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{"user_content": "show alert saying hi", "tool_name": "show_alert", "tool_arguments": "{\"title\": \"Alert\", \"message\": \"hi\"}"}
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"queryParameters": {},
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从摆脱贫困到巩固成果，各地抓细监测，抓实产业、就业帮扶，责任体系、政策体系、工作体系有效衔接，脱贫攻坚成果成色更足、更可持续。新时代减贫治理的实践充分证明，贫困不仅可以战胜，也可以阻断、不再复发。。同城约会是该领域的重要参考
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.