most high-performant expert based on query's requirements.
arXiv Detail & Related papers (2024-08-22T11:57:07Z) - RouterBench: A Benchmark for Multi-LLM Routing System [25.515453832224804]
No single model can optimally address all tasks and applications, particularly when balancing performance with cost.
This limitation has led to the development of LLM routing systems, which combine the strengths of various models to overcome the constraints of individual LLMs.
We present RouterBench, a novel evaluation framework designed to systematically assess the efficacy of LLM routing systems.
arXiv Detail & Related papers (2024-03-18T17:59:04Z) - Large Language Models: A Survey [66.39828929831017]
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks.<n>LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
arXiv Detail & Related papers (2024-02-09T05:37:09Z) - Knowledge Fusion of Large Language Models [73.28202188100646]
This paper introduces the notion of knowledge fusion for large language models (LLMs)
We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM.
Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
arXiv Detail & Related papers (2024-01-19T05:02:46Z) - LLMRec: Benchmarking Large Language Models on Recommendation Task [54.48899723591296]
The application of Large Language Models (LLMs) in the recommendation domain has not been thoroughly investigated.
We benchmark several popular off-the-shelf LLMs on five recommendation tasks, including rating prediction, sequential recommendation, direct recommendation, explanation generation, and review summarization.
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
arXiv Detail & Related papers (2023-08-23T16:32:54Z) - LVLM-eHub: A Comprehensive Evaluation Benchmark for Large
Vision-Language Models [55.304181390027274]
This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (LVLM-eHub)
Our LVLM-eHub consists of $8$ representative LVLMs such as InstructBLIP and MiniGPT-4, which are thoroughly evaluated by a quantitative capability evaluation and an online arena platform.
The study reveals several innovative findings. First, instruction-tuned LVLM with massive in-domain data such as InstructBLIP heavily overfits many existing tasks, generalizing poorly in the open-world scenario.
arXiv Detail & Related papers (2023-06-15T16:39:24Z) - LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of
Large Language Models [75.25782573728677]
This paper presents a framework for adapter-based parameter-efficient fine-tuning (PEFT) of language models (LLMs)
The framework includes state-of-the-art open-access LLMs such as LLaMA, BLOOM, and GPT-J, as well as widely used adapters such as Series adapters, Parallel adapter, Prompt-based learning and Reparametrization-based methods.
We evaluate the effectiveness of the adapters on fourteen datasets from two different reasoning tasks, Arithmetic Reasoning and Commonsense Reasoning.
arXiv Detail & Related papers (2023-04-04T16:31:37Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.