In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models

• URL: http://arxiv.org/abs/2409.15454v1
• Date: Mon, 23 Sep 2024 18:30:31 GMT
• Title: In-Context Learning May Not Elicit Trustworthy Reasoning: A-Not-B Errors in Pretrained Language Models
• Abstract summary: A-Not-B error is a phenomenon seen in infants where they repeat a previously rewarded behavior despite well-observed changed conditions. This highlights their lack of inhibitory control -- the ability to stop a habitual or impulsive response. We show that state-of-the-art LLMs (like Llama3-8b) perform consistently well with in-context learning (ICL) but make errors and show a significant drop of as many as 83.3% in reasoning tasks when the context changes trivially.
• Score: 12.700065865071519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in artificial intelligence have led to the creation of highly capable large language models (LLMs) that can perform tasks in a human-like manner. However, LLMs exhibit only infant-level cognitive abilities in certain areas. One such area is the A-Not-B error, a phenomenon seen in infants where they repeat a previously rewarded behavior despite well-observed changed conditions. This highlights their lack of inhibitory control -- the ability to stop a habitual or impulsive response. In our work, we design a text-based multi-choice QA scenario similar to the A-Not-B experimental settings to systematically test the inhibitory control abilities of LLMs. We found that state-of-the-art LLMs (like Llama3-8b) perform consistently well with in-context learning (ICL) but make errors and show a significant drop of as many as 83.3% in reasoning tasks when the context changes trivially. This suggests that LLMs only have inhibitory control abilities on par with human infants in this regard, often failing to suppress the previously established response pattern during ICL.

Related papers

• From Subsumption to Satisfiability: LLM-Assisted Active Learning for OWL Ontologies [1.1351175212666613]
  In active learning, membership queries (MQs) allow a learner to pose questions to a teacher.<n>We reformulate each candidate axiom into its corresponding counter-concept and verbalise it in controlled natural language.<n>We introduce Large Language Models (LLMs) as a third component that provides real-world examples approximating an instance of the counter-concept.
  arXiv  Detail & Related papers  (2026-04-17T20:05:30Z)
• Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs [18.37387666170851]
  We propose Erasable Reinforcement Learning (ERL), a novel framework that transforms fragile reasoning into a robust process.<n>ERL explicitly identifies faulty steps, erases them, and regenerates reasoning in place, preventing defective logic from propagating through the reasoning chain.<n>Models trained with ERL, termed ESearch, achieve substantial improvements on HotpotQA, MuSiQue, 2Wiki, and Bamboogle.
  arXiv  Detail & Related papers  (2025-10-01T13:10:36Z)
• Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs [0.0]
  Self-correction is an important capability for large language models (LLMs)<n>While LLMs can identify error in user input, they exhibit a systematic 'Self-Correction Blind Spot'<n>Testing 14 models, we find an average 64.5% blind spot rate.<n>Remarkably, simply appending "Wait" reduces blind spots by 89.3%, suggesting that the capability exists but requires activation.
  arXiv  Detail & Related papers  (2025-07-03T16:41:30Z)
• Truly Assessing Fluid Intelligence of Large Language Models through Dynamic Reasoning Evaluation [75.26829371493189]
  Large language models (LLMs) have demonstrated impressive reasoning capacities that mirror human-like thinking.<n>Existing reasoning benchmarks either focus on domain-specific knowledge (crystallized intelligence) or lack interpretability.<n>We propose DRE-Bench, a dynamic reasoning evaluation benchmark grounded in a hierarchical cognitive framework.
  arXiv  Detail & Related papers  (2025-06-03T09:01:08Z)
• Causality for Large Language Models [37.10970529459278]
  Large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks. Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it. This survey aims to explore how causality can enhance LLMs at every stage of their lifecycle.
  arXiv  Detail & Related papers  (2024-10-20T07:22:23Z)
• Automatic Curriculum Expert Iteration for Reliable LLM Reasoning [60.60318625779015]
  Hallucinations (i.e., generating plausible but inaccurate content) and laziness (i.e. excessive refusals or defaulting to "I don't know") persist as major challenges in LLM reasoning. Current efforts to reduce hallucinations primarily focus on factual errors in knowledge-grounded tasks, often neglecting hallucinations related to faulty reasoning. We propose Automatic Curriculum Expert Iteration (Auto-CEI) to enhance LLM reasoning and align responses to the model's capabilities.
  arXiv  Detail & Related papers  (2024-10-10T05:43:07Z)
• Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation [63.064204206220936]
  Foundational Large Language Models (LLMs) have changed the way we perceive technology. They have been shown to excel in tasks ranging from poem writing to coding to essay generation and puzzle solving. With the incorporation of image generation capability, they have become more comprehensive and versatile AI tools. Currently identified flaws include hallucination, biases, and bypassing restricted commands to generate harmful content.
  arXiv  Detail & Related papers  (2024-08-27T14:40:16Z)
• Metacognitive Myopia in Large Language Models [0.0]
  Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally inherent stereotypes, cloud moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework that can account for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that a lack of the two components of metacognition, monitoring and control, causes five symptoms of metacognitive myopia in LLMs.
  arXiv  Detail & Related papers  (2024-08-10T14:43:57Z)
• 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)
• Uncertainty Quantification for In-Context Learning of Large Language Models [52.891205009620364]
  In-context learning has emerged as a groundbreaking ability of Large Language Models (LLMs) We propose a novel formulation and corresponding estimation method to quantify both types of uncertainties. The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion.
  arXiv  Detail & Related papers  (2024-02-15T18:46:24Z)
• Beyond Task Performance: Evaluating and Reducing the Flaws of Large Multimodal Models with In-Context Learning [105.77733287326308]
  We evaluate 10 recent open-source LMMs from 3B up to 80B parameter scale, on 5 different axes; hallucinations, abstention, compositionality, explainability and instruction following. We explore the training-free in-context learning (ICL) as a solution, and study how it affects these limitations. Based on our ICL study, (3) we push ICL further and propose new multimodal ICL variants such as; Multitask-ICL, Chain-of-Hindsight-ICL, and Self-Correcting-ICL.
  arXiv  Detail & Related papers  (2023-10-01T12:02:59Z)
• 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.