IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)
Copied to clipboard
| Challenge: | Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. |
| Approach: | They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. |
| Outcome: | The proposed framework outperforms baseline methods in terms of effectiveness and interpretability. |
Similar Papers
Generalising LLM Routing using Past Performance Retrieval: A Few-Shot Router is Sufficient (2026.eacl-srw)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) use a single LLM to perform tasks. |
| Approach: | They propose a meta-evaluation framework that predicts per-model performance for new queries by retrieving similar past queries and reweighting model scores with lightweight attention. |
| Outcome: | The proposed framework matches the quality–cost trade-offs of generalisable routers across five routing benchmarks. |
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers. |
| Approach: | They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations . |
| Outcome: | The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs. |
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference (2024.emnlp-industry)
Copied to clipboard
Dimitris Stripelis, Zhaozhuo Xu, Zijian Hu, Alay Shah, Han Jin, Yuhang Yao, Jipeng Zhang, Tong Zhang, Salman Avestimehr, Chaoyang He
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable performance across a diverse set of domain-specific tasks. |
| Approach: | They propose a non-monolithic LLM querying system that seamlessly integrates various LLM experts into a single query interface and dynamically routes incoming queries to the most high-performant expert based on query’s requirements. |
| Outcome: | The proposed model improves query efficiency by 40% and costs by 30% while maintaining or enhancing model performance by 10%. |
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)
Copied to clipboard
Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu
| Challenge: | Large language model (LLM) routing assigns each query to the best suitable model from an ensemble. |
| Approach: | They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation . |
| Outcome: | The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing. |
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities (2026.eacl-long)
Copied to clipboard
| Challenge: | Large language model (LLM) routing has emerged as a promising solution to balancing computational costs and performance. |
| Approach: | They propose a framework that categorizes router performance across a broad spectrum of query types . large language models have revolutionized natural language processing . |
| Outcome: | The proposed framework categorizes router performance across a broad spectrum of query types . it integrates privacy and safety assessments to reveal hidden risks . |
GPT-Fathom: Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 and Beyond (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing LLM leaderboards often reference scores reported in other papers without consistent settings and prompts, which may encourage cherry-picking favored settings and for better results. |
| Approach: | They propose an open-source and reproducible LLM evaluation suite built on top of OpenAI Evals that systematically evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
| Outcome: | The evaluation suite is built on top of OpenAI Evals and evaluates 10+ leading LLMs and OpenAI’s legacy models on 20+ curated benchmarks across 7 capability categories. |
MasRouter: Learning to Route LLMs for Multi-Agent Systems (2025.acl-long)
Copied to clipboard
| Challenge: | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been demonstrated to push the boundaries of LLM capabilities, yet they often face significant costs and challenges in dynamic LLM selection. |
| Approach: | They propose a multi-agent system routing solution that integrates all components of MAS into a unified routing framework. |
| Outcome: | The proposed solution is high-performing, cost-effective, and efficient . it reduces overhead by up to 52.07 compared to current methods on HumanEval . |
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)
Copied to clipboard
Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella
| Challenge: | Existing techniques like distillation and pruning are not efficient for large language models. |
| Approach: | They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately. |
| Outcome: | The proposed framework improves system throughput while minimizing performance degradation. |
SEMIROUTER: Sparse-Data Enhanced Routing for Adaptive Multi-LLM System (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing routing methods suffer from poor scalability and dependence on datasets for training . energy footprint is also considered in the decision to implement our new LLM routing framework . |
| Approach: | They propose a new LLM routing framework that dynamically allocates queries to the most appropriate LLM. |
| Outcome: | The proposed method improves data efficiency, adaptability, and routing accuracy compared to existing methods. |
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing benchmarks focus on specific predefined model abilities, such as world knowledge, reasoning, etc., making it difficult for users to determine which LLM best suits their particular needs. |
| Approach: | They propose to evaluate large language models from a user-centric perspective and use real-world use cases to identify their effectiveness under distinct intents. |
| Outcome: | The proposed benchmarks achieve a correlation between human preference and the user-reported scenarios and human intents. |