Bridging the Know-Act Gap via Task-Level Autoregressive Reasoning
Researchers propose task-level autoregressive reasoning to bridge the know-act gap in LLMs, where models can recognize flawed inputs but fail to reflect this in generative responses
- Identify the know-act gap in LLMs, where models can recognize flawed inputs but fail to reflect this in generative responses
- Develop task-level autoregressive reasoning to bridge this gap
- Evaluate the effectiveness of this approach in various settings, such as math word problems or question answering
- Refine and fine-tune the approach to improve the performance of LLMs
AI engineers and ML researchers can benefit from this research as it provides a new approach to improve the performance of LLMs in generating valid and accurate responses, and product managers can apply this to develop more reliable AI-powered products
💡 The know-act gap in LLMs can be addressed by developing task-level autoregressive reasoning, which enables models to reflect their recognition of flawed inputs in generative responses
🤖 Bridging the know-act gap in LLMs with task-level autoregressive reasoning 💡
Key Takeaways
Researchers propose task-level autoregressive reasoning to bridge the know-act gap in LLMs, where models can recognize flawed inputs but fail to reflect this in generative responses
Full Article
Abstract:
arXiv:2603.22619v1 Announce Type: new Abstract: LLMs often generate seemingly valid answers to flawed or ill-posed inputs. This is not due to missing knowledge: under discriminative prompting, the same models can mostly identify such issues, yet fail to reflect this in standard generative responses. This reveals a fundamental know-act gap between discriminative recognition and generative behavior. Prior work largely characterizes this issue in narrow settings, such as math word problems or quest
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