Explainable Diagnosis for LLM Reasoning
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Key Takeaways
- SymDiag achieves an F1 score of 70.7 in detecting unfaithful reasoning chains.
- The system enables multi-round reasoning repair, increasing the pass rate from 53.4% to 81.4%.
- It introduces diagnostic evidence generation including inconsistency witnesses and missing premise indicators.
- The architecture supports automated patching by triggering targeted local fixes or global rewrites based on identified errors.
Summary & Methodology Analysis
SymDiag operates by converting the natural language chain of thought into structured symbolic states. This process employs a dual-branch generator consisting of Formal Translation and Critical Restatement to capture premises, intended inferences, and constraints. Once translated, a Self-Auditor performs cross-encoding consistency and sanity checks to classify failures into either TranslationError or ReasoningError categories. This separation of concerns is critical for identifying whether the fault lies in the linguistic representation or the underlying logical deduction.
Interactive System Flowchart
Cross-Examination & FAQs
A deeper dive clarifying mechanics, constraints, and baseline evaluations.
Q1. What is the primary problem SymDiag addresses?
It addresses the lack of faithfulness in LLM reasoning, where models may provide a correct answer through invalid logic, alongside the current inability to diagnose these reasoning failures.
Q2. How does SymDiag perform compared to existing methods?
SymDiag reaches an F1 score of 70.7 in faithfulness detection, outperforming existing baseline methods.
Q3. What is the practical benefit of using this system?
It significantly improves the success of reasoning tasks, raising the pass rate for multi-round reasoning from 53.4% to 81.4% after iterative repairs.
Q4. What happens when the system detects an error?
It triggers either targeted local patching or global prefix or suffix rewriting based on the generated diagnostic evidence.
Q5. What backend does the system use for logic checking?
It uses a Prolog solver to perform step-level satisfiability and entailment checks.
Q6. What specific diagnostic outputs does the system provide?
It generates failure evidence including counterexamples, inconsistency witnesses, and missing-premise indicators.
Q7. Which datasets were used in the evaluation?
The paper evaluated the system using AIME, AR-LSAT, GPQA, MMLU, and LogiDed.
Q8. Which models were used for testing?
The models include Llama-3.2-1B, Qwen3-1.7B, Qwen3-8B, GPTOSS-20B, GPTOSS-120B, and Qwen-3-Nemotron-32B-Reward.
Q9. What is a major limitation of SymDiag?
The performance of the system depends heavily on the quality of the base model used to perform the symbolic translations.