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英文字典中文字典相关资料:


  • Self-Consistency Improves Chain of Thought Reasoning in Language Models . . .
    Abstract: Chain-of-thought prompting combined with pretrained large language models has achieved encouraging results on complex reasoning tasks In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting
  • SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
    540B) On all four language models, self-consistency improves over chain-of-thought prompting (Wei et al ,2022) by a striking margin across all tasks In particular, when used with LM-540B or GPT-3, self-consistency achieves new state-of-the-art levels of performance across arithmetic reasoning tasks, including GSM8K (Cobbe et al ,2021) (+17 9%
  • SELF-CONSISTENCY IMPROVES CHAIN OF THOUGHT REASONING IN LANGUAGE MODELS
    This paper presents a method to improve chain-of-thought reasoning in language models using self-consistency
  • Progressive-Hint Prompting Improves Reasoning in Large Language Models
    Chain-of-Thought Reasoning Chain-of-thought (CoT) prompting (Wei et al ,2022) is a prominent work that demon-strates the multi-step reasoning capacities of LLMs This approach suggests that the reasoning ability can be elicited through a chain of thoughts, where an answer directly fol-lows a question without intermediate reasoning steps Least-
  • LARGE LANGUAGE MODELS CAN SELF-IMPROVE - OpenReview
    Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs We show that our approach im-proves the general reasoning ability of a 540B-parameter LLM (74 4%→82 1% on GSM8K, 78 2%→83 0% on DROP, 90 0%→94 4% on OpenBookQA, and
  • Universal Self-Consistency for Large Language Models
    Self-consistency with chain-of-thought (CoT) prompting has demonstrated remarkable performance gain by utilizing multiple reasoning paths sampled from large language models (LLMs) However, self-consistency relies on heuristics to extract answers and aggregate multiple solutions, which is not applicable to solving tasks with free-form answers
  • Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
    Abstract: We explore how generating a chain of thought---a series of intermediate reasoning steps---significantly improves the ability of large language models to perform complex reasoning In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought
  • Large Language Models Can Self-Improve - OpenReview
    unlabeled questions using Chain-of-Though (CoT) prompting and self-consistency, and fine-tune the LLM using those self-generated so-lutions as target outputs We show that with-out any ground truth label, our approach sig-nificantly improves the general reasoning abil-ity of PaLM 540B model (74 4%→82 1% on GSM8K, 90 0%→94 4% on OpenBookQA,
  • Semantic Self-Consistency: Enhancing Language Model Reasoning via . . .
    Figure 1: Whereas baseline self-consistency comprises three steps: (1) Prompt a model with chain-of-thought, (2) generate n sampled sequences, and (3) choose results based on the most occurring final output, our proposed method, shown above, decides based on the semantic consistency of the employed reasoning path Our assumption is that
  • Large Language Models Can Self-improve - OpenReview
    In this work, we demonstrate that an LLM is also capable of self-improving with only unlabeled datasets We use a pre-trained LLM to generate “high-confidence” rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and fine-tune the LLM using those self-generated solutions as target outputs





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