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Yige Yuan, Teng Xiao, Li Yunfan, Bingbing Xu, Shuchang Tao, Yunqi Qiu, Huawei Shen, Xueqi Cheng
Outline
This paper proposes Simple Energy Adaptation (SEA), a novel algorithm for aligning large-scale language models using human feedback during inference time. To overcome the limitations of existing discrete space search methods, SEA directly adjusts the response of the baseline policy to the optimal response through gradient-based sampling in the continuous latent space. SEA performs an iterative optimization procedure based on an energy function, enabling simple yet effective alignment. Experimental results demonstrate that SEA outperforms existing methods, achieving relative performance gains of up to 77.51% on AdvBench and 16.36% on MATH.
Takeaways, Limitations
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Takeaways:
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A simple and effective algorithm for inference time alignment is presented.
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Overcoming the limitations of discrete space exploration and improving performance through direct adjustment in continuous latent space.
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Demonstrated excellent performance in AdvBench and MATH benchmarks.
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High relative performance improvement compared to existing methods.
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Limitations:
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Limitations presented in the paper is not explicitly mentioned.
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Further research is needed on the generalizability of the algorithm and its applicability in various environments.
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Although the code release increases the reproducibility, further analysis is needed to determine its efficiency and scalability in real-world usage.
