We lifted 8 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| strong-ai-lab/logical-and-abstract-reasoning | canonical | 6 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| convert_examples_to_features | Ran | strong-ai-lab/logical-and-abstract-reasoning/models/utils_multiple_choice.py code served (permissive licence) · get_code("90ea10eb1aa031b1") |
| corrupt_bench | Ran | strong-ai-lab/logical-and-abstract-reasoning/src/evaluate/information_gain.py code served (permissive licence) · get_code("266daa7219ccdd1d") |
| corrupt_pvr | Ran | strong-ai-lab/logical-and-abstract-reasoning/src/evaluate/information_gain.py code served (permissive licence) · get_code("237c07af276bf442") |
| get_example_symbolic | Ran | strong-ai-lab/logical-and-abstract-reasoning/src/evaluate/causal_induction_identification.py code served (permissive licence) · get_code("fbc6ef16f2c20b2a") |
| get_trial_symbolic | Ran | strong-ai-lab/logical-and-abstract-reasoning/src/evaluate/causal_induction_identification.py code served (permissive licence) · get_code("a099bb4dec45b409") |
| parse_input_symbolic | Ran | strong-ai-lab/logical-and-abstract-reasoning/src/evaluate/causal_induction_identification.py code served (permissive licence) · get_code("0f3995390250c44f") |
| select_field | Not yet run | strong-ai-lab/logical-and-abstract-reasoning/models/run_multiple_choice.py code served (permissive licence) · get_code("0a546b305d274996") |
| simple_accuracy | Not yet run | strong-ai-lab/logical-and-abstract-reasoning/models/run_multiple_choice.py code served (permissive licence) · get_code("3c241ecfe3749a6d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs), such as LLaMA, Alpaca, Vicuna, GPT-3.5 and GPT-4, have advanced the performance of AI systems on various natural language processing tasks to human-like levels. However, their generalisation and robustness when performing logical reasoning has not been sufficiently assessed. To comprehensively evaluate this ability, we develop three new logical reasoning datasets named "ReClor-plus", "LogiQA-plus" and "LogiQAv2-plus" that extend standard logical reasoning datasets to evaluate the robustness of the LLM's reasoning. For each, we create three subsets: the first with randomly shuffled options, the second with the correct choices replaced by "none of the other options is correct", and the third with a combination of shuffling and substitution. Experiments on these datasets show that these simple augmentations greatly hinder the models' performance. Despite their high performance on the original publicly available datasets, we find that all models perform poorly on these newly constructed datasets. We also demonstrate that introducing task variations into the training set can markedly improve the model's performance on both the original and our developed datasets. Finally, we show that applying logic-driven data augmentation for fine-tuning and prompting can enhance generalisation in both discriminative and generative models, offering a path to improving their robustness for tasks involving logical reasoning. Source code and data are made publicly available at https://github.com/Strong-AI-Lab/Logical-and-abstract-reasoning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.09430")
get_code_for_paper("2310.09430")
have("2310.09430")
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