Extracting Social Connections from Finnish Karelian Refugee Interviews Using LLMs
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915160922259456 |
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| author | Laato, Joonatan Kanerva, Jenna Loehr, John Lummaa, Virpi Ginter, Filip |
| author_facet | Laato, Joonatan Kanerva, Jenna Loehr, John Lummaa, Virpi Ginter, Filip |
| contents | We performed a zero-shot information extraction study on a historical collection of 89,339 brief Finnish-language interviews of refugee families relocated post-WWII from Finnish Eastern Karelia. Our research objective is two-fold. First, we aim to extract social organizations and hobbies from the free text of the interviews, separately for each family member. These can act as a proxy variable indicating the degree of social integration of refugees in their new environment. Second, we aim to evaluate several alternative ways to approach this task, comparing a number of generative models and a supervised learning approach, to gain a broader insight into the relative merits of these different approaches and their applicability in similar studies.
We find that the best generative model (GPT-4) is roughly on par with human performance, at an F-score of 88.8%. Interestingly, the best open generative model (Llama-3-70B-Instruct) reaches almost the same performance, at 87.7% F-score, demonstrating that open models are becoming a viable alternative for some practical tasks even on non-English data. Additionally, we test a supervised learning alternative, where we fine-tune a Finnish BERT model (FinBERT) using GPT-4 generated training data. By this method, we achieved an F-score of 84.1% already with 6K interviews up to an F-score of 86.3% with 30k interviews. Such an approach would be particularly appealing in cases where the computational resources are limited, or there is a substantial mass of data to process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_13566 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extracting Social Connections from Finnish Karelian Refugee Interviews Using LLMs Laato, Joonatan Kanerva, Jenna Loehr, John Lummaa, Virpi Ginter, Filip Computation and Language We performed a zero-shot information extraction study on a historical collection of 89,339 brief Finnish-language interviews of refugee families relocated post-WWII from Finnish Eastern Karelia. Our research objective is two-fold. First, we aim to extract social organizations and hobbies from the free text of the interviews, separately for each family member. These can act as a proxy variable indicating the degree of social integration of refugees in their new environment. Second, we aim to evaluate several alternative ways to approach this task, comparing a number of generative models and a supervised learning approach, to gain a broader insight into the relative merits of these different approaches and their applicability in similar studies. We find that the best generative model (GPT-4) is roughly on par with human performance, at an F-score of 88.8%. Interestingly, the best open generative model (Llama-3-70B-Instruct) reaches almost the same performance, at 87.7% F-score, demonstrating that open models are becoming a viable alternative for some practical tasks even on non-English data. Additionally, we test a supervised learning alternative, where we fine-tune a Finnish BERT model (FinBERT) using GPT-4 generated training data. By this method, we achieved an F-score of 84.1% already with 6K interviews up to an F-score of 86.3% with 30k interviews. Such an approach would be particularly appealing in cases where the computational resources are limited, or there is a substantial mass of data to process. |
| title | Extracting Social Connections from Finnish Karelian Refugee Interviews Using LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.13566 |