鍗囩骇鐗堢粐濂虫ā鍨媀ega v2鍒锋柊涓栫晫璁板綍

鏁欒偛浣滆咃細姝︽眽閫鏃ユ湡锛2023-4-7鐐瑰嚮锛17

鏂伴椈缃戣锛堥氳鍛樿杞╋級鍦ㄦ鍓嶅叕甯冪殑鍏ㄧ悆鏉冨▉鑷劧璇█澶勭悊棰嗗煙-澶嶆潅璇█鐞嗚В娴嬭瘯SuperGLUE 涓紝姝︽眽澶у-浜笢鍙俊浜哄伐鏅鸿兘鑱斿悎鐮旂┒涓績缁勬垚鐨勬ⅵ涔嬮槦(JDExplore Dream Team, d-team)鍙備笌鍏朵腑锛屽叾鎻愬嚭鐨勫崌绾х増缁囧コ妯″瀷Vega v2瓒呰秺鍚屽満绔炴妧鐨勮胺姝屻佸井杞丱penAI绛変笟鐣岄《灏栦紒涓氾紝浠ユ诲钩鍧囧垎91.3鍒嗙櫥椤禨uperGLUE姒滈锛屽啀娆″埛鏂板鏉傝瑷鐞嗚В鎶鏈笘鐣岃褰曘

SuperGLUE鎴愮哗鎺掑悕琛

涓昏瀹屾垚浜哄憳涓烘姹夊ぇ瀛﹁绠楁満瀛﹂櫌閽熻捣鐓屻佷含涓滄帰绱㈢爺绌堕櫌涓佷寒銆佹姹夊ぇ瀛﹀浘鍍忎紶鎾笌鍗板埛鍖呰鐮旂┒涓績鍒樿強鍗庛佹姹夊ぇ瀛﹁绠楁満瀛﹂櫌鏉滃崥銆佷含涓滄帰绱㈢爺绌堕櫌銆佹倝灏煎ぇ瀛﹂櫠澶х▼銆

娴嬭瘯涓粐濂虫ā鍨媀ega v2鍦ㄥ叓涓瓙浠诲姟涓殑鍥涗釜鍗曢」浠诲姟锛屽嵆鑷劧璇█鎺ㄧ悊浠诲姟CB(CommitmentBank)銆佹枃鏈暣鍚换鍔TE(Recognizing Textual Entailment)銆佸洜鏋滄帹鐞嗕换鍔OPA(Choice of Plausible Alternatives)鍜屾寚浠f秷瑙d换鍔SC(Winograd Schema Challenge)涓潎浣嶅垪绗竴銆傜浉鍏宠礋璐d汉琛ㄧず锛岀粐濂虫ā鍨媀ega v2鍑60浜垮弬鏁伴噺瑙勬ā澶у箙瓒呰繃璋锋瓕鎻愬嚭鐨5400浜胯秴澶фā鍨婸aLM鐨勫鏉傝瑷鐞嗚В鎬ц兘锛屽厖鍒嗚瘉鏄庝簡姝︽眽澶у-浜笢鍙俊浜哄伐鏅鸿兘鑱斿悎鐮旂┒涓績鑷劧璇█鐞嗚В鎶鏈按骞冲湪瓒呯骇娣卞害瀛︿範棰嗗煙鐨勫叏鐞冮鍏堝湴浣嶃

SuperGLUE浠诲姟鍔熻兘灞曠ず

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Vega v2妯″瀷璁粌鏂规硶

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SuperGLUE姣旇禌瀹樼綉鍦板潃锛歨ttps://super.gluebenchmark.com/

妯″瀷鎶鏈姤鍛婁互鍙婄浉鍏冲伐浣滆鏂囷細

[1] Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUEhttps://arxiv.org/pdf/2212.01853.pdf

[2] Panda: Prompt transfer meets knowledge distillation for efficient model adaptationhttps://arxiv.org/pdf/2208.10160.pdf

锛堜緵鍥撅細璁$畻鏈哄闄 缂栬緫锛氱浉鑼癸級