Summary of Automatic Curriculum Expert Iteration For Reliable Llm Reasoning, by Zirui Zhao et al.
Automatic Curriculum Expert Iteration for Reliable LLM Reasoning
by Zirui Zhao, Hanze Dong, Amrita Saha, Caiming Xiong, Doyen Sahoo
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 A novel approach to mitigate hallucination and laziness in large language models (LLMs) is proposed, focusing on enhancing LLM reasoning and aligning responses with its capabilities. The Automatic Curriculum Expert Iteration (Auto-CEI) method iteratively explores the reasoning trajectories near the LLM policy, guiding incorrect paths back on track to reduce compounding errors and improve robustness. This approach promotes appropriate “I don’t know” responses after sufficient reasoning attempts, effectively balancing assertiveness and conservativeness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) often struggle with hallucinations (generating plausible but inaccurate content) and laziness (excessive refusals or defaulting to “I don’t know”). A new method called Automatic Curriculum Expert Iteration (Auto-CEI) helps LLMs reason more accurately and honestly. Auto-CEI adjusts the curriculum to reward extended reasoning before acknowledging incapability, allowing LLMs to push their limits and behave more realistically. |
Keywords
» Artificial intelligence » Hallucination
