Artificial Language Models Teach Us Nothing About Language

A wave of neuroscientific research has attempted to exploit the sophisticated statistical power of large language models (LLMs) to explore how the human brain responds to language. Yet, one issue that some linguists have felt has not been well addressed is how this kind of research exposes new facts about language and may, in fact, pose an obstacle to genuine scientific insights.

As the accuracy of natural language processing (NLP) models increased over the 2010s, there was a clear sacrifice in interpretability. Effectively, the more powerful and accurate language models have become, the less cognitively plausible they seem to be. Even though bigger language models "learn" aspects of human language, like syntactic relations, at more accurate rates than smaller models, at the same time, the need for these bigger models to learn syntax decreases for most tasks that we actually need them for.

Other concerns have arisen. Humans parse sentences hierarchically, yet LLMs appear to have strong linear biases. Some discuss this issue using a tone that places less emphasis on the importance of linguistic creativity and generation, the hallmark of human language. Idan Blank noted recently that "language processing is arguably more than just prediction"—much like visual attention is "arguably" more than just photoreception.

Models of Language

Brushing over a rich and controversial history, an important theme in recent theoretical linguistics concerns how many properties of linguistic theory that were initially carried over from formal systems and mathematical models from the 1950s-70s are not appropriate for characterizing human psychology. A variety of these themes carry important implications for how we make appropriate use of LLMs.

There are certain linguistic theories that tend to be tied more intimately to research using LLMs due to their interest in the explanatory power of domain-general reasoning. For example, the framework of construction grammar is built on the assumption that humans memorize a large number of multi-unit constructions and then manipulate these memorized objects. Yet, with memorized constructions, we still need some kind of generative system to modify these or to form them in the first place.

Frameworks such as construction grammar confuse the artifacts of the language system (the outputs of language, like constructions) with the object of investigation itself. "Constructions" are a result of language; they do not constitute it. They are not a plausible object of psycholinguistic investigation: There are far too many independent factors that conspire into every given construction.

These objections are important for our understanding of artificial models. We cannot take a large number of constructions (i.E., linearized outputs of the underlying generative computational procedure of syntax) and expect to explain human language. We will assuredly get statistically significant approximations for parsing data and even neural responses by focusing on constructions and their distributional statistics, but these issues are much too coarse-grained to be objects of linguistic theory.

Neurobiology

Last month, a paper from the lab of Evelina Fedorenko at MIT published in Neurobiology of Language argued that "lexical semantic content, not syntactic structure, is the main contributor to ANN-brain similarity of fMRI responses in the language network." Yet, simply because ANNs align with fMRI blood oxygenation level-dependent (BOLD) responses best via lexico-semantics, it does not follow that syntactic information is not neurally represented.

The effects documented in the Fedorenko Lab paper are heavily driven by content words, which carry clear conceptual semantic content, whereas we know from behavioral research that function words carry very few processing costs. However, we also know that functional grammatical structure is essential in delivering syntactic information, and some linguists have even gone as far as to argue that cross-linguistic diversity emerges exclusively from functional grammatical information (as opposed to content words like nouns and verbs).

Fedorenko and colleagues conclude with this: "The critical result—that lexical-semantic content is the main contributor to the similarity between ANN representations and neural ones—aligns with the idea that the goal of the human language system is to extract meaning from linguistic strings." What goes unnoticed is that "lexico-semantic content" also delivers modifications to syntactic information.

One would be hard-pressed to find anybody who would disagree with the idea that humans use language to extract meaning. This is not a scientific discovery. To my knowledge, there are no predictions from within theoretical linguistics concerning which scale of neural activity or complexity syntax is encoded. If it is not to be found in the BOLD signal, then too much the worse for fMRI.

Another Fedorenko Lab paper from August used GPT-based encoding models and fMRI data to accurately predict neural responses to language. The authors conclude: "A systematic analysis of the model-selected sentences reveals that surprisal and well-formedness of linguistic input are key determinants of response strength in the language network."

There are no reasons from within linguistic theory to doubt the processing importance of surprisal. Fedorenko and colleagues test if their language model predicts responses to sentences that are expected to trigger minimal language network activity ("We were sitting on the couch") versus sentences that are meant to trigger maximal activity ("People on Insta be like 'Gross'"; "Jiffy Lube of therapies"; "Notice how you reacted to WTF"). These were collated often from social media usage.

Yet, notions like "lexical access" and "semantic integration" (that are dismissed by Fedorenko and colleagues as being outdated) crucially form part of theories. "Surprisal" measures are not theories. What is even more surprising is that Fedorenko and colleagues end up showing that measures of semantic plausibility and grammaticality both explain variance beyond surprisal. Yet, they report these results without offering some kind of theoretical account for this.

Thus, while the authors begin their paper by claiming that models of the brain built around linguistic theory are problematic and old-fashioned, they ultimately end up supporting these traditional concepts.

Like many others, I remain unconvinced that the best way to build a science of language processing in the brain is to use fMRI and expose participants to sentences like "People on Insta be like 'Gross'" and then measure how surprised the language network is and see if this aligns with an encoding model.

Celebration of these results has been widespread both on social media and in some more established science publications. Neuroscience driven by novel exploitations of statistical patterns in neural and language data is part of the reason why The Guardian asked in 2022, "Are we witnessing the dawn of post-theory science?"

If we are, there is little to celebrate.

What Is NLP? Natural Language Processing Explained

Natural language processing is a branch of AI that enables computers to understand, process, and generate language just as people do — and its use in business is rapidly growing.

Natural language processing definition

Natural language processing (NLP) is the branch of artificial intelligence (AI) that deals with training computers to understand, process, and generate language. Search engines, machine translation services, and voice assistants are all

While the term originally referred to a system's ability to read, it's since become a colloquialism for all computational linguistics. Subcategories include natural language generation (NLG) — a computer's ability to create communication of its own — and natural language understanding (NLU) — the ability to understand slang, mispronunciations, misspellings, and other variants in language.

The introduction of transformer models in the 2017 paper "Attention Is All You Need" by Google researchers revolutionized NLP, leading to the creation of generative AI models such as Bidirectional Encoder Representations from Transformer (BERT) and subsequent DistilBERT — a smaller, faster, and more efficient BERT — Generative Pre-trained Transformer (GPT), and Google Bard.

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How natural language processing works

NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Phrases, sentences, and sometimes entire books are fed into ML engines where they're processed using grammatical rules, people's real-life linguistic habits, and the like. An NLP algorithm uses this data to find patterns and extrapolate what comes next. For example, a translation algorithm that recognizes that, in French, "I'm going to the park" is "Je vais au parc" will learn to predict that "I'm going to the store" also begins with "Je vais au." All the algorithm then needs is the word for "store" to complete the translation task.

NLP applications

Machine translation is a powerful NLP application, but search is the most used. Every time you look something up in Google or Bing, you're helping to train the system. When you click on a search result, the system interprets it as confirmation that the results it has found are correct and uses this information to improve search results in the future.

Chatbots work the same way. They integrate with Slack, Microsoft Messenger, and other chat programs where they read the language you use, then turn on when you type in a trigger phrase. Voice assistants such as Siri and Alexa also kick into gear when they hear phrases like "Hey, Alexa." That's why critics say these programs are always listening; if they weren't, they'd never know when you need them. Unless you turn an app on manually, NLP programs must operate in the background, waiting for that phrase.

Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.

Rajeswaran V, senior director at Capgemini, notes that Open AI's GPT-3 model has mastered language without using any labeled data. By relying on morphology — the study of words, how they are formed, and their relationship to other words in the same language — GPT-3 can perform language translation much better than existing state-of-the-art models, he says.

NLP systems that rely on transformer models are especially strong at NLG.

Natural language processing examples

Data comes in many forms, but the largest untapped pool of data consists of text — and unstructured text in particular. Patents, product specifications, academic publications, market research, news, not to mention social media feeds, all have text as a primary component and the volume of text is constantly growing. Apply the technology to voice and the pool gets even larger. Here are three examples of how organizations are putting the technology to work:

Edmunds drives traffic with GPT: The online resource for automotive inventory and information has created a ChatGPT plugin that exposes its unstructured data — vehicle reviews, ratings, editorials — to the generative AI. The plugin enables ChatGPT to answer user questions about vehicles with its specialized content, driving traffic to its website.

Eli Lilly overcomes translation bottleneck: With global teams working in a variety of languages, the pharmaceutical firm developed Lilly Translate, a home-grown NLP solution, to help translate everything from internal training materials and formal, technical communications to regulatory agencies. Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems.

Accenture uses NLP to analyze contracts: The company's Accenture Legal Intelligent Contract Exploration (ALICE) tool helps the global services firm's legal organization of 2,800 professionals perform text searches across its million-plus contracts, including searches for contract clauses. ALICE uses "word embedding" to go through contract documents paragraph by paragraph, looking for keywords to determine whether the paragraph relates to a particular contract clause type.

Natural language processing software

Whether you're building a chatbot, voice assistant, predictive text application, or other application with NLP at its core, you'll need tools to help you do it. According to Technology Evaluation Centers, the most popular software includes:

Natural Language Toolkit (NLTK), an open-source framework for building Python programs to work with human language data. It was developed in the Department of Computer and Information Science at the University of Pennsylvania and provides interfaces to more than 50 corpora and lexical resources, a suite of text processing libraries, wrappers for natural language processing libraries, and a discussion forum. NLTK is offered under the Apache 2.0 license.

Mallet, an open-source, Java-based package for statistical NLP, document classification, clustering, topic modeling, information extraction, and other ML applications to text. It was primarily developed at the University of Massachusetts Amherst.

SpaCy, an open-source library for advanced natural language processing explicitly designed for production use rather than research. Licensed by MIT, SpaCy was made with high-level data science in mind and allows deep data mining.

Amazon Comprehend. This Amazon service doesn't require ML experience. It's intended to help organizations find insights from email, customer reviews, social media, support tickets, and other text. It uses sentiment analysis, part-of-speech extraction, and tokenization to parse the intention behind the words.

Google Cloud Translation. This API uses NLP to examine a source text to determine language and then use neural machine translation to dynamically translate the text into another language. The API allows users to integrate the functionality into their own programs.

Natural language processing courses

There's a wide variety of resources available for learning to create and maintain NLP applications, many of which are free. They include:

NLP – Natural Language Processing with Python from Udemy. This course provides an introduction to natural language processing in Python, building to advanced topics such as sentiment analysis and the creation of chatbots. It consists of 11.5 hours of on-demand video, two articles, and three downloadable resources. The course costs $94.99, which includes a certificate of completion.

Data Science: Natural Language Processing in Python from Udemy. Aimed at NLP beginners who are conversant with Python, this course involves building a number of NLP applications and models, including a cipher decryption algorithm, spam detector, sentiment analysis model, and article spinner. The course consists of 12 hours of on-demand video and costs $99.99, which includes a certificate of completion.

Natural Language Processing Specialization from Coursera. This intermediate-level set of four courses is intended to prepare students to design NLP applications such as sentiment analysis, translation, text summarization, and chatbots. It includes a career certificate.

Hands On Natural Language Processing (NLP) using Python from Udemy. This course is for individuals with basic programming experience in any language, an understanding of object-oriented programming concepts, knowledge of basic to intermediate mathematics, and knowledge of matrix operations. It's completely project-based and involves building a text classifier for predicting sentiment of tweets in real-time, and an article summarizer that can fetch articles and find the summary. The course consists of 10.5 hours of on-demand video and eight articles, and costs $19.99, which includes a certificate of completion.

Natural Language Processing in TensorFlow by Coursera. This course is part of Coursera's TensorFlow in Practice Specialization, and covers using TensorFlow to build natural language processing systems that can process text and input sentences into a neural network. Coursera says it's an intermediate-level course and estimates it will take four weeks of study at four to five hours per week to complete.

NLP salaries

Here are some of the most popular job titles related to NLP and the average salary (in US$) for each position, according to data from PayScale.

Computational linguist: $60,000 to $126,000

Data scientist: $79,000 to $137,000

Data science director: $107,000 to $215,000

Lead data scientist: $115,000 to $164,000

Machine learning engineer: $83,000 to $154,000

Senior data scientist: $113,000 to $177,000

Software engineer: $80,000 to $166,000

Natural Language Processing Market Size Analysis: Insights And Forecast To 2030

The Research Report on Natural Language Processing Market [117 Pages] offers thorough perspective on industry performance, latest key trends and comprehensive exploration of Industry segments by Type [Statistical NLP, Hybrid based NLP, Rule NLP], Applications [Machine translation, Information extraction, Report generation, Question answering, Others] and Regions. The report presents concise aspects on key dynamics with market growth rate, size, trade, and insights into key players. It highlights the convergence of market trends, business tactics, and the competitive environment. This report goes beyond conventional analyses by providing both qualitative and quantitative perspectives through SWOT and PESTLE evaluations. Through meticulous research and thorough analysis, the report aims to offer valuable insights to stakeholders, vendors, and various participants within the industry.

In our latest research report, we highlight the rapid growth of the global Natural Language Processing market and provide detailed insights into the projected market size, share, and revenue estimations up to 2030.Ask for Sample Report

Who is the Largest Player of Natural Language Processing Market worldwide?

Microsoft Corporation Salesforce.Com Inc. Inbenta Technologies Inc. AppOrchid Inc. SAS Institute Inc. Adobe Systems Incorporated Klevu Oy Nvidia Corporation Intel Corporation Verint System Inc. NetBase Solutions Inc. SAP SE Genpact Limited IBM Corporation Rasa Technologies GmbH Micro Focus International PLC (HPE) Amazon Web Services Inc. Veritone Inc. 3M Company Babylon Healthcare Services Limited Google Inc.

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What is Market Insights and Analysis?

The global Natural Language Processing market size was valued at USD 13017.98 million in 2022 and is expected to expand at a CAGR of 18.27% during the forecast period, reaching USD 35630.81 million by 2028.

The competitive landscape analysis encompasses a thorough examination of key players operating in the market. It assesses their market presence, product offerings, strategic initiatives, and growth trajectories. This analysis empowers businesses with valuable insights to make informed decisions, adapt to market trends, and devise effective strategies to maintain a competitive edge in the dynamic industry landscape.

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What are the factors driving the growth of the Natural Language Processing Market?

Growing demand for below applications around the world has had a direct impact on the growth of the Natural Language Processing

Machine translation

Information extraction

Report generation

Question answering

Others

What are the types of Natural Language Processing available in the Market?

Based on Product Types the Market is categorized into Below types that held the largest Natural Language Processing market share In 2023.

Statistical NLP

Hybrid based NLP

Rule NLP

Regional Outlook:

North America (United States, Canada and Mexico)

Europe (Germany, UK, France, Italy, Russia and Turkey etc.)

Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)

South America (Brazil, Argentina, Columbia etc.)

Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)

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Following Key Questions Covered in this Report:

What is the Current Market Size and Growth Rate of the Natural Language Processing Market?

What are the Key Trends and Developments Shaping the Natural Language Processing Market?

What are the Main Drivers and Restraints Affecting the Growth of the Natural Language Processing Market?

How is the Natural Language Processing Market Segmented by Manufacturers, Types, Applications, and Regions?

Who are the Major Players in the Natural Language Processing Market and What are Their Strategies?

What is the Competitive Landscape and Market Share of Different Companies?

What are the Future Growth Prospects and Opportunities in the Natural Language Processing Market?

What are the Industry Challenges and Potential Mitigation Strategies?

How is Consumer Behavior Impacting Demand Patterns in the Natural Language Processing Market?

What is the Impact of Regulatory Policies on the Natural Language Processing Market?

What are the Technological Innovations and Advancements in the Natural Language Processing Industry?

What is the Forecasted Market Growth Rate and Potential Size in the Coming Years?

What are the Key Market Entry Barriers and How Can They Be Overcome?

What is the Impact of External Factors, such as COVID-19, on the Natural Language Processing Market?

What are the Evolving Customer Preferences and Their Impact on the Market?

Covid-19 Impact on Natural Language Processing Market:

The unprecedented outbreak of the Covid-19 pandemic has reverberated across industries worldwide, ushering in a period of profound transformation. The landscape of businesses and markets has been reshaped as supply chains were disrupted, consumer behaviors shifted, and economies faced unforeseen challenges. Comprehensive research on the Covid-19 impact on various industries has become imperative to understand the extent of its influence, ranging from disruptions in production and distribution to changes in demand patterns and workforce dynamics. This research delves into the multifaceted repercussions, offering insights into strategies for resilience, adaptation, and recovery. It sheds light on the evolving paradigms within industries, providing a roadmap for stakeholders to navigate these uncertain times with informed decisions and strategic responses.

Key inclusions of the Natural Language Processing market report:

A detailed impact analysis of COVID-19 on the Natural Language Processing market.

In-depth statistical analysis of market size, sales volume, and revenue, segmented by product type, application, and geography.

Comprehensive coverage of major market trends, including drivers, challenges, and opportunities.

Identification and analysis of growth opportunities for businesses operating in the Natural Language Processing market.

Accurate and up-to-date figures showcasing the market growth rate and projected growth trends.

A thorough examination of the advantages and disadvantages of both direct and indirect sales channels in the Natural Language Processing market.

Insights into the key players in the industry, including traders, distributors, and dealers, and their impact on the market.

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Detailed TOC of Natural Language Processing Market Research Report:

1 Natural Language Processing Market Overview

1.1 Product Overview and Scope of Natural Language Processing Market

1.2 Natural Language Processing Market Segment by Type

1.3 Global Natural Language Processing Market Segment by Application

1.4 Global Natural Language Processing Market, Region Wise (2017-2029)

1.5 Global Market Size (Revenue) of Natural Language Processing (2017-2029)

1.5.1 Global Natural Language Processing Market Revenue Status and Outlook (2017-2029)

1.5.2 Global Natural Language Processing Market Sales Status and Outlook (2017-2029)

1.6 Influence of Regional Conflicts on the Natural Language Processing Industry

1.7 Impact of Carbon Neutrality on the Natural Language Processing Industry

2 Natural Language Processing Market Upstream and Downstream Analysis

2.1 Natural Language Processing Industrial Chain Analysis

2.2 Key Raw Materials Suppliers and Price Analysis

2.3 Key Raw Materials Supply and Demand Analysis

2.4 Market Concentration Rate of Raw Materials

2.5 Manufacturing Process Analysis

2.6 Manufacturing Cost Structure Analysis

2.6.1 Labor Cost Analysis

2.6.2 Energy Costs Analysis

2.6.3 RandD Costs Analysis

2.7 Major Downstream Buyers of Natural Language Processing Analysis

2.8 Impact of COVID-19 on the Industry Upstream and Downstream

3 Players Profiles

4 Global Natural Language Processing Market Landscape by Player

4.1 Global Natural Language Processing Sales and Share by Player (2017-2022)

4.2 Global Natural Language Processing Revenue and Market Share by Player (2017-2022)

4.3 Global Natural Language Processing Average Price by Player (2017-2022)

4.4 Global Natural Language Processing Gross Margin by Player (2017-2022)

4.5 Natural Language Processing Market Competitive Situation and Trends

4.5.1 Natural Language Processing Market Concentration Rate

4.5.2 Natural Language Processing Market Share of Top 3 and Top 6 Players

4.5.3 Mergers and Acquisitions, Expansion

5 Global Natural Language Processing Sales, Revenue, Price Trend by Type

5.1 Global Natural Language Processing Sales and Market Share by Type (2017-2022)

5.2 Global Natural Language Processing Revenue and Market Share by Type (2017-2022)

5.3 Global Natural Language Processing Price by Type (2017-2022)

5.4 Global Natural Language Processing Sales, Revenue and Growth Rate by Type (2017-2022)

6 Global Natural Language Processing Market Analysis by Application

6.1 Global Natural Language Processing Consumption and Market Share by Application (2017-2022)

6.2 Global Natural Language Processing Consumption Revenue and Market Share by Application (2017-2022)

6.3 Global Natural Language Processing Consumption and Growth Rate by Application (2017-2022)

7 Global Natural Language Processing Sales and Revenue Region Wise (2017-2022)

7.1 Global Natural Language Processing Sales and Market Share, Region Wise (2017-2022)

7.2 Global Natural Language Processing Revenue and Market Share, Region Wise (2017-2022)

7.3 Global Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.4 United States Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.5 Europe Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.6 China Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.7 Japan Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.8 India Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.9 Southeast Asia Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.10 Latin America Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

7.11 Middle East and Africa Natural Language Processing Sales, Revenue, Price and Gross Margin (2017-2022)

8 Global Natural Language Processing Market Forecast (2022-2029)

8.1 Global Natural Language Processing Sales, Revenue Forecast (2022-2029)

8.2 Global Natural Language Processing Sales and Revenue Forecast, Region Wise (2022-2029)

8.3 Global Natural Language Processing Sales, Revenue and Price Forecast by Type (2022-2029)

8.4 Global Natural Language Processing Consumption Forecast by Application (2022-2029)

8.5 Natural Language Processing Market Forecast Under COVID-19

9 Industry Outlook

9.1 Natural Language Processing Market Drivers Analysis

9.2 Natural Language Processing Market Restraints and Challenges

9.3 Natural Language Processing Market Opportunities Analysis

9.4 Emerging Market Trends

9.5 Natural Language Processing Industry Technology Status and Trends

9.6 News of Product Release

9.7 Consumer Preference Analysis

9.8 Natural Language Processing Industry Development Trends under COVID-19 Outbreak

10 Research Findings and Conclusion

11 Appendix

11.1 Methodology

11.2 Research Data Source

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