<prompt> Bolela mabaka a mantlha a hlahisoang ke papisoKakaretso: Litlhahiso tse tsoelang pele tsa tokiso e nepahetseng bakeng sa mesebetsi eo u batlang ho e etsa e sa tsoa hlaha e le mokhoa o kopaneng ho fapana le tokiso e felletseng ea mohlala. Ka ho susumetsoa ke liphetho tsena tse tšepisang, re batlisisa monyetla oa ho fumana tlhaloso e hlakileng ea mongolo ea litlatsetso tse tsoelang pele tse tšepahalang bothateng boo ba bo rarollang. Ka tsebetso, re bona boitšoaro lipakeng tsa mosebetsi o rarolloang ka likhothaletso tse tsoelang pele le likhakanyo tse hlakileng tsa baahelani ba bona: Re ka fumana likhothaletso tse tsoelang pele tse rarollang mosebetsi ha re ntse re hlahisa mongolo o sa lumellaneng mohlala, tlhaloso ea e fapaneng kapa mosebetsi o hanyetsanang, ha o ntse o le ka hara moeli o monyenyane haholo 2 oa tsebetso e tsoelang pele ea boholo bo lekanang bakeng sa mosebetsi. Re fana ka maikutlo a mabapi le boitsoaro bona bo makatsang le bo makatsang, hammoho le litlhahlobo tse matla tse lekanyang phello ea liparamente tse fapaneng. Mohlala, bakeng sa mefuta e meholoanyane, re bona bofokoli bo phahameng, ke hore, re ka fumana lintlha tse mabapi le mongolo o mong le o mong o fapaneng ka ho nepahala. Liphuputso tsena li na le litlamorao tsa bohlokoa tse amanang le bothata ba ho toloka litlatsetso tse tsoelang pele ka botsepehi le kakaretso ea tsona ho pholletsa le mehlala le mesebetsi, ho fana ka tataiso bakeng sa tsoelo-pele ea nako e tlang ea ho khothaletsa mehlala ea lipuo.  <response>: Instruction in English: State the main arguments that the abstract makes . Response in English: The abstract makes several main arguments: 1. Fine-tuning continuous prompts for target tasks is a compact alternative to full model fine-tuning.2. The authors are investigating whether it is possible to extract a discrete textual interpretation of continuous prompts that is faithful to the problem they solve.3. They observe a wayward behavior between the task solved by continuous prompts and their nearest neighbor discrete projections.4. They provide intuitions and extensive empirical analyses to explain this behavior and quantify the effect of various parameters.5. They find that for larger model sizes, there is higher waywardness. 6. These findings have significant implications for the difficulty of interpreting continuous prompts, their generalization across models and tasks, and future progress in prompting language models. . Response in Sesotho: Moelelo o fana ka mabaka a maloa a mantlha: 1. Ho lokisa litaba tse tsoelang pele bakeng sa mesebetsi eo ho lebelloang ho eona ke mokhoa o kopaneng ho fapana le tokiso e felletseng ea mohlala. 2. Bangoli ba ntse ba etsa lipatlisiso hore na hoa khoneha ho fumana tlhaloso e hlakileng ea mongolo ea litlhahiso tse tsoelang pele tse tšepahalang bothateng boo ba bo rarollang. 3. Ba bona boitšoaro bo khelohileng lipakeng tsa mosebetsi o rarolloang ka likhothaletso tse tsoelang pele le likhakanyo tse hlakileng tsa baahelani ba bona. 4. Ba fana ka maikutlo le litlhahlobo tse ngata tsa mahlale ho hlalosa boitšoaro bona le ho lekanya phello ea mekhahlelo e fapaneng. 5. Ba fumana hore bakeng sa boholo bo boholo ba mohlala, ho na le bofokoli bo phahameng. 6. Liphuputso tsena li na le litlamorao tse kholo bakeng sa bothata ba ho toloka litlatsetso tse tsoelang pele, kakaretso ea tsona ho meralo le mesebetsi, le khatelo-pele ea nako e tlang ea ho susumetsa mehlala ea lipuo. 