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+memorize+text+from+several+sources+of+documents.+In+pretraining%252C+LLM+trains+to+maximize+the+likelihood+of+text+but+neither+receives+the+source+of+the+text+nor+memorizes+the+source.+Accordingly%252C+LLM+can+not+provide+document+information+on+the+generated+content%252C+and+users+do+not+obtain+any+hint+of+reliability%252C+which+is+crucial+for+factuality+or+privacy+infringement.+This+work+introduces+token-level+source+identification+in+the+decoding+step%252C+which+maps+the+token+representation+to+the+reference+document.+We+propose+a+bi-gram+source+identifier%252C+a+multi-layer+perceptron+with+two+successive+token+representations+as+input+for+better+generalization.+We+conduct+extensive+experiments+on+Wikipedia+and+PG19+datasets+with+several+LLMs%252C+layer+locations%252C+and+identifier+sizes.+The+overall+results+show+a+possibility+of+token-level+source+identifiers+for+tracing+the+document%252C+a+crucial+problem+for+the+safe+use+of+LLMs.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.f2b6f209ca48816108bafe47098897c6%3frik%3dsUEfKU7CKASbPQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Derived+Language+Model&FORM=IRPRST&ck=254F45BF74A129AAF50BE443B273A808&selectedIndex=26&itb=0)
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