Teburin Abubuwan Ciki
- 1. Gabatarwa
- 2. Ayyuka Masu Alaka
- 3. Hanyoyin Bincike
- 4. Sakamako
- 5. Tattaunawa
- 6. Kammalawa
- 7. Bincike na Asali
- 8. Cikakkun Bayanai na Fasaha da Tsarin Lissafi
- 9. Sakamakon Gwaji da Bayanin Hoton
- 10. Misalin Tsarin Bincike
- 11. Ayyuka da Hanyoyi na Gaba
- 12. Manazarta
1. Gabatarwa
Ilimin Artificial Intelligence (AI) ya kawo sauyi a ilimi ta hanyar canza hanyoyin koyo da koyarwa. Manyan samfuran harshe (LLMs) kamar OpenAI ChatGPT, Microsoft Bing Chat (BingChat), da Google Bard suna wakiltar ci gaba mai mahimmanci a wannan fanni. Wannan takarda ta kimanta ayyukansu akan bayanan jarrabawar kammala sakandare ta Vietnam (VNHSGE) na Turanci, inda ta magance tambayoyin bincike guda uku: (1) Menene ayyukan ChatGPT, BingChat, da Bard akan bayanan Turanci na VNHSGE? (2) Yaya waɗannan LLMs suke kwatanta da ɗaliban Vietnam a ƙwarewar Turanci? (3) Wane irin dama ne LLMs ke da shi don koyarwa da koyon harshen Turanci a Vietnam?
2. Ayyuka Masu Alaka
2.1 Manyan Samfuran Harshe
Ci gaba na baya-bayan nan a cikin LLMs, musamman tsarin BERT da GPT, sun ba da damar sadarwa mai kama da ɗan adam. An horar da waɗannan samfuran akan manyan tarin rubutu kuma an daidaita su don ayyuka na musamman, suna nuna iyawa a ilimi, samar da abun ciki, da fassara.
2.2 Amfani da LLM a Ilimi
An yi amfani da LLMs a cikin mataimakan kama-da-wane, chatbots, da tsarin koyo na kan layi. Nazarin da Kasneci et al. (2023) da Kung et al. (2023) suka yi ya nuna yuwuwar su don koyo na musamman, kodayake ana buƙatar kimantawa a hankali don yanayin ilimi daban-daban.
3. Hanyoyin Bincike
3.1 Bayanan
Bayanan Turanci na VNHSGE ya ƙunshi tambayoyin zaɓi da yawa waɗanda suka shafi nahawu, ƙamus, fahimtar karatu, da ƙwarewar rubutu, an tsara su don kimantawa a matakin sakandare a Vietnam.
3.2 Ma'aunin Kimantawa
Ana auna aiki ta amfani da daidaito (kashi na amsoshi daidai). Ana kimanta samfuran akan saitin tambayoyi iri ɗaya don tabbatar da kwatancen gaskiya.
3.3 Saitin Gwaji
An gwada kowane samfur (ChatGPT GPT-3.5, BingChat, da Google Bard) akan bayanan a ƙarƙashin yanayin sarrafawa. An rubuta amsoshi kuma an ƙididdige su bisa ga maɓallin amsa na hukuma.
4. Sakamako
4.1 Ayyuka Gabaɗaya
BingChat ya sami mafi girman daidaito a 92.4%, sai Bard a 86%, da ChatGPT a 79.2%. Waɗannan sakamakon sun nuna bambanci mai mahimmanci a ayyukan LLM akan aiki ɗaya.
4.2 Kwatanta da Ayyukan Dan Adam
Duk LLMs uku sun fi matsakaicin ɗalibin sakandare na Vietnam a ƙwarewar Turanci, wanda ke nuna yuwuwar su azaman kayan aikin ilimi na ƙari.
5. Tattaunawa
5.1 Tasiri ga Ilimin Turanci
Babban aikin BingChat da Bard ya nuna cewa za su iya zama madadin ingantacciya ga ChatGPT, musamman a yankunan da ChatGPT ba ya samuwa a hukumance. Waɗannan samfuran na iya tallafawa karatun kai, ba da amsa nan take, da haɓaka sakamakon koyo.
5.2 Iyakoki da Ayyuka na Gaba
Iyakoki sun haɗa da mayar da hankali kan bayanan guda ɗaya da rashin bincike na inganci game da tunanin samfur. Ayyuka na gaba ya kamata su bincika manyan bayanai, iyawar harsuna da yawa, da haɗawa cikin saitunan aji.
6. Kammalawa
Wannan binciken ya nuna cewa BingChat, Bard, da ChatGPT sun fi ɗaliban Vietnam a jarrabawar Turanci ta VNHSGE, inda BingChat ke kan gaba. Waɗannan binciken suna goyan bayan haɗa LLMs cikin ilimin harshen Turanci, suna ba da mafita na koyo masu iya haɓakawa da samuwa.
7. Bincike na Asali
Wannan takarda ta ba da kwatancen da ya dace kuma mai amfani na manyan LLMs uku akan gwajin Turanci daidaitacce, tana magance gibin da ke cikin wallafe-wallafe game da ayyukan LLM a yanayin ilimi na waje da Ingilishi. Binciken da BingChat ya fi ChatGPT da Bard duka yana da mahimmanci musamman, saboda yana ƙalubalantar tunanin cewa samfurin da ya fi shahara (ChatGPT) shine mafi kyau. Wannan ya yi daidai da bincike mai faɗi da ke nuna cewa aikin samfur na iya bambanta sosai a cikin harsuna da fannoni (Brown et al., 2020; Devlin et al., 2019). Gudunmawar binciken ta ta'allaka ne ga dacewarsa kai tsaye ga malamai da masu tsara manufofi na Vietnam, yana ba da haske mai amfani don haɗa LLMs cikin manhaja. Duk da haka, ana iya ƙarfafa binciken ta hanyar nazarin nau'ikan kurakurai da kowane samfur yake yi, saboda wannan zai ba da zurfin fahimtar ilimi. Misali, shin kurakurai sun ta'allaka ne a nahawu, ƙamus, ko fahimtar karatu? Irin wannan rarrabuwa zai taimaka wajen daidaita hanyoyin LLM. Bugu da ƙari, binciken bai magance yuwuwar son zuciya a cikin bayanan ko bayanan horar da samfuran ba, wanda zai iya shafar iyawar gabaɗaya. Duk da waɗannan iyakokin, takardar ta nuna gamsarwa cewa LLMs na iya zama kayan aiki masu inganci don koyon harshen Turanci, musamman a wuraren da ke da ƙarancin albarkatu. Bincike na gaba ya kamata ya bincika nazarin dogon lokaci don tantance tasirin koyo da LLM ke taimakawa akan sakamakon ɗalibai a kan lokaci.
8. Cikakkun Bayanai na Fasaha da Tsarin Lissafi
Ana kimanta aikin kowane LLM ta amfani da daidaito, wanda aka ayyana kamar haka:
$Daidaito = \frac{Adadin\ Amsoshi\ Daidai}{Jimillar\ Adadin\ Tambayoyi} \times 100\%$
Ga bayanan da ke da $N$ tambayoyi, daidaito $A$ ga samfur $M$ shine:
$A_M = \frac{1}{N} \sum_{i=1}^{N} \mathbb{1}(\hat{y}_i = y_i)$
inda $\hat{y}_i$ shine hasashen samfur kuma $y_i$ shine gaskiyar tushe don tambaya $i$.
9. Sakamakon Gwaji da Bayanin Hoton
An taƙaita sakamakon a cikin hoton ginshiƙi da ke kwatanta daidaiton samfuran uku. X-axis tana wakiltar samfuran (ChatGPT, Bard, BingChat), kuma y-axis tana wakiltar kashi na daidaito. Ginshiƙin BingChat ya kai 92.4%, na Bard 86%, na ChatGPT 79.2%. Layin kwance yana nuna matsakaicin aikin ɗan adam (kusan 70%), yana nuna duk samfuran sun wuce wannan ma'auni.
10. Misalin Tsarin Bincike
Yi la'akari da tambaya misali daga bayanan Turanci na VNHSGE: "Zaɓi kalmar da ta dace don cika jimlar: She ___ to school every day." Zaɓuɓɓuka: A) go, B) goes, C) going, D) gone. Amsar daidai ita ce B) goes. Ana rubuta amsar kowane samfur kuma a ƙididdige shi. Wannan misali mai sauƙi yana nuna tsarin kimantawa da aka yi amfani da shi ga duk tambayoyin da ke cikin bayanan.
11. Ayyuka da Hanyoyi na Gaba
Ana iya haɗa LLMs cikin ilimin Turanci na sakandare a Vietnam ta hanyar: (1) Tsarin koyarwa na AI wanda ke ba da amsa na musamman; (2) Ƙididdigar rubutu ta atomatik da gyaran nahawu; (3) Wakilan tattaunawa don yin magana; (4) Dandamalin koyo masu daidaitawa waɗanda ke daidaita wahala bisa ga aikin ɗalibi. Hanyoyi na gaba sun haɗa da haɓaka LLMs na harsuna da yawa waɗanda aka keɓance don yanayin Vietnam, haɗa da bambance-bambancen al'adu, da tabbatar da samun damar fasaha daidai.
12. Manazarta
- Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901.
- Dao, X.-Q., et al. (2023a). ChatGPT on the Vietnamese High School Graduation Examination. arXiv preprint.
- Dao, X.-Q., et al. (2023b). ChatGPT on an English Test Case. arXiv preprint.
- Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.
- Kasneci, E., et al. (2023). ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, 102274.
- Kung, T. H., et al. (2023). Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education. PLOS Digital Health, 2(2), e0000198.
- OpenAI. (2023). GPT-4 Technical Report. arXiv preprint.
- Thorp, H. H. (2023). ChatGPT is Fun, But Not an Author. Science, 379(6630), 313-313.
Babban Fahimta, Tsarin Hankali, Ƙarfi da Rarrauna, Hanyoyi Masu Amfani
Babban Fahimta: Wannan takarda kwatancen aiki ne na zahiri wanda ya kawar da hayaniya, yana nuna cewa 'mafi kyau' ya dogara da yanayi. Mamayar BingChat a jarrabawar Vietnam ita ce farkawa ga waɗanda suke ɗauka cewa ChatGPT ya fi girma a duniya.
Tsarin Hankali: Takardar tana bin tsari bayyananne, madaidaiciya: bayanin matsala (bukatar kimanta LLM a Vietnam), hanyoyin (gwajin daidaitacce), sakamako (BingChat > Bard > ChatGPT), da tasiri (LLMs a matsayin kayan aikin ilimi masu inganci). Hankalin yana da inganci amma ba shi da zurfi a cikin nazarin kuskure.
Ƙarfi da Rarrauna: Ƙarfi ya haɗa da tsarin gwaji mai da hankali da sake maimaitawa da kuma dacewa kai tsaye ga manufofin ilimi na Vietnam. Rarrauna sun haɗa da ƙarancin bayanan (jarrabawa ɗaya), rashin bincike na inganci (me yasa BingChat ya yi nasara?), da kuma rashin tattaunawa game da son zuciya na samfur ko wakilcin bayanan. Binciken hoto ne mai amfani amma ba cikakken kimantawa ba.
Hanyoyi Masu Amfani: Ga malaman Vietnam: Gwada BingChat da Bard a cikin aji nan take, mai da hankali kan motsa jiki na nahawu da ƙamus. Ga masu bincike: Gudanar da nazarin kuskure don gano raunin samfur na musamman. Ga masu tsara manufofi: Saka hannun jari a haɓaka LLM na gida wanda aka keɓance don manhajar Vietnam. Babban abin da za a ɗauka: kar a saka duk ƙwai a cikin kwando ɗaya na LLM—rarraba kuma gwada a gida.