Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
124 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Evalverse: Unified and Accessible Library for Large Language Model Evaluation (2404.00943v2)

Published 1 Apr 2024 in cs.CL and cs.AI

Abstract: This paper introduces Evalverse, a novel library that streamlines the evaluation of LLMs by unifying disparate evaluation tools into a single, user-friendly framework. Evalverse enables individuals with limited knowledge of artificial intelligence to easily request LLM evaluations and receive detailed reports, facilitated by an integration with communication platforms like Slack. Thus, Evalverse serves as a powerful tool for the comprehensive assessment of LLMs, offering both researchers and practitioners a centralized and easily accessible evaluation framework. Finally, we also provide a demo video for Evalverse, showcasing its capabilities and implementation in a two-minute format.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Qwen technical report. arXiv preprint arXiv:2309.16609.
  3. Open llm leaderboard. Hugging Face.
  4. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology.
  5. Benchmarking large language models in retrieval-augmented generation. arXiv preprint arXiv:2309.01431.
  6. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457.
  7. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168.
  8. A framework for few-shot language model evaluation.
  9. Legalbench: Prototyping a collaborative benchmark for legal reasoning. arXiv preprint arXiv:2209.06120.
  10. Evaluating large language models: A comprehensive survey. arXiv preprint arXiv:2310.19736.
  11. A survey on large language models: Applications, challenges, limitations, and practical usage. Authorea Preprints.
  12. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300.
  13. How good are gpt models at machine translation? a comprehensive evaluation. arXiv preprint arXiv:2302.09210.
  14. Shashank Mohan Jain. 2022. Hugging face. In Introduction to Transformers for NLP: With the Hugging Face Library and Models to Solve Problems, pages 51–67. Springer.
  15. Mistral 7b. arXiv preprint arXiv:2310.06825.
  16. A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods. arXiv preprint arXiv:2403.02901.
  17. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361.
  18. Solar 10.7b: Scaling large language models with simple yet effective depth up-scaling.
  19. Efficient memory management for large language model serving with pagedattention. In Proceedings of the 29th Symposium on Operating Systems Principles, pages 611–626.
  20. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
  21. Truthfulqa: Measuring how models mimic human falsehoods. arXiv preprint arXiv:2109.07958.
  22. Evaluation of general large language models in contextually assessing semantic concepts extracted from adult critical care electronic health record notes. arXiv preprint arXiv:2401.13588.
  23. OpenAI. 2023. Gpt-4 technical report.
  24. Samuel J Paech. 2023. Eq-bench: An emotional intelligence benchmark for large language models. arXiv preprint arXiv:2312.06281.
  25. Philip Resnik and Jimmy Lin. 2010. Evaluation of nlp systems. The handbook of computational linguistics and natural language processing, pages 271–295.
  26. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, 64(9):99–106.
  27. Large language models encode clinical knowledge. Nature, 620(7972):172–180.
  28. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805.
  29. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  30. Fingpt: Instruction tuning benchmark for open-source large language models in financial datasets. arXiv preprint arXiv:2310.04793.
  31. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682.
  32. Huggingface’s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771.
  33. Fofo: A benchmark to evaluate llms’ format-following capability. arXiv preprint arXiv:2402.18667.
  34. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830.
  35. A survey of large language models. arXiv preprint arXiv:2303.18223.
  36. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in Neural Information Processing Systems, 36.
  37. Instruction-following evaluation for large language models. arXiv preprint arXiv:2311.07911.
  38. Toolqa: A dataset for llm question answering with external tools. Advances in Neural Information Processing Systems, 36.
Citations (2)
View on Semantic Scholar

Summary

  • The paper introduces a unified library that consolidates fragmented LLM evaluation tools into a single, accessible framework.
  • It employs a modular architecture with components such as no-code Slack integration, submodules, and benchmark connectors.
  • The framework reproduces benchmark scores with high fidelity and improves evaluation speed compared to original implementations.

Evalverse: A Unified Library for Streamlining the Evaluation of LLMs

Introduction to Evalverse

The computational linguistics field has witnessed remarkable transformations with the advent of LLMs, driven by rapid advancements and complex applications ranging from natural language understanding to domain-specific tasks. Despite these achievements, the decentralized nature of LLM evaluation tools has complicated thorough and comparative assessments. Addressing this challenge, Evalverse emerges as a pioneering library designed to centralize and simplify LLM evaluations for a broad audience, including individuals with limited AI background. By integrating disparate evaluation frameworks and facilitating no-code evaluations through platforms such as Slack, Evalverse offers an efficient, user-friendly approach to LLM assessment.

Evaluation Landscape and Evalverse's Niche

LLM evaluation encompasses multiple crucial aspects, including general performance, chat application functionality, Retrieval Augmented Generation (RAG) capabilities, and domain-specific performance. Numerous frameworks exist for evaluating these diverse facets, but the scattered landscape necessitates a comprehensive tool that unites them under a single umbrella. Evalverse fulfills this need by consolidating existing evaluation methodologies, thereby offering a unified and expandable evaluation library that addresses the fragmented state of LLM evaluation.

Architecture and Features of Evalverse

Evalverse's innovative architecture comprises six main components: Submodule, Connector, Evaluator, Compute Cluster, Database, and Reporter. This design facilitates a unified framework that not only supports no-code evaluation via communication platforms like Slack but also ensures expandability to accommodate new evaluation tools and methodologies. Key functionalities include:

  • No-code Evaluation: Offers an accessible pathway for users to initiate LLM evaluations and receive reports without coding expertise, leveraging Slack as an initial communication platform for this purpose.
  • Unified and Expandable Evaluation Library: By integrating external benchmarks as submodules, Evalverse allows for easy updates and the addition of new benchmarks, maintaining relevance with the fast-paced advancements in the LLM landscape.

Comparative Analysis and Performance

Evalverse presents a meticulous comparative analysis, demonstrating its ability to reproduce benchmark scores from original implementations with high fidelity. The framework supports evaluation across a broad spectrum of models, highlighting its versatility and comprehensive coverage. Additionally, Evalverse introduces efficiency improvements in evaluation times compared to original repositories, showcasing the benefits of its optimized architecture.

Future Perspectives and Implications

Evalverse sets a precedent for future development in LLM evaluation frameworks by offering a scalable, accessible tool that can adapt to evolving evaluation needs. Its architecture not only simplifies the evaluation process for researchers and practitioners but also opens the door to a wider audience seeking to understand and leverage LLM capabilities. The framework's ability to integrate new methodologies and benchmarks ensures its long-term relevance and potential to drive innovation in LLM evaluation practices.

Conclusion

Evalverse emerges as a novel solution to the challenges of evaluating LLMs by providing a unified, accessible, and expandable library that incorporates diverse evaluation tools. Its architecture promotes efficient, no-code evaluations, empowering a broader audience to engage in LLM assessments. By consolidating the fragmented landscape of LLM evaluation, Evalverse facilitates comparative assessments and accelerates the progress of research and applications in the field of computational linguistics and artificial intelligence.

Limitations and Ethics Considerations

Despite its innovative approach, Evalverse acknowledges potential challenges, such as the need for continuous updates and the reliance on community contributions. It emphasizes responsible usage, privacy, and security in LLM evaluations and advocates for ethical considerations in AI developments. Through transparency and inclusivity, Evalverse aims to foster ethical research practices within the computational linguistics community.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube