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        <title>Collaborative Performance Prediction for Large Language Models</title>
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        <namePart type="given">Qiyuan</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic similarities within model families and utilized such similarities for performance prediction. However, they tend to overlook the similarities between model families and only consider design factors listed in the original scaling law. To overcome these limitations, we introduce a novel framework, Collaborative Performance Prediction (CPP), which significantly enhances prediction accuracy by leveraging the historical performance of various models on downstream tasks and other design factors for both model and task. We also collect a collaborative data sourced from online platforms containing both historical performance and additional design factors. With the support of the collaborative data, CPP not only surpasses traditional scaling laws in predicting the performance of scaled LLMs but also facilitates a detailed analysis of factor importance, an area previously overlooked.</abstract>
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    <identifier type="doi">10.18653/v1/2024.emnlp-main.150</identifier>
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%0 Conference Proceedings
%T Collaborative Performance Prediction for Large Language Models
%A Zhang, Qiyuan
%A Lyu, Fuyuan
%A Liu, Xue
%A Ma, Chen
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-collaborative
%X Comprehensively understanding and accurately predicting the performance of large language models across diverse downstream tasks has emerged as a pivotal challenge in NLP research. The pioneering scaling law on downstream works demonstrated intrinsic similarities within model families and utilized such similarities for performance prediction. However, they tend to overlook the similarities between model families and only consider design factors listed in the original scaling law. To overcome these limitations, we introduce a novel framework, Collaborative Performance Prediction (CPP), which significantly enhances prediction accuracy by leveraging the historical performance of various models on downstream tasks and other design factors for both model and task. We also collect a collaborative data sourced from online platforms containing both historical performance and additional design factors. With the support of the collaborative data, CPP not only surpasses traditional scaling laws in predicting the performance of scaled LLMs but also facilitates a detailed analysis of factor importance, an area previously overlooked.
%R 10.18653/v1/2024.emnlp-main.150
%U https://aclanthology.org/2024.emnlp-main.150/
%U https://doi.org/10.18653/v1/2024.emnlp-main.150
%P 2576-2596

Markdown (Informal)

[Collaborative Performance Prediction for Large Language Models](https://aclanthology.org/2024.emnlp-main.150/) (Zhang et al., EMNLP 2024)

• Collaborative Performance Prediction for Large Language Models (Zhang et al., EMNLP 2024)

ACL

• Qiyuan Zhang, Fuyuan Lyu, Xue Liu, and Chen Ma. 2024. Collaborative Performance Prediction for Large Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 2576–2596, Miami, Florida, USA. Association for Computational Linguistics.