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ProbabilisticLanguage Modelling
+are+a+promising+venue+for+natural+language+understanding+and+generation+tasks.+However%252C+current+LLMs+are+far+from+reliable%3a+they+are+prone+to+generate+non-factual+information+and%252C+more+crucially%252C+to+contradict+themselves+when+prompted+to+reason+about+beliefs+of+the+world.+These+problems+are+currently+addressed+with+large+scale+fine-tuning+or+by+delegating+consistent+reasoning+to+external+tools.+In+this+work%252C+we+strive+for+a+middle+ground+and+introduce+a+training+objective+based+on+principled+probabilistic+reasoning+that+teaches+a+LLM+to+be+consistent+with+external+knowledge+in+the+form+of+a+set+of+facts+and+rules.+Fine-tuning+with+our+loss+on+a+limited+set+of+facts+enables+our+LLMs+to+be+more+logically+consistent+than+previous+baselines+and+allows+them+to+extrapolate+to+unseen+but+semantically+similar+factual+knowledge+more+systematically.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.10768bbebee43415ccb6aac3f97d1606%3frik%3d7rXtrqJz62uElw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Probabilistic+Language+Modelling&FORM=IRPRST&ck=61CEA8E1404146E9EC9C94CED2C8ED57&selectedIndex=26&itb=0)
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