We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ssai-trento/llm-zero-shot-nl | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| int_to_days | Ran | ssai-trento/llm-zero-shot-nl/utils.py code served (permissive licence) · get_code("5b4005a233d11c77") |
| list_predicted_users | Ran | ssai-trento/llm-zero-shot-nl/utils.py code served (permissive licence) · get_code("637941d3a32c65a3") |
| prompt_generator | Ran | ssai-trento/llm-zero-shot-nl/utils.py code served (permissive licence) · get_code("e7726f2ef6f7fb06") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Predicting the locations an individual will visit in the future is crucial for solving many societal issues like disease diffusion and reduction of pollution. However, next-location predictors require a significant amount of individual-level information that may be scarce or unavailable in some scenarios (e.g., cold-start). Large Language Models (LLMs) have shown good generalization and reasoning capabilities and are rich in geographical knowledge, allowing us to believe that these models can act as zero-shot next-location predictors. We tested more than 15 LLMs on three real-world mobility datasets and we found that LLMs can obtain accuracies up to 36.2%, a significant relative improvement of almost 640% when compared to other models specifically designed for human mobility. We also test for data contamination and explored the possibility of using LLMs as text-based explainers for next-location prediction, showing that, regardless of the model size, LLMs can explain their decision.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.20962")
get_code_for_paper("2405.20962")
have("2405.20962")
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