需要多少參數才能換一顆燈泡？評估對話遊戲自我對弈的表現，並根據模型特性進行分析。

摘要

一個優秀的大型語言模型（LLM）有何特質？它在相關基準測試中表現出色，這些測試希望能夠合理地評估模型在真實應用中所面臨的挑戰。但是，是什麼讓模型表現出色？是什麼賦予模型其能力？我們採用了最近引入的一種基準測試，旨在通過對話遊戲的自我對弈，在目標導向、主動代理的情境中挑戰能力，並分析模型特徵（如參數數量或訓練類型）對性能發展的影響。我們發現，雖然參數數量與性能之間存在明顯關係，但在特定大小範圍內仍存在性能點的廣泛分佈，這需要通過訓練參數（如微調數據質量和方法）來解釋。從更實際的角度來看，我們還發現，不同存取方法的性能存在一定程度的不可預測性，可能是由於未暴露的抽樣參數，並且在推論期間至少對中等權重量化具有非常歡迎的性能穩定性。

English

What makes a good Large Language Model (LLM)? That it performs well on the relevant benchmarks -- which hopefully measure, with some validity, the presence of capabilities that are also challenged in real application. But what makes the model perform well? What gives a model its abilities? We take a recently introduced type of benchmark that is meant to challenge capabilities in a goal-directed, agentive context through self-play of conversational games, and analyse how performance develops as a function of model characteristics like number of parameters, or type of training. We find that while there is a clear relationship between number of parameters and performance, there is still a wide spread of performance points within a given size bracket, which is to be accounted for by training parameters such as fine-tuning data quality and method. From a more practical angle, we also find a certain degree of unpredictability about performance across access methods, possible due to unexposed sampling parameters, and a, very welcome, performance stability against at least moderate weight quantisation during inference.