Introduces TableGrid Navigation (TGN) and Progressive Inference Prompting (PIP) as training-free structured prompting frameworks that improve LLM performance on table question answering over baselines on TableBench and achieve SOTA on FeTaQa.
Faithful logical reasoning via symbolic chain-of-thought
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Derivation Prompting constructs logic-based derivation trees in RAG generation to improve interpretability and reduce unacceptable answers compared to standard RAG or long-context methods in a case study.
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.
LogicAgent uses a semiotic-square-guided approach to enhance logical reasoning in LLMs on the new RepublicQA benchmark and others, reporting average gains of 6.25% and 7.05% respectively.
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.
citing papers explorer
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Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
Introduces TableGrid Navigation (TGN) and Progressive Inference Prompting (PIP) as training-free structured prompting frameworks that improve LLM performance on table question answering over baselines on TableBench and achieve SOTA on FeTaQa.
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Derivation Prompting: A Logic-Based Method for Improving Retrieval-Augmented Generation
Derivation Prompting constructs logic-based derivation trees in RAG generation to improve interpretability and reduce unacceptable answers compared to standard RAG or long-context methods in a case study.
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GoAT-X: A Graph of Auditing Thoughts for Securing Token Transactions in Cross-Chain Contracts
GoAT-X introduces a Graph of Auditing Thoughts framework that combines static data flow extraction with structured LLM reasoning to identify semantic vulnerabilities in cross-chain token transactions.
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Semantic-Aware Logical Reasoning via a Semiotic Framework
LogicAgent uses a semiotic-square-guided approach to enhance logical reasoning in LLMs on the new RepublicQA benchmark and others, reporting average gains of 6.25% and 7.05% respectively.
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Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding
Advanced language representations shape LLMs' schemas to improve knowledge activation and problem-solving.