Qian Liu, Hao Fei, Liangming Pan, Jundong Xu, Mong Li Lee, W. Hsu
We lifted 7 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| Aiden0526/SymbCoT | canonical | 7 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| argmax | Ran | Aiden0526/SymbCoT/baselines/evaluation.py code served (permissive licence) · get_code("c336622e652dd934") |
| compute_f1_score | Ran | Aiden0526/SymbCoT/evaluate.py code served (permissive licence) · get_code("8f1a04852f7b3744") |
| dispatch_openai_chat_requests | Ran | Aiden0526/SymbCoT/utils.py code served (permissive licence) · get_code("4e1c4ff342f96bc9") |
| dispatch_openai_prompt_requests | Ran | Aiden0526/SymbCoT/utils.py code served (permissive licence) · get_code("94928eafb9afc8c7") |
| evaluate_performance | Ran | Aiden0526/SymbCoT/evaluate.py code served (permissive licence) · get_code("175a1319fe2df65c") |
| extract_number | Ran | Aiden0526/SymbCoT/baselines/evaluation.py code served (permissive licence) · get_code("471cfa8084196a13") |
| normalize_text | Ran | Aiden0526/SymbCoT/baselines/evaluation.py code served (permissive licence) · get_code("71986ed6ac7b9157") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
While the recent Chain-of-Thought (CoT) technique enhances the reasoning ability of large language models (LLMs) with the theory of mind, it might still struggle in handling logical reasoning that relies much on symbolic expressions and rigid deducing rules. To strengthen the logical reasoning capability of LLMs, we propose a novel Symbolic Chain-of-Thought, namely SymbCoT, a fully LLM-based framework that integrates symbolic expressions and logic rules with CoT prompting. Technically, building upon an LLM, SymbCoT 1) first translates the natural language context into the symbolic format, and then 2) derives a step-by-step plan to solve the problem with symbolic logical rules, 3) followed by a verifier to check the translation and reasoning chain. Via thorough evaluations on 5 standard datasets with both First-Order Logic and Constraint Optimization symbolic expressions, SymbCoT shows striking improvements over the CoT method consistently, meanwhile refreshing the current stateof-the-art performances. We further demonstrate that our system advances in more faithful, flexible, and explainable logical reasoning. To our knowledge, this is the first to combine symbolic expressions and rules into CoT for logical reasoning with LLMs. Code is open at https://github.com/Aiden0526/SymbCoT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.18357")
get_code_for_paper("2405.18357")
have("2405.18357")
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