Temporal Blind Spots in Large Language Models

• URL: http://arxiv.org/abs/2401.12078v1
• Date: Mon, 22 Jan 2024 16:20:14 GMT
• Title: Temporal Blind Spots in Large Language Models
• Authors: Jonas Wallat, Adam Jatowt, Avishek Anand
• Abstract summary: Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. This study investigates the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding.
• Score: 20.631107338678234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have recently gained significant attention due to their unparalleled ability to perform various natural language processing tasks. These models, benefiting from their advanced natural language understanding capabilities, have demonstrated impressive zero-shot performance. However, the pre-training data utilized in LLMs is often confined to a specific corpus, resulting in inherent freshness and temporal scope limitations. Consequently, this raises concerns regarding the effectiveness of LLMs for tasks involving temporal intents. In this study, we aim to investigate the underlying limitations of general-purpose LLMs when deployed for tasks that require a temporal understanding. We pay particular attention to handling factual temporal knowledge through three popular temporal QA datasets. Specifically, we observe low performance on detailed questions about the past and, surprisingly, for rather new information. In manual and automatic testing, we find multiple temporal errors and characterize the conditions under which QA performance deteriorates. Our analysis contributes to understanding LLM limitations and offers valuable insights into developing future models that can better cater to the demands of temporally-oriented tasks. The code is available\footnote{https://github.com/jwallat/temporalblindspots}.

Related papers

• Enhancing Temporal Understanding in LLMs for Semi-structured Tables [50.59009084277447]
  We conduct a comprehensive analysis of temporal datasets to pinpoint the specific limitations of large language models (LLMs) Our investigation leads to enhancements in TempTabQA, a dataset specifically designed for temporal temporal question answering. We introduce a novel approach, C.L.E.A.R. to strengthen LLM capabilities in this domain.
  arXiv  Detail & Related papers  (2024-07-22T20:13:10Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• STBench: Assessing the Ability of Large Language Models in Spatio-Temporal Analysis [12.582867572800488]
  Large language models (LLMs) hold promise for reforming the methodology of rapid rapid evolution of large language models. This paper builds the benchmark dataset STBench, containing 13 distinct computation tasks and over 60,000 QA pairs. Experimental results reveal that existing LLMs show remarkable performance on knowledge comprehension and distinct-temporal reasoning tasks.
  arXiv  Detail & Related papers  (2024-06-27T10:34:02Z)
• Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning [20.066249913943405]
  Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors. We introduce novel synthetic datasets specifically designed to assess LLM temporal reasoning abilities in various scenarios. Our findings provide valuable insights into the strengths and weaknesses of current LLMs in temporal reasoning tasks.
  arXiv  Detail & Related papers  (2024-06-13T14:31:19Z)
• Guiding LLM Temporal Logic Generation with Explicit Separation of Data and Control [0.7580487359358722]
  Temporal logics are powerful tools that are widely used for the synthesis and verification of reactive systems. Recent progress on Large Language Models has the potential to make the process of writing such specifications more accessible.
  arXiv  Detail & Related papers  (2024-06-11T16:07:24Z)
• Prompting Large Language Models with Knowledge Graphs for Question Answering Involving Long-tail Facts [50.06633829833144]
  Large Language Models (LLMs) are effective in performing various NLP tasks, but struggle to handle tasks that require extensive, real-world knowledge. We propose a benchmark that requires knowledge of long-tail facts for answering the involved questions. Our experiments show that LLMs alone struggle with answering these questions, especially when the long-tail level is high or rich knowledge is required.
  arXiv  Detail & Related papers  (2024-05-10T15:10:20Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilities [46.02234423159257]
  Large language models (LLMs) have been applied in many fields and have developed rapidly in recent years. Recent works treat large language models as emphzero-shot time series reasoners without further fine-tuning. Our study shows that LLMs perform well in predicting time series with clear patterns and trends, but face challenges with datasets lacking periodicity.
  arXiv  Detail & Related papers  (2024-02-16T17:15:28Z)
• 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)
• MenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models [17.322480769274062]
  Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. This paper constructs Multiple Sensitive Factors Time QA (MenatQA) with total 2,853 samples for evaluating the time comprehension and reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2023-10-08T13:19:52Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)

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.