Papers with causal QA benchmarks
Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification (2026.eacl-long)
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| Challenge: | Large language models (LLMs) are increasingly being applied to causal reasoning tasks. |
| Approach: | They propose a symbolic verification framework that checks whether LLM-generated causal expressions are derivable from a given causal graph using do-calculus and probability theory. |
| Outcome: | The proposed framework can recover correct answers that would otherwise be marked incorrect due to superficial differences. |