Noah Siegel, Amy Widdicombe, Henning Heyen, María Pérez-Ortiz, Philip Treleaven
We lifted 3 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| henningheyen/scalability-of-llm-posthoc-explanations | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| make_test_set_esnli | Ran | henningheyen/scalability-of-llm-posthoc-explanations/utils.py code served (permissive licence) · get_code("e1c087e8e537738f") |
| make_test_set_cose | Not yet run | henningheyen/scalability-of-llm-posthoc-explanations/utils.py code served (permissive licence) · get_code("2fbae2b2de24931f") |
| make_test_set_mnli | Not yet run | henningheyen/scalability-of-llm-posthoc-explanations/utils.py code served (permissive licence) · get_code("d428e5283a97296e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs) are becoming bigger to boost performance. However, little is known about how explainability is affected by this trend. This work explores LIME explanations for DeBERTaV3 models of four different sizes on natural language inference (NLI) and zero-shot classification (ZSC) tasks. We evaluate the explanations based on their faithfulness to the models' internal decision processes and their plausibility, i.e. their agreement with human explanations. The key finding is that increased model size does not correlate with plausibility despite improved model performance, suggesting a misalignment between the LIME explanations and the models' internal processes as model size increases. Our results further suggest limitations regarding faithfulness metrics in NLI contexts.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.05348")
get_code_for_paper("2405.05348")
have("2405.05348")
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