Summary of Credes: Causal Reasoning Enhancement and Dual-end Searching For Solving Long-range Reasoning Problems Using Llms, by Kangsheng Wang et al.
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs
by Kangsheng Wang, Xiao Zhang, Hao Liu, Songde Han, Huimin Ma, Tianyu Hu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to large language models (LLMs) for handling combinatorial optimization problems involving long-range reasoning. The main limitations of current LLMs are causal hallucinations and huge search spaces, which can lead to inconsistencies between reasoning and state transitions. To address these issues, the authors introduce the Causal Relationship Enhancement (CRE) mechanism, combining cause-effect interventions with Individual Treatment Effect (ITE), to guarantee solid causal rightness between each step of reasoning and state transition. Additionally, a Dual-End Searching (DES) approach is proposed to seek solutions by simultaneously starting from both initial and goal states on the causal probability tree. By integrating CRE and DES (CreDes), the authors demonstrate that their model can achieve simultaneous multi-step reasoning, outperforming existing State-of-the-Art (SOTA) solutions in long-range reasoning tasks with improved accuracy and time efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make computers better at solving complex problems. Right now, computers are good at understanding short texts but struggle when they need to reason about longer sequences of events. The authors propose two main ideas: first, a way to ensure that the computer’s reasoning is consistent with what it observes; second, a new approach to searching through possibilities that allows the computer to consider many different solutions simultaneously. By combining these ideas, the authors show that their system can solve long-range reasoning problems more accurately and efficiently than current state-of-the-art approaches. |
Keywords
» Artificial intelligence » Optimization » Probability
