LLMs' Understanding of Natural Language Revealed (2407.19630v2)

Abstract: LLMs are the result of a massive experiment in bottom-up, data-driven reverse engineering of language at scale. Despite their utility in a number of downstream NLP tasks, ample research has shown that LLMs are incapable of performing reasoning in tasks that require quantification over and the manipulation of symbolic variables (e.g., planning and problem solving); see for example [25][26]. In this document, however, we will focus on testing LLMs for their language understanding capabilities, their supposed forte. As we will show here, the language understanding capabilities of LLMs have been widely exaggerated. While LLMs have proven to generate human-like coherent language (since that's how they were designed), their language understanding capabilities have not been properly tested. In particular, we believe that the language understanding capabilities of LLMs should be tested by performing an operation that is the opposite of 'text generation' and specifically by giving the LLM snippets of text as input and then querying what the LLM "understood". As we show here, when doing so it will become apparent that LLMs do not truly understand language, beyond very superficial inferences that are essentially the byproduct of the memorization of massive amounts of ingested text.

Analyzing the Language Understanding Capabilities of LLMs

The paper entitled "LLMs’ Understanding of Natural Language Revealed" by Walid S. Saba offers a rigorous critique of the perceived language understanding capabilities of LLMs. The paper is grounded in the context that while LLMs have shown impressive abilities in text generation for various NLP tasks, their genuine ability to comprehend language remains contentious. This work argues that despite the apparent success of LLMs, they do not understand language in a manner aligning with human linguistic cognition.

The author emphasizes that LLMs, despite their sophisticated design, lack the competence to perform reasoning tasks that involve symbolic manipulation and conceptual understanding. The central thesis is that the evaluations conducted on LLMs have been misaligned with genuine language understanding tasks. Traditionally, LLMs have been tested through prompts that effectively exploit their design for text generation, yet these tests do not capture the deeper comprehension aspects required in linguistic reasoning.

The paper endeavors to assess the language understanding of LLMs through a method that reverses the typical prompt response evaluation. Instead of simply generating text, LLMs are queried on specific text snippets to examine their interpretative capabilities. The linguistic phenomena focused on in these tests include:

• Intension: The paper argues that LLMs, grounded in deep neural networks (DNNs), inherently operate on extensional principles, which inhibits their capacity to grasp nuanced intensional meanings vital for understanding language semantics.
• Nominal Modification: LLMs struggle to correctly interpret the modifiers’ relations to the head nouns, which significantly distorts their comprehension of semantic content.
• Propositional Attitudes: The inability of LLMs to distinguish between knowledge and belief, or truth, highlights a significant shortcoming in their understanding of nuanced human language constructs.
• Copredication and Metonymy: These areas reveal that LLMs struggle with the simultaneous application of multiple predicates or the use of one entity to implicitly refer to another. The examples illustrate that LLMs fail to recognize multiple reference types and implicit commonsense relationships.
• Reference Resolution: The paper points out that LLMs often misinterpret pronouns or relative pronouns without considering commonsense inferences beyond syntactic structures.

The paper maintains that the aforementioned failures are not merely trivial misunderstandings but consequential errors that question the current narrative on the understanding capability of LLMs. The implications of this research are profound, especially in the context of developing AI systems that need to perform accurate semantic interpretation and reasoning. It suggests that current LLMs might not be adequate for building higher-order language understanding systems and indicates a need for AI research to explore beyond traditional neural network architectures.

Furthermore, the work underscores the utility of commonsense knowledge in linguistic tasks and advocates for a comprehensive approach in designing AI systems that can parallel human-like language comprehension. Despite these highlighted deficiencies, the author acknowledges that LLMs can assist in moving towards enhanced language understanding, suggesting that these models serve as a base for future advancements.

This paper stimulates discussion on the limitations of data-driven models in capturing the complexities of human language cognition. It proposes a pathway towards more integrated AI systems that can truly comprehend language, aligning with human reasoning capabilities. The ongoing and future implications of this research are pivotal for both theoretical explorations and practical advancements in AI-driven language technologies.