PaperlayerView exact revision on arXiv

Source-linked AI summary

Do Large Language Models Understand Logic or Just Mimick Context?

Junbing Yan, Chengyu Wang, Jun Huang, Wei Zhang

arXiv:2402.12091v1cs.CLcs.AI

TL;DR

The paper asks whether LLMs genuinely understand logical rules or instead become more likely to reach correct answers through context. It probes this with counterfactual changes to example content and logical concepts, finding that contextual performance gains do not establish genuine understanding.

  • Problem

    Although prompting methods improve LLM performance on logical-reasoning tasks, whether models grasp underlying rules or merely converge more effectively on correct answers remains unresolved.

    Source: §1 · p. 1

  • Method

    The study uses counterfactual changes to in-context example components and logical definitions to test reasoning across LogicalCOT tasks.

    Source: §1 · p. 2, §4.2 · p. 4

  • Results

    LLMs show limited adaptation to changed logical definitions, while in-context examples improve reasoning performance and larger models are resilient to some example-content replacements.

    Source: §1 · p. 2

  • Takeaways & Limitations

    Improved performance through in-context learning and Chain-of-Thought does not amount to genuine understanding of logical operations or definitions.

    Source: §5 · p. 8

    The authors have not identified an in-context-learning method that markedly improves comprehension, and note that next-token pretraining may not suit tasks requiring longer dependencies and global information.

    Source: Limitations · pp. 8–9

Abstract

from arXiv · show

Over the past few years, the abilities of large language models (LLMs) have received extensive attention, which have performed exceptionally well in complicated scenarios such as logical reasoning and symbolic inference. A significant factor contributing to this progress is the benefit of in-context learning and few-shot prompting. However, the reasons behind the success of such models using contextual reasoning have not been fully explored. Do LLMs have understand logical rules to draw inferences, or do they ``guess'' the answers by learning a type of probabilistic mapping through context? This paper investigates the reasoning capabilities of LLMs on two logical reasoning datasets by using counterfactual methods to replace context text and modify logical concepts. Based on our analysis, it is found that LLMs do not truly understand logical rules; rather, in-context learning has simply enhanced the likelihood of these models arriving at the correct answers. If one alters certain words in the context text or changes the concepts of logical terms, the outputs of LLMs can be significantly disrupted, leading to counter-intuitive responses. This work provides critical insights into the limitations of LLMs, underscoring the need for more robust mechanisms to ensure reliable logical reasoning in LLMs.

1 Introduction

The introduction asks whether LLMs genuinely grasp logical rules or instead become more likely to answer correctly through contextual examples. It tests this distinction by perturbing example content and logical definitions, and reports gains from in-context examples alongside limited rule adaptation.

1 Introduction

  • Chain-of-Thought in-context examples markedly improved logical-reasoning performance across models ranging from 7B to 200B parameters.The authors also report clearer, more normative, and more accurate generated responses.Source: §1 · p. 2
  • 70B and 200B models maintained accuracy when in-context example content was replaced, whereas 7B and 13B models declined without standard examples.The introduction describes resilience to replacements involving extraneous text, reasoning chains, and patterns.Source: §1 · p. 2
  • Models showed minimal successful adaptation when logical-symbol definitions changed, supporting the authors’ conclusion that outputs rely on probabilistic associations rather than genuine rule comprehension.Prompt or in-context guidance yielded limited improvement in successful adjustments.Source: §1 · p. 2

2 Related Work

Related work describes large language models and counterfactual prompting, then positions this study as using counterfactuals to examine LLM understanding of logical rules and definitions.

2.1 Large Language Models

  • LLMs’ scale and generative capabilities have grown, with training commonly framed as pre-training, supervised fine-tuning, and reinforcement learning from human feedback (Ouyang et al., 2022b).The section also names LLaMA, LLaMA 2, BLOOM, BLOOMZ, Galactica, GLM, and Pythia as prominent open-source models.Source: §2.1 · p. 2

2.2 Counterfactual Prompt

  • Counterfactual text generation has been studied in domains including courts, dialogue, natural language inference, and named entity recognition, and may help interpret LLMs.Source: §2.2 · p. 2

2.3 Logical Reasoning

  • The authors present their work as the first use of counterfactual methods to examine how far expansive models understand logical rules and definitions.Earlier work reported improved metrics on logic tasks, including with chain-of-thought prompting (Wei et al., 2022b).Source: §2.3 · pp. 2–3

3 Method

The method separates in-context examples into components and tests them through replacement and logical-definition changes to probe their roles in reasoning and rule understanding.

3 Method

  • In-context examples are divided into text, reasoning chain, pattern, and supplementary natural-language definitions of logical symbols.Text contains the question and given information; reasoning chain is the thought process; pattern includes key symbols, answers, and other special text.Source: §3 · p. 3
  • Replacement swaps text, reasoning chains, or patterns with same-domain examples or unrelated text to probe component importance, robustness, and pattern understanding.Table 1 compares raw Entailment Bank data with examples after replacement or modification; in-domain replacements are blue and out-of-domain replacements red.Source: §3 · p. 3, Table 1 · p. 4
  • Modification redefines logical concepts such as AND and OR, testing whether the model correctly inverts their meanings in its output.The authors expect low success under token-level probabilistic association and accurate interchange under genuine rule comprehension.Source: §3 · p. 3

4 Experiment

The experiments evaluate LLMs’ logical-rule understanding across model sizes and reasoning tasks, using LogicalCOT and replacement data from in-domain examples or Wikipedia.

4.1 Models

  • Experiments test LLaMA2 models from 7B to 70B parameters and Qwen models from 7B to 200B parameters without training them.The Qwen models were pretrained on up to 3 trillion multilingual tokens; the 200B model was the largest available to the authors.Source: §4.1 · p. 4

4.2 Datasets

  • LogicalCOT provides Folio language-to-logic, Entailment Bank inference-chain, and MRC tasks; Figure 2 frames Qwen performance on Entailment Bank by replacement part.MRC provides a passage and question and asks the model to identify the correct answer.Source: §4.2 · pp. 4–5, Figure 2 · p. 5
  • In-domain replacements use other samples, while out-of-domain replacements use randomly selected paragraphs from English Wikipedia dated 2020/03/01.The source description identifies 2.6 billion documents for the out-of-domain data.Source: §4.2 · p. 5

4.3 Influence of In-Context Examples

In-context examples are associated with higher prediction accuracy, with larger models benefiting more as the number of examples increases. Using eight examples does not significantly improve on four, while examples also help standardize output format.

4.3 Influence of In-Context Examples

  • Accuracy rises with the number of in-context examples, although eight examples do not significantly outperform four.The accuracy relationship is positive, but the reported gain from increasing the examples from four to eight is not significant.Source: §4.3 · p. 5
  • The accuracy benefit from additional in-context examples becomes more pronounced as model size grows from 7B to 200B parameters.The passage describes larger models as benefiting disproportionately from an increased number of examples.Source: §4.3 · p. 5
  • In-context examples help standardize responses into the expected output structure.Source: §4.3 · p. 5
  • Figure 3 frames replacement-part effects on Entailment Bank performance for LLaMA models.Source: Figure 3 · p. 5

4.4 Influence of Texts

Smaller models lose accuracy when example context is modified, while 70B and 200B models are largely robust to in- and out-of-domain text changes. Yet out-of-domain data can match or sometimes exceed in-domain data on in-domain examples, especially for smaller models.

4.4 Influence of Texts

  • 70B and 200B models show negligible accuracy impacts from in- or out-of-domain context changes, while 7B and 13B models decline notably, especially with out-of-domain text.The authors hypothesize that larger models retain salient information despite perturbations, whereas smaller models are more susceptible to textual interference.Source: §4.4 · p. 5
  • On in-domain text, models using out-of-domain data match or sometimes surpass results from in-domain data, with the improvement more pronounced in smaller models.The authors hypothesize that greater divergence from the original data distribution may help models identify irrelevant text and focus on task-relevant content.Source: §4.4 · pp. 5–6
  • Table 2 reports accuracy results for LLaMA and Qwen models on the logical datasets.Its caption identifies the metrics as accuracy percentages and defines “w/o” as no in-context example and “Raw” as regular in-context examples.Source: Table 2 · p. 6

4.5 Influence of Reasoning Chain

Replacing reasoning chains reduces accuracy more for smaller models than larger ones, but the size pattern reverses for out-of-domain chains. The authors attribute these differences to how models use text and intermediate reasoning.

4.5 Influence of Reasoning Chain

  • With in-domain reasoning-chain replacement, accuracy falls substantially for 7B and 13B models but only slightly for 70B and 200B models.The decline from chain replacement is less pronounced than the decline caused by replacing the text that defines the problem and potential solutions.Source: §4.5 · p. 6
  • With out-of-domain chains on Entailment Bank, 7B and 13B models show only modest reasoning declines, while 70B and 200B models decline more substantially.The authors hypothesize that smaller models may ignore the chains and associate input directly with answers, whereas larger models are more affected by chain content.Source: §4.5 · pp. 6–7
  • Figure 4 reports results across LLaMA and Qwen scales and settings on Entailment Bank, using four in-context samples per target example.Source: Figure 4 · p. 7

4.6 Influence of Pattern

Pattern substitutions can preserve accuracy even when logical connectives are replaced, suggesting that models may rely on surface-level linkage cues. When logical definitions are changed, however, models adapt poorly, with modification rates below 1% for smaller models and below 5% for larger ones.

4.6 Influence of Pattern

  • Replacing [AND] with [OR] can interchange corresponding terms without significantly reducing accuracy, and non-sequitur substitutions such as [AND] with [APPLE/BANANA] also do not meaningfully reduce accuracy.Source: §4.6 · p. 7
  • Models appear to recognize bracketed syntactic links between sentences more than the nuanced semantics of connectives such as [AND] and [OR].The authors suggest that surface-level cues may maintain coherence without deep processing of logical relationships.Source: §4.6 · p. 7

4.7 Test for Logical Understanding Ability

  • The logical-predicate test adds a prompt after an in-context example and evaluates whether models revise connective usage under modified definitions.The expected response replaces conjunctions previously expressed with [AND] or [OR] according to the revised meanings.Source: §4.7 · p. 7
  • After logical predicates are redefined, modification rates remain below 1% for 7B and 13B models and below 5% for larger models.The authors describe the smaller models’ grasp of logical semantics as limited and the larger models’ predicate understanding as rudimentary and unsatisfactory.Source: §4.7 · p. 8
  • A Qwen-200B-Chat example acknowledges that the demonstrations conflict with the revised AND/OR definitions, then reasons from the modified meaning of OR.Source: §4.7 · p. 8

4.8 Enhancing Logical Comprehension Ability for LLM

The section tests whether guidance can improve logical-operator swaps without further training. Contextualized examples that mirror the requested modification improve performance more than prompt guidance alone, while indicating continued dependence on examples.

4.8 Enhancing Logical Comprehension Ability for LLM

  • Prompt-based instructions to interchange [OR] and [AND] improved operator swaps, but the gains did not meet expectations.The instructions aimed to preserve grammatical correctness and logical consistency.Source: §4.8 · p. 8
  • Adding illustrative modification examples to guiding prompts increased the model’s modification rate.The reported increase indicates substantial dependence on contextually provided examples.Source: §4.8 · p. 8
  • Examples matching the anticipated modification format are needed for significant improvement; definitions or prompts alone are inadequate against recurring operator-predicate patterns.The section identifies reducing dependence on context-based examples as future research.Source: §4.8 · p. 8

5 Conclusion

Across models with 7B to 200B parameters, size alone does not guarantee understanding of logical constructs. In-context learning and methods such as COT can improve performance without demonstrating genuine understanding of logical operations or definitions.

5 Conclusion

  • Models ranging from 7B to 200B parameters were evaluated, and size alone did not guarantee profound understanding of logical constructs.The observed performance disparity between smaller and larger models did not establish that larger models understood the rules.Source: §5 · p. 8
  • Larger models may show traces of semantic learning, but their outputs often lack logical validity when logical predicates are swapped.Source: §5 · p. 8
  • In-context learning and methods such as COT may improve logical-reasoning performance without establishing genuine understanding of logical operations and definitions.The findings also do not establish that these methods confer logical-reasoning capability.Source: §5 · p. 8

Limitations

The limitations section reports that gains from prompts and in-context examples remain marginal, and that next-token prediction may not suit logical tasks requiring long-span dependencies and global information.

Limitations

  • Prompts and in-context examples have produced only marginal enhancement, and no method has yet markedly improved comprehension through in-context learning.Source: Limitations · p. 8
  • Next-token prediction may not be ideally suited to logical tasks requiring longer-span dependencies and integration of global information.Source: Limitations · pp. 8–9
  • The authors identify an alternative pre-training strategy tailored to these logical-task requirements as a promising direction for future research.Source: Limitations · p. 9
Loading 2402.12091v1…