We lifted 8 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| KaiNylund/lm-weights-encode-time | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| cos_dist | Ran | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_time_vec_distances.py code served (permissive licence) · get_code("6b806dcd989a5d86") |
| cos_sim | Ran | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_closest_month_time_vec_combo.py code served (permissive licence) · get_code("5cf053908b10bc25") |
| get_indicator_embs | Ran | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_closest_year_time_vec_combo.py code served (permissive licence) · get_code("f256d010fc4b6e0f") |
| l2_dist | Ran | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_time_vec_distances.py code served (permissive licence) · get_code("57a097a3a02fb406") |
| time_less | Ran | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_time_vec_distances.py code served (permissive licence) · get_code("6f8530654afebd88") |
| get_model_flattened_weights | Not yet run | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_closest_month_time_vec_combo.py code served (permissive licence) · get_code("65920bba78dddf56") |
| get_model_flattened_weights | Not yet run | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_closest_year_time_vec_combo.py code served (permissive licence) · get_code("5d5cdab39165c79d") |
| get_model_flattened_weights | Not yet run | KaiNylund/lm-weights-encode-time/misc_analysis_and_figures/get_time_vec_monthly_projections.py code served (permissive licence) · get_code("1cbb4247ee981bfc") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present time vectors, a simple tool to customize language models to new time periods. Time vectors are created by finetuning a language model on data from a single time (e.g., a year or month), and then subtracting the weights of the original pretrained model. This vector specifies a direction in weight space that, as our experiments show, improves performance on text from that time period. Time vectors specialized to adjacent time periods appear to be positioned closer together in a manifold. Using this structure, we interpolate between time vectors to induce new models that perform better on intervening and future time periods, without any additional training. We demonstrate the consistency of our findings across different tasks, domains, model sizes, and time scales. Our results suggest that time is encoded in the weight space of finetuned models.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.13401")
get_code_for_paper("2312.13401")
have("2312.13401")
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