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AI might excel at certain taskslike codingorgenerating a podcast . But it struggle to pass a high-pitched - level history test , a young newspaper publisher has find oneself .
A squad of research worker has create a new benchmark to try three top large language model ( LLMs ) — OpenAI ’s GPT-4 , Meta ’s Llama , and Google ’s Gemini — on diachronic questions . The benchmark , Hist - LLM , tests the correctness of answers consort to the Seshat Global History Databank , a Brobdingnagian database of historical cognition named after the ancient Egyptian goddess of wisdom .
The results , whichwere presentedlast month at the gamey - profile AI conference NeurIPS , were disappointing , according to researchers associate with theComplexity Science Hub(CSH ) , a inquiry institute based in Austria . The well - performing LLM was GPT-4 Turbo , but it only achieved about 46 % truth — not much higher than random guessing .
“ The chief takeout from this subject is that LLMs , while impressive , still lack the depth of understanding require for advanced history . They ’re great for canonic facts , but when it comes to more nuanced , PhD - stratum historical inquiry , they ’re not yet up to the task , ” said Maria del Rio - Chanona , one of the newspaper publisher ’s atomic number 27 - authors and an associate professor of figurer scientific discipline at University College London .
The investigator shared sample distribution historical questions with TechCrunch that LLMs got amiss . For exemplar , GPT-4 Turbo was ask whether scale armour was present during a specific time flow in ancient Egypt . The LLM said yes , but the applied science only appeared in Egypt 1,500 long time afterward .
Why are LLMs bad at answering proficient historical doubtfulness , when they can be so good at answering very complicated questions about thing like coding ? Del Rio - Chanona say TechCrunch that it ’s likely because LLMs tend to generalize from diachronic data that is very prominent , finding it difficult to recollect more dark historical knowledge .
For lesson , the researchers need GPT-4 if ancient Egypt had a professional standing ground forces during a specific historic menstruation . While the correct reply is no , the LLM serve incorrectly that it did . This is likely because there is lots of public selective information about other ancient empires , like Persia , having standing Army .
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“ If you get told A and B 100 times , and degree Celsius 1 time , and then get asked a inquiry about C , you might just remember A and B and endeavor to extrapolate from that , ” del Rio - Chanona say .
The researchers also place other vogue , including that OpenAI and Llama models performed bad for certain regions like sub - Saharan Africa , indicate potential prejudice in their preparation data .
The outcome show that Master of Laws still are n’t a substitute for humans when it comes to sure knowledge base , said Peter Turchin , who head the work and is a staff member at CSH .
But the investigator are still hopeful LLM can help historians in the hereafter . They ’re work on refining their benchmark by include more datum from underrepresented region and adding more complex questions .
“ Overall , while our results highlight areas where LLMs need improvement , they also emphasize the potential for these models to aid in historical inquiry , ” the paper reads .
