Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models

• URL: http://arxiv.org/abs/2310.06117v2
• Date: Tue, 12 Mar 2024 04:38:27 GMT
• Title: Take a Step Back: Evoking Reasoning via Abstraction in Large Language Models
• Authors: Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H. Chi, Quoc V Le and Denny Zhou
• Abstract summary: Step-Back Prompting enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing specific details. Using the concepts and principles to guide reasoning, LLMs significantly improve their abilities in following a correct reasoning path towards the solution.
• Score: 122.19845578690466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Step-Back Prompting, a simple prompting technique that enables LLMs to do abstractions to derive high-level concepts and first principles from instances containing specific details. Using the concepts and principles to guide reasoning, LLMs significantly improve their abilities in following a correct reasoning path towards the solution. We conduct experiments of Step-Back Prompting with PaLM-2L, GPT-4 and Llama2-70B models, and observe substantial performance gains on various challenging reasoning-intensive tasks including STEM, Knowledge QA, and Multi-Hop Reasoning. For instance, Step-Back Prompting improves PaLM-2L performance on MMLU (Physics and Chemistry) by 7% and 11% respectively, TimeQA by 27%, and MuSiQue by 7%.

Related papers

• Understanding the Role of LLMs in Multimodal Evaluation Benchmarks [77.59035801244278]
  This paper investigates the role of the Large Language Model (LLM) backbone in Multimodal Large Language Models (MLLMs) evaluation. Our study encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs. Key findings reveal that some benchmarks allow high performance even without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone.
  arXiv  Detail & Related papers  (2024-10-16T07:49:13Z)
• Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning [2.313664320808389]
  This study proposes an innovative model, Spatial-to-Relational Transformation and Curriculum Q-Learning (S2RCQL) We design a path-planning algorithm based on Q-learning to mitigate the context inconsistency hallucination. Using the Q-value of state-action as auxiliary information for prompts, we correct the hallucinations of LLMs.
  arXiv  Detail & Related papers  (2024-08-23T16:02:54Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Reinforcement Learning Problem Solving with Large Language Models [0.0]
  Large Language Models (LLMs) have an extensive amount of world knowledge, and this has enabled their application in various domains to improve the performance of Natural Language Processing (NLP) tasks. This has also facilitated a more accessible paradigm of conversation-based interactions between humans and AI systems to solve intended problems. We show the practicality of our approach through two detailed case studies for "Research Scientist" and "Legal Matter Intake"
  arXiv  Detail & Related papers  (2024-04-29T12:16:08Z)
• Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks [54.153914606302486]
  In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs) We propose a new paradigm called Hint-enhanced In-Context Learning (HICL) to explore the power of ICL in open-domain question answering.
  arXiv  Detail & Related papers  (2023-11-03T14:39:20Z)
• TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models [52.734140807634624]
  Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs. We introduce TRACE, a novel benchmark designed to evaluate continual learning in LLMs.
  arXiv  Detail & Related papers  (2023-10-10T16:38:49Z)
• Metacognitive Prompting Improves Understanding in Large Language Models [12.112914393948415]
  We introduce Metacognitive Prompting (MP), a strategy inspired by human introspective reasoning processes. We conduct experiments on four prevalent Large Language Models (LLMs) across ten natural language understanding (NLU) datasets. MP consistently outperforms existing prompting methods in both general and domain-specific NLU tasks.
  arXiv  Detail & Related papers  (2023-08-10T05:10:17Z)
• Toward Efficient Language Model Pretraining and Downstream Adaptation via Self-Evolution: A Case Study on SuperGLUE [203.65227947509933]
  This report describes our JDExplore d-team's Vega v2 submission on the SuperGLUE leaderboard. SuperGLUE is more challenging than the widely used general language understanding evaluation (GLUE) benchmark, containing eight difficult language understanding tasks.
  arXiv  Detail & Related papers  (2022-12-04T15:36:18Z)
• Large Language Models are Zero-Shot Reasoners [28.6899375595088]
  Chain of thought (CoT) prompting is a technique for eliciting complex multi-step reasoning through step-by-step answer examples. We show that LLMs are decent zero-shot reasoners by simply adding Let's think step by step'' before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances.
  arXiv  Detail & Related papers  (2022-05-24T09:22:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.