Podcasts > Lex Fridman Podcast > #426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

By Lex Fridman

In this episode of the Lex Fridman Podcast, Edward Gibson delves into the intricacies of language and grammar. He explores dependency grammar and its role in understanding the cognitive costs of language production and comprehension. Gibson explains how languages naturally have short dependencies to minimize these cognitive burdens.

The discussion also examines cultural differences between languages, highlighting how certain indigenous societies lack precise terms for quantities and colors – reflecting their unique perceptions of the world. Gibson contrasts this with the complexities of legalese, advocating for simpler legal writing. The episode further explores the capabilities and limitations of large language models in replicating the full nuances of human language and meaning.

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

1-Page Summary

Dependency grammar

Edward Gibson presents dependency grammar as a framework that reveals the connections between words within sentences, forming tree structures to represent these relationships. He appreciates this approach for its ability to illustrate cognitive processing costs, as longer dependencies within sentences increase the difficulty of language production and comprehension. Gibson supports his viewpoint with experimental evidence, highlighting that dependency lengths are critical factors in gauging the cognitive load of language usage.

Why languages have short dependencies

Gibson proposes that languages inherently have short dependencies to lower the cognitive burdens associated with sentence production and comprehension. He notes that while languages could be structured to have even shorter dependencies, a balance is maintained to keep the language learnable and rules consistent. Short dependencies result in simpler sentences, like those in Hemingway's writing, which are easier to produce and understand. The tendency towards short dependencies is seen as a linguistically universal feature to minimize cognitive costs.

Cultural Differences Between Languages

Observing the differences in how cultures use language, particularly in terms of color and number terminology, Gibson examines the disparities between industrialized and non-industrialized societies. He discusses the Pirahã and Tsimane languages of the Amazon, which lack specific number words, relying instead on terms like few and many to represent quantities. Gibson posits that the absence of precise counting words can affect the abilities of a society and reflects the practical needs that influence language development. The difficulty in translating concepts from languages with these gaps reveals deep cultural variations in how different communities perceive and articulate the world.

Legalese as an exception

Gibson critiques the complexity of legal language, pointing out that legalese is fraught with center embedding, which disrupts comprehension and recall—even among lawyers. He discovers that a significant proportion of sentences in legal texts involve definitions nested within subject-verb constructs, contributing to the difficulty in understanding. This stands in stark contrast to natural language tendencies, highlighting how legal documents, perhaps unintentionally, prioritize a certain performative complexity. Gibson advocates for simpler legal writing, arguing that it is feasible and preferable.

Large language models

The conversation with Lex Fridman reveals Gibson's skepticism about Large Language Models (LLMs) being comprehensive theories of language due to their significant size and lack of conciseness. While acknowledging their syntactical capabilities, he contends that LLMs do not truly grasp meaning. He references shortcomings from earlier AI research and emphasizes that understanding meaning remains a vital challenge for LLMs. Fridman and Gibson express that the complexity of human language—as evidenced by Noam Chomsky's observations—is not yet fully replicated by LLMs that primarily focus on the form of language rather than its semantic substance.

1-Page Summary

Additional Materials

Clarifications

  • Dependency grammar is a linguistic approach that focuses on the relationships between words in sentences. It represents these relationships using tree structures, where each word is connected to another based on their grammatical dependencies. This method helps illustrate how words in a sentence rely on each other for meaning and function, providing a visual representation of the syntactic structure of language. By mapping out these dependencies, dependency grammar aids in understanding how words work together to convey meaning in a sentence.
  • Cognitive processing costs related to longer dependencies in sentences pertain to the increased mental effort required to process and understand sentences with complex relationships between words. As the distance between dependent elements grows, the cognitive load on the brain intensifies, impacting language production and comprehension. This phenomenon is supported by experimental evidence showing that longer dependencies in sentences lead to higher processing difficulties. Understanding these costs sheds light on how the structure of language influences the cognitive effort needed for effective communication.
  • Short dependencies in languages are linguistic structures where words in a sentence are closely connected, reducing the cognitive effort needed for comprehension and production. By keeping relationships between words concise, languages aim to make sentences easier to process and understand. This design choice helps speakers avoid mental strain when constructing or interpreting sentences. Overall, the use of short dependencies is a universal feature in languages to streamline communication and minimize cognitive load.
  • Balancing language structures for learnability and consistency involves designing languages with dependencies that are neither too long nor too short to optimize ease of learning while maintaining rule predictability. This balance ensures that sentences are structured in a way that is intuitive for speakers to produce and comprehend, striking a harmony between complexity and simplicity in linguistic expression. By keeping dependencies manageable, languages become more accessible for users to grasp and use effectively, fostering efficient communication and cognitive processing. This approach aims to create a system that is both structured enough to follow patterns but flexible enough to accommodate various expressions and meanings.
  • Short dependencies in language lead to simpler sentence structures because they reduce the cognitive load on speakers and listeners. When dependencies between words are short, sentences are easier to produce and understand. This simplicity is akin to the straightforward style found in Hemingway's writing. By minimizing the distance between related words, languages make communication more efficient and less mentally taxing.
  • In languages without specific number words, like the Pirahã and Tsimane languages, speakers use general terms like "few" and "many" instead. This lack of precise counting words can influence how these societies perceive and communicate quantities. It reflects a different approach to quantification and may impact cognitive processes related to numerical concepts. The absence of distinct number words in these languages sheds light on how language shapes cognitive abilities and reflects cultural priorities.
  • Legal language, particularly legalese, is known for its complexity due to the use of intricate sentence structures and specialized terminology. Center embedding in legal texts involves nesting definitions within subject-verb constructs, making sentences convoluted and challenging to understand. This complexity can hinder comprehension and recall, even among legal professionals. Simplifying legal writing is advocated to enhance clarity and accessibility in legal documents.
  • Large Language Models (LLMs) are advanced AI systems designed to process and generate human language. The skepticism towards LLMs as comprehensive theories of language stems from concerns about their ability to truly understand the meaning behind language, despite their syntactical prowess. Critics argue that LLMs, while proficient in language structure, often lack a deep semantic understanding that is crucial for true comprehension. This skepticism highlights the ongoing challenge in AI research to bridge the gap between language processing based on form and language understanding based on meaning.
  • Large Language Models (LLMs) primarily emphasize syntactical patterns and structures in language, focusing on how words and phrases are arranged within sentences. This emphasis on syntax means that LLMs excel at generating text that follows grammatical rules but may struggle to deeply understand the nuanced meanings and contexts of words and sentences. While LLMs can mimic human language patterns effectively, their comprehension of semantics, subtleties, and deeper layers of meaning remains a significant challenge in natural language processing. This distinction between syntax and meaning underscores the ongoing efforts to enhance LLMs' capabilities to grasp language understanding beyond surface-level structures.

Counterarguments

  • Dependency grammar, while useful, is not the only framework for understanding language structure; other models like phrase structure grammar also offer valuable insights and may be preferred in certain linguistic analyses.
  • The cognitive processing costs associated with longer dependencies might be mitigated by other factors such as working memory capacity, context, and familiarity with the language, which can vary among individuals.
  • Some languages or dialects may exhibit longer dependencies due to their syntactic structures, and speakers of these languages may not necessarily experience higher cognitive burdens.
  • The balance between short dependencies for cognitive ease and language learnability might not be as clear-cut; some languages may have evolved to prioritize other features such as expressiveness or stylistic richness.
  • Simpler sentences with short dependencies are not universally easier to understand; complexity can sometimes clarify meaning or convey nuance that simpler sentences cannot.
  • The claim that short dependencies are a linguistically universal feature may overlook the diversity of linguistic structures across the world's languages.
  • Cultural differences in language, such as the absence of precise counting words, may not always negatively impact societal abilities; they may instead represent different cognitive strategies or value systems.
  • Legalese and its complexity may serve specific functions such as precision and covering legal contingencies, which might be lost with oversimplification.
  • The argument for simplifying legal writing assumes that all legal language complexity is unnecessary, which may not account for the intricacies and requirements of legal discourse.
  • Large Language Models (LLMs) may have potential in understanding meaning that has not yet been fully realized or demonstrated, and ongoing research could address current limitations.
  • The focus of LLMs on the form of language rather than semantic substance is a current limitation, but it does not preclude the possibility of future models achieving a better balance between form and meaning.
  • The criticism of LLMs' size and lack of conciseness may not take into account the complexity of human language and the computational power needed to model it effectively.

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

Dependency grammar

Edward Gibson sheds light on dependency grammar, emphasizing its effectiveness in illustrating the cognitive processing involved in language comprehension and production.

Analysis of sentences as dependency trees representing connections between words

Edward Gibson explains dependency grammar as a system of connections between words that make up sentences. Fridman clarifies the central concept, stating that in dependency grammar, each word is linked to just one other word, forming a tree structure. This structure elucidates the relationships and distances between words.

Allows measurement of cognitive processing cost based on dependency lengths

Gibson is fond of dependency grammar because it transparently shows the lengths of dependencies between words. These lengths are key indicators of cognitive processing costs—the longer the dependency, the harder it is to produce and understand the sentence. He further explains that trees can represent the cognitive processing cos ...

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Dependency grammar

Additional Materials

Clarifications

  • Dependency grammar is a linguistic approach that focuses on the relationships between words in a sentence. It represents these relationships as directed links between words, showing how they depend on each other for meaning. This method helps to analyze the structure of sentences by highlighting the connections and dependencies between individual words. In essence, it simplifies the complex relationships within a sentence by illustrating how each word relates to others in a hierarchical manner.
  • In dependency grammar, each word in a sentence is connected to exactly one other word, known as its head. This connection represents the grammatical relationship between the words. This one-to-one relationship simplifies the structure and analysis of sentences. It helps in understanding how words relate to each other within a sentence.
  • In dependency grammar, tree structures represent how words in a sentence are connected to each other. Each word is linked to another word, forming a tree that shows the relationships and distances between words. This visual representation helps illustrate the flow of information and dependencies within the sentence. The structure of these trees can reveal the complexity of cognitive processing involved in understanding and producing language.
  • Cognitive processing cost based on dependency lengths in language relates to the mental effort required to understand and produce sentences based on the relationships between words. Longer dependencies between words in a sentence can increase the cognitive load, making it more challenging for individuals to process and comprehend the information. By measuring the distances and connections between words in a sentence, researchers can assess the cognitive processing cost associated with different linguistic structures. This concept helps in understanding how the complexity of sentence structures impacts the cognitive effort needed for language comprehension and production.
  • Experiments assessing sentence goodness involve evaluating how well-formed or coherent a sentence is perceived to be. Reading times experiments measure the time it takes individuals to read and comprehend sentences. Brain activation studies use neuroimaging techniques to observe which areas of the brain are active during language processing tasks. These studies provide insights into the cognitive processes involved in language comprehension and production.
  • The link between dependency lengths and cognitive load in language processing is based on the idea that longer dependencies between words in a sentence can increase the mental effort required to understand and produce that ...

Counterarguments

  • Dependency grammar may oversimplify the complexity of language by focusing solely on binary relationships between words, ignoring other linguistic levels like phonology, morphology, and discourse.
  • The assumption that each word is linked to only one other word may not account for all linguistic phenomena, such as free word order in some languages or the presence of multi-headed constructions.
  • The tree structure may not capture all nuances of word relationships, such as semantic roles and pragmatic context, which can also influence cognitive processing.
  • The measurement of cognitive processing cost based on dependency lengths may not be universally applicable, as different languages and cultures might process dependencies differently.
  • The focus on dependency lengths might overlook other factors that contribute to cognitive load, such as lexical difficulty, syntactic ambiguity, or working memory capacity.
  • The claim that longer dependencies and nested structures increase production and comprehension difficulties exponentially may not hold true for all individuals, as there can be significant va ...

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

Why languages have short dependencies

Edward Gibson explains that languages are structured to have short dependencies between words to minimize the difficulty in sentence production and comprehension.

Minimizes production difficulty and comprehension confusion

Lex Fridman and Edward Gibson discuss the theory that most languages favor short dependencies to reduce cognitive processing costs. Gibson underscores that languages optimize for shorter dependency lengths compared to a control set constructed from random scramblings of sentence elements. Fridman and Gibson explore the impact of these short dependencies on the simplicity of sentence structure and ease of understanding.

Long dependencies explained by dependency grammar

Gibson discusses that while languages could potentially minimize dependency lengths even further, they also maintain regular rules to facilitate learnability. He notes that although this is somewhat shakier territory, the concept of dependency grammar can explain the presence of short dependencies, as languages aim for harmonic word order to minimize production difficulty and potential comprehension confusion.

Gibson empha ...

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Why languages have short dependencies

Additional Materials

Clarifications

  • Dependency lengths in the context of language structure refer to the distance between words in a sentence that are linked in meaning. Short dependencies indicate that these related words are closer together in the sentence, making it easier for speakers to produce and comprehend the sentence. Longer dependencies mean that the related words are farther apart, which can increase the cognitive effort required to connect them in meaning during language processing. The concept of dependency lengths helps explain how languages are structured to optimize communication efficiency by minimizing the cognitive load on speakers and listeners.
  • Cognitive processing costs related to dependency lengths in language refer to the mental effort required to link words that are separated by varying distances in a sentence. Short dependencies involve words closely related in meaning, reducing the cognitive load for speakers and listeners. Longer dependencies, where related words are farther apart, can increase the mental effort needed to process the relationship between these words, impacting both production and comprehension of language. This concept highlights how the arrangement of words in a sentence can influence the ease or difficulty of understanding and producing language.
  • Dependency grammar is a linguistic approach that focuses on the relationships between words in a sentence. It represents these relationships as directed links between words, showing how they depend on each other for meaning. In the context of short dependencies in languages, dependency grammar helps explain how languages structure sentences to minimize cognitive processing costs by keeping related words closer together. By analyzing these dependencies, linguists can better understand how language is organized and how it influences sentence production and comprehension.
  • Harmonic word order in relation to dependency lengths means that languages tend to organize words in a way that minimizes the distance between words that are closely related in meaning. This organization helps reduce cognitive effort in processing sentences by making it ...

Counterarguments

  • While short dependencies may generally reduce cognitive load, some languages and linguistic structures naturally employ longer dependencies without necessarily increasing difficulty for native speakers.
  • Cognitive processing costs can be subjective and vary among individuals; what is complex for one person may be simple for another, depending on their familiarity and proficiency with the language.
  • The optimization for shorter dependency lengths might not account for stylistic and rhetorical choices in language that intentionally use longer dependencies for effect, such as in poetry or legal language.
  • The theory that languages inherently favor short dependencies may not fully consider the role of cultural and historical influences on language development, which can lead to longer dependencies.
  • Regular rules to facilitate learnability might sometimes result in longer dependencies, as seen in highly inflected languages where agreement between distant words is required.
  • Dependency grammar is one of many theories that attempt to explain linguistic structures, and there may be alternative grammatical frameworks that offer different explanations for the presence of short or long dependencies.
  • The assertion that words linked in meaning being further apart always result in higher cognitive costs could be challenged by the fact that context an ...

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

Cultural Differences Between Languages

Edward Gibson’s observations concentrate on the variances in color and number terminology among different cultures, revealing intriguing contrasts between industrialized societies and remote, non-industrialized communities.

Counting Differences Between Industrialized and Remote Cultures

While the content does not specifically discuss counting differences between industrialized and remote cultures, Edward Gibson explores the concept of number representation in language and its variability among cultures.

Number Words Missing from Some Amazonian Languages

Gibson shares insights into the languages of isolated communities, such as the Tsimane and Pirahã of the Amazon, which lack words for exact counting. The Pirahã language, for example, does not include words for 'one,' 'two,' 'three,' or 'four,' and instead uses quantifiers like few, some, and many. These quantifier words do not represent specific numbers; rather, they are used contextually to indicate approximate quantities.

Although people from these cultures can perform exact matching tasks with a small number of objects by sight, their ability to do so diminishes with larger quantities due to the lack of specific number words. For example, they can match quantities accurately up to about three or four, but can only estimate when dealing with larger numbers such as eight. This indicates that without number words to count with, precision in tasks is compromised.

Cultural Implications of Language

Gibson suggests that the absence of words for exact counts can limit a society's capabilities. He hypothesizes that the invention of a counting system within a culture may emerge from practical needs, such as farming, where keeping track of a number of animals necessitates a counting system ...

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Cultural Differences Between Languages

Additional Materials

Clarifications

  • Number representation in language varies among cultures, impacting how numbers are expressed and understood. Different languages may have unique ways of counting and representing quantities, such as using specific words for numbers or relying on general quantifiers. This variability can influence cognitive processes related to numerical tasks and highlight cultural differences in how societies perceive and communicate numerical information.
  • In some Amazonian languages like Pirahã, specific words for exact counting, such as 'one,' 'two,' 'three,' or 'four,' are absent. Instead, these languages use quantifiers like 'few,' 'some,' and 'many' to describe quantities. This linguistic feature reflects a different approach to numerical representation compared to languages that have distinct words for precise numbers. The absence of specific count words in these languages can impact tasks requiring precise numerical distinctions.
  • In some languages like Pirahã, specific number words like 'one,' 'two,' 'three,' or 'four' are absent. Instead, quantifier words like 'few,' 'some,' and 'many' are used to indicate approximate quantities. This linguistic feature reflects a cultural emphasis on general quantity perception over precise numerical values. This practice can impact tasks requiring exact counting, as individuals rely on contextual estimations rather than specific numbers.
  • In cultures without specific number words, individuals may struggle with precision in tasks requiring exact counting due to the absence of linguistic tools for precise numerical representation. This limitation becomes evident when dealing with larger quantities, as the lack of distinct number words hinders their ability to accurately count beyond a certain point. The reliance on quantifiers like 'few' and 'many' instead of precise numbers can lead to challenges in tasks that demand exact numerical values. This linguistic constraint can impact various aspects of daily life, highlighting the influence of language on cognitive processes related to numerical concepts.
  • The hypothesis that the invention of a counting system within a culture may emerge from practical needs suggests that the development of numerical concepts and counting methods could be driven by the specific requirements of a society. For instance, in contexts like agriculture or trade, where precise quantification is essential, the need for a structured counting system becomes apparent. This hypothesis implies that the evolution of counting systems is closely linked to the practical demands and activities of a community, shaping how numbers are conceptualized and expressed in language.
  • Translating concepts between languages can be challenging due to differences in how each language expresses ideas, leading to conceptual gaps that make direct translation difficult. These gaps can arise from unique cultural perspectives, historical influences, or linguistic structures that do not have direct equivalents in another language. As a result, certain concepts may not translate accurately, requiring a deeper understanding of the cultural context to convey the intended meaning effectively. This challenge highlights the complex ...

Counterarguments

  • The idea that the absence of exact number words may limit a society's capabilities could be challenged by arguing that different cognitive tools may develop in the absence of such words, leading to alternative ways of understanding and interacting with the world.
  • The assertion that a counting system emerges from practical needs like farming might be countered by pointing out that counting systems can also arise from other social and cultural practices, not solely from economic or practical activities.
  • The notion that precision in tasks diminishes without specific number words could be met with the argument that precision is not always necessary or valued in all cultures, and that other forms of categorization and estimation may be equally effective in certain contexts.
  • The difficulty in translating concepts between languages due to conceptual gaps might be nuanced by considering the role of cultural exchange and bilingualism in bridging understanding, suggesting that translation challenges can be overcome with time and exposure.
  • The view that industrialized and remote cultures perceive counting and numbers differently could be expanded to acknowledge that there is significant variation within these broad categories, and that not all industrialized or remote cultures w ...

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

Legalese as an exception

Edward Gibson delves into the intricate nature of legal language found in contracts and laws, often referred to as "legalese," and its impact on comprehension.

Center embedding common and hinders understanding

Definitions placed inside subject-verb relations

Edward Gibson discusses why legalese is notoriously difficult to understand compared to other types of professional texts, including academic texts. Gibson, along with Eric Martinez, evaluated various contracts and found that center embedding, or the placement of nested structures within a sentence, is rampant in legalese and contributes significantly to its complexity. Not only does this practice hinder comprehension, but it also negatively impacts recall.

Lawyers, who regularly deal with legalese, experience poor recall and understanding when reading sentences with center-embedded clauses. Interestingly, when presented with non-center-embedded versions of texts, both legal professionals and laypeople show a preference, suggesting that simplifying the structure could benefit all readers.

Gibson remarks on the high incidence of center embedding in legal texts, where clauses intervene between subjects and verbs, a practice much more common than in other texts. Approximately 70% of sentences in contracts and laws feature a center-embedded clause, which is significantly higher than the 20% rate found in other types of writings. Such a high prevalence of center embedding makes legal language uniquely challenging.

One specific issue that Gibson criticizes is the insertion of definitions within the sentence, disrupting the syntactic flow between subject and verb. He acknowledges that simplifying the legal material to avoid center embedding is quite feasible while still conveying the same information. By extracting definitions from within the sentence, legal texts could become more understandabl ...

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Legalese as an exception

Additional Materials

Clarifications

  • Center embedding in sentences involves nesting phrases within other phrases of the same type, creating complex structures that can hinder comprehension. This linguistic phenomenon can lead to difficulties in parsing sentences due to limitations in human short-term memory. The more layers of center embedding in a sentence, the more challenging it becomes for individuals to process and understand the information presented. Researchers have studied the grammaticality and acceptability of sentences with multiple center embeddings, highlighting the complexities involved in language processing.
  • In legal language, inserting definitions within sentences disrupts the natural flow of syntax by interrupting the connection between the subject and the verb. This practice makes it challenging for readers to follow the structure of the sentence smoothly. By extracting definitions from within the sentence, the syntax can be simplified, allowing for clearer comprehension of the legal text. This disruption in syntax caused by definitions placed within sentences is a common feature in legalese that contributes to its complexity.
  • Long dependencies in legal documents refer to situations where a sentence contains complex structures that require the reader to hold multiple pieces of information in mind to understand the sentence fully. These dependencies can involve clauses, definitions, or other elements that are separated from each other within the sentence, making it challenging to connect the related parts. In legal writing, long dependencies can create confusion and hinder comprehension, especially when these dependencies are not clearly organized or when they interrupt the natural flow of the sentence. Simplifying the structure by reducing these long ...

Counterarguments

  • Legalese may be complex for a reason; it often needs to be precise and unambiguous to cover all potential legal contingencies, which can require complex structures and specific terminology.
  • Center embedding might not be the sole or primary reason for poor comprehension; other factors such as unfamiliarity with legal concepts or the inherent complexity of the subject matter could also contribute.
  • Lawyers and legal professionals are trained to understand complex legal language, and simplifying it could potentially lead to misinterpretations or loopholes in legal documents.
  • The preference for non-center-embedded texts in studies might not translate to better legal outcomes or clearer legal relationships when applied to actual contracts and laws.
  • The high incidence of center embedding in legal texts could be a reflection of the need to incorporate multiple legal concepts and stipulations within a single sentence for legal efficacy.
  • Definitions within sentences can sometimes provide immediate clarity and context, reducing the need to cross-reference other parts of a document.
  • The assertion that legal language could be simplified without losing meaning might not account for the nuances and precision required in legal drafting.
  • The idea that legal language is in ...

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#426 – Edward Gibson: Human Language, Psycholinguistics, Syntax, Grammar & LLMs

Large language models

Edward Gibson and Lex Fridman discuss the capabilities and limitations of Large Language Models (LLMs) in replicating human language and understanding.

Best current theories for modeling linguistic form

During the conversation, Fridman and Gibson consider the proficiency of LLMs at handling the form of language and discuss how well these models imitate the structure and syntax of human language.

Don't capture meaning or understand language

Despite their ability to predict what's good and bad in the English language, Gibson notes that LLMs might not be great theories due to their size. He implies a preference for more concise theories. Moreover, both Fridman and Gibson touch upon the idea that LLMs might use formalisms like dependency grammar to model language form; however, it's not clear to what extent they capture meaning or understand language.

Gibson shares that AI in the field of natural language during the '80s did not impress him, as it seemed more like a set of hacks rather than a real theory. He notes that syntax is a comparatively easier challenge than meaning, which LLMs still struggle to grasp. Gibson emphasizes that while LLMs handle form well, they fail to understand meaning, a sentiment echoed by Fridman as they discuss language models' limitations.

One sign of these limitations is that large language models can be tricked because they do not understand what is happening in a given interaction. Using the Monty Hall problem to illustrate, Gibson explains that LLMs can’t remember or integrate specific knowledge, defaulting to the most fa ...

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Large language models

Additional Materials

Clarifications

  • Large Language Models (LLMs) are advanced artificial neural networks known for their ability to generate human-like text and perform various natural language processing tasks. They learn patterns and relationships from vast amounts of text data during training, enabling them to predict and generate coherent language. Notable examples include OpenAI's GPT series and Google's PaLM, which have gained attention for their language generation capabilities. LLMs have been developed using transformer-based architectures and are continuously evolving to improve their language understanding and generation capabilities.
  • Dependency grammar is a linguistic framework that focuses on how words in a sentence are connected to each other through directed links, with the verb typically seen as the central element. It emphasizes the relationships between words based on dependencies rather than traditional phrase structures. This approach is useful for analyzing languages with flexible word order and has roots in the work of linguist Lucien Tesnière. Dependency grammar has a long history, with early forms of dependency-based analysis found in ancient grammatical traditions like Pāṇini's work.
  • The Monty Hall problem is a probability puzzle where a contestant is asked to choose between three doors, one hiding a prize and the others hiding goats. After the contestant makes a choice, the host, who knows what's behind each door, opens another door revealing a goat. The contestant is then given the option to stick with their original choice or switch to the remaining unopened door. The optimal strategy is to switch doors, as it increases the contesta ...

Counterarguments

  • LLMs are designed to handle large-scale data and complexity, which can be an advantage in capturing the nuances of language.
  • The size of LLMs may be necessary to process the vast amount of linguistic data required for accurate language modeling.
  • Dependency grammar and other formalisms are tools that can help LLMs approximate the structure of language, and ongoing research may improve their ability to capture meaning.
  • AI in the '80s laid the groundwork for current advancements, and what seemed like hacks may have been necessary steps in the evolution of the field.
  • Understanding meaning is a complex task, and LLMs have shown progress in areas such as context-awareness and disambiguation, suggesting a growing capability in semantic understanding.
  • The ability of LLMs to be tricked is not unique to machines; humans can also be misled or confused, especially in unfamiliar situations.
  • LLMs' reliance on training data reflects a learning process, and with more diverse and comprehensive data, their responses can become more accurate and less predictable.
  • While LLMs may not fully understand interactions, they can still provide useful assistance and perform tasks effectively with ...

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