知人善「認」
知人善「認」
2023/09/18
2023/09/22
<div><b><u></u>歷史人物與事件的自動化擷取技術與系統開發(二)</b></div><div><b>自動化建構歷史人物的事理圖譜</b><br><b>2023數位人文研究計畫成果展</b><br></div>
展覽頁面呈現形式
縱式
策展者
歷史人物與事件的自動化擷取技術與系統開發(二) 自動化建構歷史人物的事理圖譜
中央研究院資訊所 馬偉雲、謝佑明、林鑫彤、廖盈翔
展覽封面
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展覽資料
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"desc": "<div>由於計算能力的限制，相對小的模型在控制力與推理能力方面，可能都會面臨挑戰。</div><div>我們打算使用強化學習技術來增進對模型的控制能力，使之能夠符合我們對模型的各種要求。此外，我們預計使用思考鏈技術來提升推理能力。</div><div><br></div><div>強化式學習(Reinforcement Learning)</div><div>強化式學習是一種利用回饋訊號的校正技術。在預訓練和調適訓練完成後，我們讓模型試著根據未見過的指令作答，在每一時間點產生當時的字的生成分布並取樣，以這種方式可以產生多樣性的句子。這時我們聘請人力或是GPT4來進行這些句子的排序，降低不盡理想或不符合特定需求的句子順位。重排序的結果再轉換成兩兩句子之間的比對結果，用以訓練一個為句子打分的回饋模型，所打的分數即可作為回饋訊號再回傳給語言模型，達到校正的目的。</div><div><br></div><div>思考鏈(Chain of Thought)</div><div>思考鏈近年受到許多關注。主要的精神為在訓練或few shot learning的過程中，幫助模型一步步的展開，並且記錄每一步的結果作為思考歷程。<br>實驗證明，比之直接生成答案，具思考過程的作答更有助於最後答案的正確性.。<br>在後續計畫中，我們會考慮在幾種最新現有技術當中挑選適合的技術來實作與使用。</div>",
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