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Limits for Learning with Language Models (2306.12213v1)

Published 21 Jun 2023 in cs.CL

Abstract: With the advent of LLMs, the trend in NLP has been to train LLMs on vast amounts of data to solve diverse language understanding and generation tasks. The list of LLM successes is long and varied. Nevertheless, several papers provide empirical evidence that LLMs fail to capture important aspects of linguistic meaning. Focusing on universal quantification, we provide a theoretical foundation for these empirical findings by proving that LLMs cannot learn certain fundamental semantic properties including semantic entailment and consistency as they are defined in formal semantics. More generally, we show that LLMs are unable to learn concepts beyond the first level of the Borel Hierarchy, which imposes severe limits on the ability of LMs, both large and small, to capture many aspects of linguistic meaning. This means that LLMs will continue to operate without formal guarantees on tasks that require entailments and deep linguistic understanding.

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Citations (17)
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Summary

  • The paper presents theoretical findings that expose LLMs' difficulty with universal quantification and semantic entailment beyond finite contexts.
  • It employs formal models, including continuation semantics, to demonstrate intrinsic limits in transferring finite training to infinite linguistic structures.
  • Empirical evaluations on models like BERT, RoBERTa, and GPT variants underscore consistent challenges in reliable semantic reasoning.

Limits for Learning with LLMs

Introduction

The paper "Limits for Learning with LLMs" (2306.12213) sets out to establish theoretical limits on the learnability of semantic concepts by LLMs. Despite their apparent successes in multiple NLP tasks, these models face inherent challenges in fully capturing linguistic meaning, particularly concerning universal quantification and semantic entailment. The premise is that though LLMs are widely lauded for their fluency and contextually relevant language generation, they fundamentally lack the ability to learn key semantic properties defined in formal semantics. This essay provides an authoritative summary of the findings presented in the paper, focusing on the implications and evidence supporting these claims.

Theoretical Foundations

The paper hypothesizes that LLMs, due to their training protocols, fall short in learning semantic concepts that extend beyond the first level of the Borel Hierarchy. Specifically, the authors argue that LLMs are unable to learn universal quantification because of the requirement to understand semantic entailment across infinite structures. The paper draws on the expressive capabilities of neural networks to highlight inherent limitations in their learnability with respect to sophisticated semantic tasks.

Semantic Consequence and Universal Quantification

At the core of these limitations is the concept of universal quantification—which underlies semantic consequence in truth-conditional semantics. The authors elaborate that LLMs reportedly fail to understand universal quantifiers like "every", pointing to their inability to extend learning from finite training data to infinite domains, which is required to grasp universal quantification effectively. By representing models as strings using continuation semantics, the paper articulates the challenge LLMs face in dealing with entailments started by universal quantifiers.

Empirical Evidence and Observations

The paper presents empirical studies on models such as BERT and RoBERTa, which demonstrate the models' inconsistencies in reliably determining the truth conditions of semantic content involving universal quantification. Experiments reveal that the models often fail to distinguish between models where universal statements like "Everything is blue" hold true versus models where they do not. These failures are particularly pronounced on longer strings and more complex examples.

Results from GPT Models

Despite being more robust, GPT3.5 and ChatGPT also exhibit instability in handling entailments associated with universal quantification, often due to over-generalization and misinterpretation of underspecified strings. These empirical results significantly bolster the theoretical claims, suggesting that while LLMs may appear competent in certain contexts, they falter in comprehensive semantic tasks that demand understanding complex linguistic constructs.

Theoretical Implications and Future Directions

The paper makes bold claims regarding the inability of LLMs to effectively learn certain Borel sets, which applies to a range of linguistic expressions essential for deep conversational understanding and reasoning. The implications are profound for building models that require nuanced comprehension and entailment, highlighting that the boundaries of statistical learning within LLMs must be reconsidered.

Speculation on AI Advancements

The authors suggest pursuing avenues beyond current LLM approaches, possibly incorporating structured linguistic knowledge into learning frameworks to better align neural architectures with the inferential demands of language semantics. This resonates with growing discourse on integrating domain-specific knowledge to refine AI models' capabilities.

Conclusion

The essay underscores the inherent limits of LLMs in mastering linguistic meaning, attributing failures to their constraints in learning universal quantification and higher-order Borel sets. The paper advocates for a re-evaluation of current approaches in AI to overcome these bounds, urging consideration of novel methodologies that blend statistical learning with formal semantic comprehension. This discourse invites future research to interrogate and expand the horizons of linguistic understanding in artificial intelligence.

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