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+have+demonstrated+remarkable+capabilities+across+various+applications%252C+fundamentally+reshaping+the+landscape+of+natural+language+processing+(NLP)+research.+However%252C+recent+evaluation+frameworks+often+rely+on+the+output+probabilities+of+LLMs+for+predictions%252C+primarily+due+to+computational+constraints%252C+diverging+from+real-world+LLM+usage+scenarios.+While+widely+employed%252C+the+efficacy+of+these+probability-based+evaluation+strategies+remains+an+open+research+question.+This+study+aims+to+scrutinize+the+validity+of+such+probability-based+evaluation+methods+within+the+context+of+using+LLMs+for+Multiple+Choice+Questions+(MCQs)%252C+highlighting+their+inherent+limitations.+Our+empirical+investigation+reveals+that+the+prevalent+probability-based+evaluation+method+inadequately+aligns+with+generation-based+prediction.+Furthermore%252C+current+evaluation+frameworks+typically+assess+LLMs+through+predictive+tasks+based+on+output+probabilities+rather+than+directly+generating+responses%252C+owing+to+computational+limitations.+We+illustrate+that+these+probability-based+approaches+do+not+effectively+correspond+with+generative+predictions.+The+outcomes+of+our+study+can+enhance+the+understanding+of+LLM+evaluation+methodologies+and+provide+insights+for+future+research+in+this+domain.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.ee092ca4ada6dbf0576ffc17764a5877%3frik%3dUaJzsqgCCJwzlQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Misalignment+in+Large+Language+Model&simid=607992397587439136&FORM=IRPRST&ck=DC5D94ADBE4C8DCAE9F49879AC508D71&selectedIndex=1&itb=0)
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