洞察 - Computational Complexity - # Grammaticality Judgment in Large Language Models

Large Language Models Still Struggle to Comprehend Language on Par with Humans Despite Scaling

Q: What other factors, beyond model size, could contribute to bridging the gap between LLM and human language abilities?

In addition to model size, several other factors could play a role in bridging the gap between Large Language Models (LLMs) and human language abilities. One crucial factor is the quality and diversity of the training data. While LLMs are trained on vast amounts of text data, the quality, relevance, and diversity of this data can significantly impact their language understanding capabilities. Ensuring that the training data covers a wide range of linguistic phenomena, contexts, and styles can help LLMs develop a more comprehensive understanding of language. Another important factor is the architecture and design of the models. Improvements in the architecture, such as incorporating mechanisms for handling long-range dependencies, capturing context more effectively, and enhancing the ability to reason and infer meaning, can contribute to better language understanding. Additionally, advancements in model training techniques, such as curriculum learning, multi-task learning, and transfer learning, can also enhance the performance of LLMs. Furthermore, incorporating explicit linguistic knowledge and constraints into the training process can help LLMs develop a more linguistically informed understanding of language. By integrating linguistic principles, rules, and constraints into the training objectives, LLMs may be better equipped to handle complex linguistic phenomena and make more accurate language predictions.

Q: How can the differences in language learning between humans and LLMs be further explored and addressed?

To further explore and address the differences in language learning between humans and LLMs, researchers can consider the following approaches: Fine-grained Linguistic Evaluation: Conducting detailed linguistic analyses and evaluations of LLMs across a wide range of linguistic tasks and phenomena can provide insights into their strengths and limitations. By systematically testing LLMs on various linguistic tasks, researchers can identify specific areas where LLMs struggle and develop targeted interventions to improve their performance. Incorporating Linguistic Theory: Integrating linguistic theories and principles into the design and training of LLMs can help align their language abilities more closely with human linguistic cognition. By grounding LLMs in linguistic theory, researchers can ensure that these models capture essential aspects of human language processing and understanding. Comparative Studies: Conducting comparative studies between LLMs and human language processing can shed light on the differences and similarities in their language abilities. By systematically comparing the performance of LLMs and humans on various language tasks, researchers can identify areas where LLMs excel and where they fall short compared to human language understanding. Interdisciplinary Collaboration: Collaborating across disciplines such as linguistics, cognitive science, and artificial intelligence can provide a holistic perspective on the differences in language learning between humans and LLMs. By bringing together experts from different fields, researchers can leverage diverse insights and methodologies to address the challenges in bridging the gap between LLMs and human language abilities.

Q: What are the implications of the persistent gap in language understanding for the use of LLMs as cognitive theories of language?

The persistent gap in language understanding between LLMs and humans has significant implications for the use of LLMs as cognitive theories of language. Some of the key implications include: Limitations in Generalization: The inability of LLMs to consistently exhibit human-like language understanding raises questions about their capacity to serve as accurate cognitive theories of language. If LLMs cannot capture the full range of linguistic phenomena and nuances present in human language, their utility as comprehensive cognitive models may be limited. Validity of Linguistic Theories: The discrepancies between LLMs and human language abilities highlight the challenges in using LLMs to validate or refine linguistic theories. If LLMs struggle to exhibit linguistic behaviors that align with established linguistic principles, their effectiveness in informing linguistic theory may be compromised. Interpretation of Results: Researchers and practitioners using LLMs as cognitive theories of language must interpret the model outputs with caution, considering the inherent differences in language understanding between LLMs and humans. Understanding the limitations of LLMs in capturing human-like language abilities is essential for drawing accurate conclusions from model predictions. Future Research Directions: The persistent gap in language understanding underscores the need for continued research and development in the field of natural language processing. Addressing the challenges in bridging the differences between LLMs and human language abilities can drive advancements in model design, training methodologies, and linguistic theory, paving the way for more accurate and reliable cognitive models of language.

核心概念

Even the largest language models tested, such as ChatGPT-4, do not demonstrate linguistic abilities on par with humans in grammaticality judgment tasks, despite significant scaling in model size.

摘要

The study investigates whether scaling in terms of model size (number of parameters) can bridge the gap between the language abilities of Large Language Models (LLMs) and humans. Three LLMs - Bard (137 billion parameters), ChatGPT-3.5 (175 billion), and ChatGPT-4 (1.5 trillion) - were tested on a grammaticality judgment task featuring four linguistic phenomena: anaphora, center embedding, comparatives, and negative polarity.

The results show that while the largest model, ChatGPT-4, outperforms the smaller models in accuracy, it still exhibits significant differences compared to human performance:

Accuracy: ChatGPT-4 is more accurate than humans for grammatical sentences, but less accurate for ungrammatical sentences. Its accuracy also decreases for ungrammatical sentences upon repeated exposure.
Stability: ChatGPT-4's responses are less stable than humans, with more fluctuations and deviations from the preferred response, especially for ungrammatical sentences.
Repetition effects: While some models show improvements in accuracy with repeated exposure, this is not a consistent pattern across conditions, suggesting that scaling alone does not guarantee convergence towards human-like performance.

The authors argue that these differences between LLMs and humans stem from fundamental differences in the language learning process in silico vs. in vivo. Specifically, LLMs have access to negative evidence (information about ungrammatical constructions) during training, unlike humans, and rely on massive amounts of data rather than the limited input available to children. Additionally, LLMs lack the ability to form hypotheses about language and develop an understanding of linguistic reference, leading to semantic hallucinations. These factors contribute to the persistent gap between LLM and human language abilities, even with significant scaling.

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The study reports the following key statistics:

Bard has 137 billion parameters
ChatGPT-3.5 has 175 billion parameters
ChatGPT-4 has 1.5 trillion parameters
The study collected 1,200 grammaticality judgments in total (400 per LLM)
The best-performing model, ChatGPT-4, achieved 93.5% accuracy for grammatical sentences and 67.1% accuracy for ungrammatical sentences

引用

"Even the largest model tested, ChatGPT-4, does not demonstrate linguistic abilities on par with humans in grammaticality judgment tasks, despite significant scaling in model size."
"The failure of LLMs to consistently tell apart grammatical from ungrammatical language without deviations in the judgments casts doubt on the human-likeness of their linguistic abilities."

从中提取的关键见解

Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans

by Vittoria Den... 在 arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14883.pdf

Language in Vivo vs. in Silico: Size Matters but Larger Language Models Still Do Not Comprehend Language on a Par with Humans

更深入的查询

What other factors, beyond model size, could contribute to bridging the gap between LLM and human language abilities?

In addition to model size, several other factors could play a role in bridging the gap between Large Language Models (LLMs) and human language abilities. One crucial factor is the quality and diversity of the training data. While LLMs are trained on vast amounts of text data, the quality, relevance, and diversity of this data can significantly impact their language understanding capabilities. Ensuring that the training data covers a wide range of linguistic phenomena, contexts, and styles can help LLMs develop a more comprehensive understanding of language.
Another important factor is the architecture and design of the models. Improvements in the architecture, such as incorporating mechanisms for handling long-range dependencies, capturing context more effectively, and enhancing the ability to reason and infer meaning, can contribute to better language understanding. Additionally, advancements in model training techniques, such as curriculum learning, multi-task learning, and transfer learning, can also enhance the performance of LLMs.
Furthermore, incorporating explicit linguistic knowledge and constraints into the training process can help LLMs develop a more linguistically informed understanding of language. By integrating linguistic principles, rules, and constraints into the training objectives, LLMs may be better equipped to handle complex linguistic phenomena and make more accurate language predictions.

How can the differences in language learning between humans and LLMs be further explored and addressed?

To further explore and address the differences in language learning between humans and LLMs, researchers can consider the following approaches:

Fine-grained Linguistic Evaluation: Conducting detailed linguistic analyses and evaluations of LLMs across a wide range of linguistic tasks and phenomena can provide insights into their strengths and limitations. By systematically testing LLMs on various linguistic tasks, researchers can identify specific areas where LLMs struggle and develop targeted interventions to improve their performance.

Incorporating Linguistic Theory: Integrating linguistic theories and principles into the design and training of LLMs can help align their language abilities more closely with human linguistic cognition. By grounding LLMs in linguistic theory, researchers can ensure that these models capture essential aspects of human language processing and understanding.

Comparative Studies: Conducting comparative studies between LLMs and human language processing can shed light on the differences and similarities in their language abilities. By systematically comparing the performance of LLMs and humans on various language tasks, researchers can identify areas where LLMs excel and where they fall short compared to human language understanding.

Interdisciplinary Collaboration: Collaborating across disciplines such as linguistics, cognitive science, and artificial intelligence can provide a holistic perspective on the differences in language learning between humans and LLMs. By bringing together experts from different fields, researchers can leverage diverse insights and methodologies to address the challenges in bridging the gap between LLMs and human language abilities.

What are the implications of the persistent gap in language understanding for the use of LLMs as cognitive theories of language?

The persistent gap in language understanding between LLMs and humans has significant implications for the use of LLMs as cognitive theories of language. Some of the key implications include:

Limitations in Generalization: The inability of LLMs to consistently exhibit human-like language understanding raises questions about their capacity to serve as accurate cognitive theories of language. If LLMs cannot capture the full range of linguistic phenomena and nuances present in human language, their utility as comprehensive cognitive models may be limited.

Validity of Linguistic Theories: The discrepancies between LLMs and human language abilities highlight the challenges in using LLMs to validate or refine linguistic theories. If LLMs struggle to exhibit linguistic behaviors that align with established linguistic principles, their effectiveness in informing linguistic theory may be compromised.

Interpretation of Results: Researchers and practitioners using LLMs as cognitive theories of language must interpret the model outputs with caution, considering the inherent differences in language understanding between LLMs and humans. Understanding the limitations of LLMs in capturing human-like language abilities is essential for drawing accurate conclusions from model predictions.

Future Research Directions: The persistent gap in language understanding underscores the need for continued research and development in the field of natural language processing. Addressing the challenges in bridging the differences between LLMs and human language abilities can drive advancements in model design, training methodologies, and linguistic theory, paving the way for more accurate and reliable cognitive models of language.