Summary of Beyond Labels: Aligning Large Language Models with Human-like Reasoning, by Muhammad Rafsan Kabir et al.
Beyond Labels: Aligning Large Language Models with Human-like Reasoning
by Muhammad Rafsan Kabir, Rafeed Mohammad Sultan, Ihsanul Haque Asif, Jawad Ibn Ahad, Fuad Rahman, Mohammad Ruhul Amin, Nabeel Mohammed, Shafin Rahman
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 fine-tuning approach for large language models (LLMs) is proposed to align them with human reasoning, producing morally correct and human-like decisions. The existing models are prone to generating false positives and malicious responses, raising ethical concerns. A curated dataset, Dataset for Aligning Reasons (DFAR), is designed to aid in this alignment process. The fine-tuning approach utilizes ethics labels and their corresponding reasons, improving the accuracy of LLMs on an ethical-unethical classification task and a reason-generation task. The proposed approach outperforms existing methods, achieving higher accuracy scores and lower misalignment rates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are designed to make decisions like humans, but they often produce incorrect or malicious responses. To fix this, researchers have created a special dataset with statements that are either ethical or unethical, along with reasons why they are correct or not. A new way of fine-tuning these models is proposed, which uses both the ethics labels and the corresponding reasons to make them more human-like. This approach was tested on two tasks: classifying statements as ethical or unethical, and generating reasons for those classifications. The results show that this new method works better than the old one, producing more accurate and reasonable responses. |
Keywords
» Artificial intelligence » Alignment » Classification » Fine tuning
