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+with+chain-of-thought+(COT)+prompting+have+demonstrated+impressive+abilities+on+simple+nature+language+inference+tasks.+However%252C+they+tend+to+perform+poorly+on+Multi-hop+Question+Answering+(MHQA)+tasks+due+to+several+challenges%252C+including+hallucination%252C+error+propagation+and+limited+context+length.+We+propose+a+prompting+method%252C+Finite+State+Machine+(FSM)+to+enhance+the+reasoning+capabilities+of+LLM+for+complex+tasks+in+addition+to+improved+effectiveness+and+trustworthiness.+Different+from+COT+methods%252C+FSM+addresses+MHQA+by+iteratively+decomposing+a+question+into+multi-turn+sub-questions%252C+and+self-correcting+in+time%252C+improving+the+accuracy+of+answers+in+each+step.+Specifically%252C+FSM+addresses+one+sub-question+at+a+time+and+decides+on+the+next+step+based+on+its+current+result+and+state%252C+in+an+automaton-like+format.+Experiments+on+benchmarks+show+the+effectiveness+of+our+method.+Although+our+method+performs+on+par+with+the+baseline+on+relatively+simpler+datasets%252C+it+excels+on+challenging+datasets+like+Musique.+Moreover%252C+this+approach+mitigates+the+hallucination+phenomenon%252C+wherein+the+correct+final+answer+can+be+recovered+despite+errors+in+intermediate+reasoning.+Furthermore%252C+our+method+improves+LLMs%27+ability+to+follow+specified+output+format+requirements%252C+significantly+reducing+the+difficulty+of+answer+interpretation+and+the+need+for+reformatting.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.1f75f71441e552bb37dbd1a5afc080ec%3frik%3d%252bxA5vcQsu%252bcYTQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=FSM+Ai+in+Games+Example&FORM=IRPRST&ck=9FA009760FBC79625838153B78175FE2&selectedIndex=6&itb=0)
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