Temporal Knowledge Question Answering via Abstract Reasoning Induction

• URL: http://arxiv.org/abs/2311.09149v2
• Date: Fri, 17 May 2024 03:17:02 GMT
• Title: Temporal Knowledge Question Answering via Abstract Reasoning Induction
• Authors: Ziyang Chen, Dongfang Li, Xiang Zhao, Baotian Hu, Min Zhang,
• Abstract summary: This study addresses the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs) We propose Abstract Reasoning Induction (ARI) framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. Our approach achieves remarkable improvements, with relative gains of 29.7% and 9.27% on two temporal QA datasets.
• Score: 32.08799860090592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we address the challenge of enhancing temporal knowledge reasoning in Large Language Models (LLMs). LLMs often struggle with this task, leading to the generation of inaccurate or misleading responses. This issue mainly arises from their limited ability to handle evolving factual knowledge and complex temporal logic. To overcome these limitations, we propose Abstract Reasoning Induction (ARI) framework, which divides temporal reasoning into two distinct phases: Knowledge-agnostic and Knowledge-based. This framework offers factual knowledge support to LLMs while minimizing the incorporation of extraneous noisy data. Concurrently, informed by the principles of constructivism, ARI provides LLMs the capability to engage in proactive, self-directed learning from both correct and incorrect historical reasoning samples. By teaching LLMs to actively construct knowledge and methods, it can significantly boosting their temporal reasoning abilities. Our approach achieves remarkable improvements, with relative gains of 29.7% and 9.27% on two temporal QA datasets, underscoring its efficacy in advancing temporal reasoning in LLMs. The code can be found at https://github.com/czy1999/ARI-QA

Related papers

• Unveiling Factual Recall Behaviors of Large Language Models through Knowledge Neurons [13.266817091775042]
  We investigate whether Large Language Models (LLMs) actively recall or retrieve their internal repositories of factual knowledge when faced with reasoning tasks. We reveal that LLMs fail to harness the critical factual associations under certain circumstances. We assess the effect of Chain-of-Thought (CoT) prompting, a powerful technique for addressing complex reasoning tasks.
  arXiv  Detail & Related papers  (2024-08-06T15:07:08Z)
• Reasoning with Large Language Models, a Survey [2.831296564800826]
  This paper reviews the rapidly expanding field of prompt-based reasoning with LLMs. Our taxonomy identifies different ways to generate, evaluate, and control multi-step reasoning. We find that self-improvement, self-reflection, and some meta abilities of the reasoning processes are possible through the judicious use of prompts.
  arXiv  Detail & Related papers  (2024-07-16T08:49:35Z)
• Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? [70.19200858203388]
  Temporal reasoning is fundamental for large language models to comprehend the world. CoTempQA is a benchmark containing four co-temporal scenarios. Our experiments reveal a significant gap between the performance of current LLMs and human-level reasoning.
  arXiv  Detail & Related papers  (2024-06-13T12:56:21Z)
• CausalBench: A Comprehensive Benchmark for Causal Learning Capability of LLMs [27.362012903540492]
  The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning. The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning.
  arXiv  Detail & Related papers  (2024-04-09T14:40:08Z)
• Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models [51.72963030032491]
  Knowledge documents for large language models (LLMs) may conflict with the memory of LLMs due to outdated or incorrect knowledge. We construct a new dataset, dubbed KNOT, for knowledge conflict resolution examination in the form of question answering.
  arXiv  Detail & Related papers  (2024-04-04T16:40:11Z)
• Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance [38.49506722997423]
  Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios. Despite this, when tasked with simple questions supported by a generic fact, LLMs often fail to provide consistent and precise answers. This has sparked a vigorous debate about whether LLMs are genuinely reasoning or merely memorizing.
  arXiv  Detail & Related papers  (2024-03-14T04:06:13Z)
• When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation [66.01754585188739]
  Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence.
  arXiv  Detail & Related papers  (2024-02-18T04:57:19Z)
• KnowTuning: Knowledge-aware Fine-tuning for Large Language Models [83.5849717262019]
  We propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs. KnowTuning generates more facts with less factual error rate under fine-grained facts evaluation.
  arXiv  Detail & Related papers  (2024-02-17T02:54:32Z)
• Is Knowledge All Large Language Models Needed for Causal Reasoning? [11.476877330365664]
  This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence. We propose a novel causal attribution model that utilizes do-operators" for constructing counterfactual scenarios.
  arXiv  Detail & Related papers  (2023-12-30T04:51:46Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [91.30946119104111]
  We show that large language models (LLMs) possess unwavering confidence in their capabilities to respond to questions. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We also find that LLMs have a propensity to rely on the provided retrieval results when formulating answers.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Rethinking with Retrieval: Faithful Large Language Model Inference [91.66406351103484]
  We propose a novel post-processing approach, rethinking with retrieval (RR) RR retrieves relevant external knowledge based on the reasoning steps obtained from the chain-of-thought prompting. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks.
  arXiv  Detail & Related papers  (2022-12-31T22:35:34Z)

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.