Summary of Flame: Factuality-aware Alignment For Large Language Models, by Sheng-chieh Lin et al.
FLAME: Factuality-Aware Alignment for Large Language Models
by Sheng-Chieh Lin, Luyu Gao, Barlas Oguz, Wenhan Xiong, Jimmy Lin, Wen-tau Yih, Xilun Chen
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the limitations of conventional large language model (LLM) alignment, which often leads to the generation of false facts or hallucination. The authors identify factors contributing to this issue in both supervised fine-tuning (SFT) and reinforcement learning (RL), including training on new knowledge or unfamiliar texts that can encourage LLMs to generate novel responses. They also find that reward functions used in RL can guide LLMs towards providing longer, more detailed responses, which may not always be factual. To address this issue, the authors propose factuality-aware alignment, combining factuality-aware SFT and direct preference optimization-based RL. The proposed approach demonstrates improved factual response generation while maintaining instruction-following capabilities. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make big language models more accurate when they’re trying to help people by following instructions. Right now, these models can sometimes come up with false information or “make things up.” The researchers found that this happens because the models are trained on new information or texts that they haven’t seen before, which can lead them to generate responses that aren’t true. They also discovered that the way the models are rewarded for their responses can encourage them to provide more detailed answers, even if those answers aren’t accurate. To fix this problem, the researchers came up with a new approach called “factuality-aware alignment.” This method helps the models generate more factual responses while still allowing them to follow instructions correctly. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Hallucination * Large language model * Optimization * Reinforcement learning * Supervised