Public articles linked to the same research event.
arXiv Targeting the Logic Drift that appears in LLM outputs during structured knowledge reasoning, this work proposes the Logits-to-Logic framework, whose core modules are logits strengthening and logits filtering, to directly correct the logits produced in the autoregressive generation process; experiments report significantly improved logic consistency in structured knowledge reasoning and state-of-the-art performance on multiple KGQA benchmarks.
Targeting the Logic Drift that appears in LLM outputs during structured knowledge reasoning, this work proposes the Logits-to-Logic framework, whose core modules are logits strengthening and logits filtering, to directly correct the logits produced in the autoregressive generation process; experiments report significantly improved logic consistency in structured knowledge reasoning and state-of-the-art performance on multiple KGQA benchmarks.
Targeting the Logic Drift that appears in LLM outputs during structured knowledge reasoning, this work proposes the Logits-to-Logic framework, whose core modules are logits strengthening and logits filtering, to directly correct the logits produced in the autoregressive generation process; experiments report significantly improved logic consistency in structured knowledge reasoning and state-of-the-art performance on multiple KGQA benchmarks.
Targeting the Logic Drift that appears in LLM outputs during structured knowledge reasoning, this work proposes the Logits-to-Logic framework, whose core modules are logits strengthening and logits filtering, to directly correct the logits produced in the autoregressive generation process; experiments report significantly improved logic consistency in structured knowledge reasoning and state-of-the-art performance on multiple KGQA benchmarks.