Metacognitive Retrieval-Augmented Large Language Models
- URL: http://arxiv.org/abs/2402.11626v1
- Date: Sun, 18 Feb 2024 15:41:31 GMT
- Title: Metacognitive Retrieval-Augmented Large Language Models
- Authors: Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, Zhicheng Dou
- Abstract summary: This paper introduces MetaRAG, an approach that combines the retrieval-augmented generation process with metacognition.
By integrating this, MetaRAG enables the model to monitor, evaluate, and plan its response strategies.
Empirical evaluations show that MetaRAG significantly outperforms existing methods.
- Score: 43.57020180706832
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Retrieval-augmented generation have become central in natural language
processing due to their efficacy in generating factual content. While
traditional methods employ single-time retrieval, more recent approaches have
shifted towards multi-time retrieval for multi-hop reasoning tasks. However,
these strategies are bound by predefined reasoning steps, potentially leading
to inaccuracies in response generation. This paper introduces MetaRAG, an
approach that combines the retrieval-augmented generation process with
metacognition. Drawing from cognitive psychology, metacognition allows an
entity to self-reflect and critically evaluate its cognitive processes. By
integrating this, MetaRAG enables the model to monitor, evaluate, and plan its
response strategies, enhancing its introspective reasoning abilities. Through a
three-step metacognitive regulation pipeline, the model can identify
inadequacies in initial cognitive responses and fixes them. Empirical
evaluations show that MetaRAG significantly outperforms existing methods.
Related papers
- Recursive Introspection: Teaching Language Model Agents How to Self-Improve [30.086494067593268]
We develop RISE: Recursive IntroSpEction, an approach for fine-tuning large language models.
Our experiments show that RISE enables Llama2, Llama3, and Mistral models to improve themselves with more turns on math reasoning tasks.
arXiv Detail & Related papers (2024-07-25T17:35:59Z) - What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models [50.97705264224828]
We propose Counterfactual Inception, a novel method that implants counterfactual thinking into Large Multi-modal Models.
We aim for the models to engage with and generate responses that span a wider contextual scene understanding.
Comprehensive analyses across various LMMs, including both open-source and proprietary models, corroborate that counterfactual thinking significantly reduces hallucination.
arXiv Detail & Related papers (2024-03-20T11:27:20Z) - Metacognition is all you need? Using Introspection in Generative Agents
to Improve Goal-directed Behavior [0.0]
We introduce a metacognition module for generative agents, enabling them to observe their own thought processes and actions.
We tested the metacognition module on a variety of scenarios, including a situation where generative agents must survive a zombie apocalypse.
arXiv Detail & Related papers (2024-01-09T15:00:47Z) - CogGPT: Unleashing the Power of Cognitive Dynamics on Large Language
Models [27.81862535460598]
We propose the concept of the cognitive dynamics of large language models (LLMs) and present a corresponding task with the inspiration of longitudinal studies.
Towards the task, we develop CogBench, a novel benchmark to assess the cognitive dynamics of LLMs and validate it through participant surveys.
We introduce CogGPT for the task, which features an innovative iterative cognitive mechanism aimed at enhancing lifelong cognitive dynamics.
arXiv Detail & Related papers (2024-01-06T03:59:59Z) - From Heuristic to Analytic: Cognitively Motivated Strategies for
Coherent Physical Commonsense Reasoning [66.98861219674039]
Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions.
Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
arXiv Detail & Related papers (2023-10-24T19:46:04Z) - Large Language Models for Information Retrieval: A Survey [57.7992728506871]
Information retrieval has evolved from term-based methods to its integration with advanced neural models.
Recent research has sought to leverage large language models (LLMs) to improve IR systems.
We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
arXiv Detail & Related papers (2023-08-14T12:47:22Z) - Improving Factuality and Reasoning in Language Models through Multiagent
Debate [95.10641301155232]
We present a complementary approach to improve language responses where multiple language model instances propose and debate their individual responses and reasoning processes over multiple rounds to arrive at a common final answer.
Our findings indicate that this approach significantly enhances mathematical and strategic reasoning across a number of tasks.
Our approach may be directly applied to existing black-box models and uses identical procedure and prompts for all tasks we investigate.
arXiv Detail & Related papers (2023-05-23T17:55:11Z) - Variational Empowerment as Representation Learning for Goal-Based
Reinforcement Learning [114.07623388322048]
We discuss how the standard goal-conditioned RL (GCRL) is encapsulated by the objective variational empowerment.
Our work lays a novel foundation from which to evaluate, analyze, and develop representation learning techniques in goal-based RL.
arXiv Detail & Related papers (2021-06-02T18:12:26Z)
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.