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Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
。关于这个话题，同城约会提供了深入分析
2 月 24 日，腾讯元宝官方账号在上述内容下回复称，「非常抱歉给您带来不好的体验。经核实，该情况是由模型在处理多轮对话时输出的异常结果导致。」元宝方面表示，已紧急校正了相关问题并优化体验。，这一点在旺商聊官方下载中也有详细论述
python scripts/convert_nemo.py checkpoint.nemo -o model.safetensors --model 110m-tdt-ctc，这一点在旺商聊官方下载中也有详细论述
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