AIInsights: Nazarin Shari'a Kan Amfani da ChatGPT Don Nazarin Takardun Bincike

Teburin Abubuwan Ciki

• 1. Gabatarwa
• 2. Hanyar Bincike
  • 2.1 Tattara Bayanai
  • 2.2 Tsarin ChatGPT
  • 2.3 Ma'aunin Kimantawa
• 3. Tsarin Fasaha
  • 3.1 Tsarin Lissafi
  • 3.2 Aiwatar da Algorithm
• 4. Sakamakon Gwaji
  • 4.1 Daidaiton Rarrabawa
  • 4.2 Gano Iyaka
  • 4.3 Ingancin Tunani
• 5. Bincike Na Asali
• 6. Aikace-aikacen Gaba
• 7. Nassoshi

1. Gabatarwa

Wannan takarda tana binciko ingancin amfani da sigar ChatGPT 3.5 da 4 don nazarin takardun bincike don sauƙaƙe rubuta binciken wallafe-wallafen kimiyya. Binciken ya mai da hankali kan aikace-aikacen Hankalin Wucin Gadi (AI) a cikin Maganin Ciwon Nono (BCT) a matsayin yankin bincike. An tattara takardun bincike daga manyan rukunonin bugawa guda uku: Google Scholar, PubMed, da Scopus. An yi amfani da tsarin ChatGPT don gano rukuni, iyaka, da bayanai masu dacewa daga cikin takardun, suna taimakawa wajen tsarawa da tsara takardun bincike.

2. Hanyar Bincike

2.1 Tattara Bayanai

An tattara takardun bincike da suka shafi AI a cikin BCT daga Google Scholar, PubMed, da Scopus. Bayan haɗawa da kuma cire kwafin da suka yi kama, an kafa tarin tarin don bincike.

2.2 Tsarin ChatGPT

An yi amfani da GPT-3.5 (sabuntawa na Janairu 2022) da GPT-4 (sabuntawa na Afrilu 2023) duka. Abubuwan shiga sun haɗa da lakabin takarda, taƙaice, da abun ciki na rubutu don rarraba rukuni da iyakoki.

2.3 Ma'aunin Kimantawa

An yi amfani da bayanan gaskiya da masana fannin suka yi wa alama don kimanta daidaito a cikin gano rukuni, ƙayyadaddun iyaka, da ingancin tunani.

3. Tsarin Fasaha

3.1 Tsarin Lissafi

Ana iya ƙirƙira aikin rarrabawa ta amfani da tsarin gine-gine na tushen transformer. An ayyana tsarin kulawa kamar haka:

$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$

inda $Q$, $K$, da $V$ suna wakiltar matrices na tambaya, maɓalli, da ƙima, kuma $d_k$ shine girman maɓallin maɓalli.

3.2 Aiwatar da Algorithm

A ƙasa akwai misalin pseudo-code don rarraba takarda ta amfani da ChatGPT:

def categorize_paper(paper_text, model):
    prompt = f"""Rarraba takardar bincike mai zuwa zuwa ɗaya daga cikin ƙayyadaddun rukuni 
    masu alaƙa da AI a cikin Maganin Ciwon Nono. Takarda: {paper_text}"""
    response = model.generate(prompt)
    return extract_category(response)

# Misalin amfani
category = categorize_paper(paper_text, gpt4_model)
print(f"Rukunin da aka sanya: {category}")

4. Sakamakon Gwaji

4.1 Daidaiton Rarrabawa

GPT-4 ya fi GPT-3.5 girma tare da daidaito 77.3% vs. 65% a cikin gano rukuni.

4.2 Gano Iyaka

Rabin takardun an daidaita su daidai ta GPT-4, yana nuna matsakaicin aiki a fahimtar mahallin takarda.

4.3 Ingancin Tunani

GPT-4 ya haifar da dalilai tare da matsakaita 27% sababbin kalmomi, kuma 67% na waɗannan dalilan masana sun tabbatar da su.

5. Bincike Na Asali

Wannan binciken ya gabatar da ci gaba mai mahimmanci a cikin amfani da manyan harsunan harsuna (LLMs) kamar ChatGPT don sarrafa binciken ilimi. Ƙwararrun GPT-4 da aka nuna wajen rarraba takardun bincike tare da daidaito 77.3% da kuma samar da hujjoji masu ma'ana a cikin 67% na lokuta suna nuna yuwuwar tsarin tushen transformer a aikace-aikacen ilimi. Idan aka kwatanta da hanyoyin gargajiya irin su TF-IDF ko masu rarraba tushen BERT, ƙarfin GPT-4 yana cikin fahimtar mahallinsa da iyawar haifarwa, wanda ke ba shi damar ba kawai rarrabawa ba har ma da bayanin yanke shawararsa—wani siffa da ba kasafai ake samu a cikin tsarin al'ada ba.

Adadin 27% na samar da sabbin kalmomi a cikin tunani yana nuna cewa GPT-4 ba kawai yake maimaita bayanan horo ba amma yana gina sababbin bayanai, kodayake wannan kuma yana gabatar da yuwuwar halluci da ke buƙatar tabbatarwar masana. Wannan ya yi daidai da binciken daga takardar CycleGAN na asali (Zhu et al., 2017), inda koyon da ba a kulaba ya nuna duka yuwuwar ƙirƙira da ƙalubalen amincin. Hakazalika, rahoton fasaha na OpenAI GPT-4 ya jaddada ingantaccen tunanin da aka inganta akan GPT-3.5, musamman a cikin yankuna na musamman.

Duk da haka, daidaiton gano iyaka na 50% yana nuna iyakoki a cikin fahimtar mahalli mai sarƙaƙiya. Wannan tazara na aiki za a iya magance shi ta hanyar daidaitawa akan tarin fannoni na musamman, kamar yadda BioBERT (Lee et al., 2020) ya nuna a cikin haƙar ma'adinan rubutu na likitanci. Binciken ya mai da hankali kan maganin ciwon nono—wani yanki mai ingantaccen haraji—yana ba da yanayin sarrafawa don kimanta iyawar LLM, amma sakamako na iya bambanta a cikin yankuna marasa tsari.

Daga mahangar fasaha, tsarin kulawa mai yawan kai a cikin masu canzawa yana ba da damar sarrafa bangarori daban-daban na takarda (take, taƙaice, abun ciki) lokaci guda, kodayake farashin lissafi ya kasance mai yawa don manyan tarin. Aikin gaba zai iya binciko dabarun distillation don kiyaye aiki yayin rage buƙatun albarkatu, kama da hanyoyin a cikin DistilBERT (Sanh et al., 2019).

6. Aikace-aikacen Gaba

Haɗin tsarin kama da ChatGPT a cikin rubutun ilimi da nazarin takardun bincike yana da alƙawari ga aikace-aikace da yawa:

• Bita na Wallafe-wallafen Kai tsaye: Tsarin da zai iya haɗa ɗaruruwan takardu zuwa ingantaccen bincike.
• Gano Gibin Bincike: Taimakon AI gano wuraren bincike da ba a bincika sosai ba.
• Tallafin Bita na Takara: Kayan aiki don taimaka wa masu bita su kimanta dacewar takarda da inganci.
• Aikace-aikacen Ilimi: Malamai na AI waɗanda za su iya bayyana takardun bincike masu sarƙaƙiya ga ɗalibai.
• Canja Ilimi Tsakanin Yankuna: Gano alaƙa tsakanin fannonin bincike daban-daban.

Ya kamata ci gaban gaba ya mai da hankali kan inganta daidaito ta hanyar daidaita yanki, rage buƙatun lissafi, da haɓaka bayyana a cikin hanyoyin tunanin AI.

7. Nassoshi

1. Vaswani, A., et al. (2017). Attention is All You Need. Advances in Neural Information Processing Systems.
2. Zhu, J.-Y., et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. IEEE International Conference on Computer Vision.
3. Lee, J., et al. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics.
4. Sanh, V., et al. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108.
5. OpenAI (2023). GPT-4 Technical Report. OpenAI.
6. Devlin, J., et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT.