AI-Assisted Linguistic Inquiry: Utilizing Conversational LLM Chatbots for Construction Grammar and Frame Semantics

Copilots for Linguists AI, Constructions, and Frames (Review)

Title: AI-Assisted Linguistic Inquiry: Utilizing Conversational LLM Chatbots for Construction Grammar and Frame Semantics

Title & Citation:

The book titled AI, Constructions, and Frames by Timponi Torrent, T., Hoffmann, T., Almeida, A. L., & Turner, M. (2023), published by Cambridge University Press, explores the integration of AI, specifically Large Language Models (LLMs), into linguistic research.?

Introduction

The study, "AI-Assisted Linguistic Inquiry: Utilizing Conversational LLM Chatbots for Construction Grammar and Frame Semantics" looks into the possibilities of AI, specifically Large Language Models (LLMs), in enhancing Construction Grammar and frame semantics. The focus of this assessment moves from criticizing AI's language comprehension to utilizing its support for linguists. It looks into proprietary (ChatGPT) and open-source (OpenAssistant) LLM chatbots, with a focus on their functions in linguistic research.

1. Introduction

The Role of AI in Linguistic Research:?

Investigates the potential of AI, specifically Large Language Models (LLMs), in assisting Construction Grammar by investigating form-meaning pairs and construct development.

The focus is on utility:

Shifts discussion from whether AI understands language to how AI can serve linguists.

Experimenting with LLM Chatbots:?

Examines the utility of proprietary (ChatGPT) and open-source (OpenAssistant) LLM chatbots.

2. AI LLM Chatbots as Tools

Eliciting Interlocutors:?

Demonstrates AI chatbots' potential as continuous conversational companions, creating code and responding to corrections.

FrameNet Enhancement:

Investigates AI LLMs' assistance in expanding FrameNet, a library for linguists' frame representations.

3. Human Language Mimicry

Factors Influencing Mimicry:?

The section discusses three major elements that influence chatbots' capacity to mimic human language: user interpretation, training data embeddings, and fine-tuning via reinforcement learning.

Recognition of Linguistic Patterns:?

It is possible that AI LLMs could help linguists recognize and analyze language patterns.

4. Artificial Intelligence LLM Chatbots as Linguistic Tools

Knowledge Extraction Tool:?

Investigates the possibility of AI chatbots as tools for eliciting ideas and prompting linguists to conduct more thorough studies.

ELIZA's Effect:?

Recognizes the tool's ability to provide relevant output without understanding the context, highlighting its utility for linguists.

5. Safety Instructions & Limitations

Limitation Awareness:?

Highlights the limitations and hazards of LLMs, including as content dependability, confabulation, and reliance on training data quality.

The Need for Reliable Training Data:?

Suggests that AI be trained on authoritative literature to improve its reliability as a language informant.

6. Responsible AI Use in Linguistics

Looking at AI as a Tool:?

AI LLM chatbots are highlighted as tools for responsible, productive linguistic study in Frame Semantics and Construction Grammar.

Reliability Assessment:?

Finally, the method's reliability and brittleness are revisited in light of experimentation findings.

This Element examines the promise of AI LLM chatbots as important tools for linguists, while also noting their limitations and recommending responsible use in linguistic research for frame semantics and construction grammar.

2. Constructions

Linguistic Constructions:

1. Approaches to Construction Grammar

Diverse Vantage Points:?

Language is approached by construction grammarians through typology, language learning, cognitive mechanisms, formalization, and computational implementation.

Various Approaches:?

The theoretical assumptions and emphases of Radical Construction Grammar, Cognitive Construction Grammar, Embodied Construction Grammar, Fluid Construction Grammar, and Sign-Based Construction Grammar differ (References Hoffmann & Trousdale, 2013; Hoffmann, 2022a; Ungerer & Hartmann, 2023).

Focused Attention:?

Language, according to all constructionist views, lacks derivation, is nonmodular, and is centered on constructions (form-meaning pairs).

2. Cognitive Frames in Language

Concept of Frames:

The meaning side of language frequently corresponds with cognitive frames, which are bundles of organized knowledge employed in human reasoning (Fillmore, 1968; Fillmore & Atkins, 1992; Fillmore et al., 1988).

Frame Activation:?

Frames are used by speakers to understand expressions by activating mental arrays relevant to the situation, allowing for numerous interpretations and forced frame shifts.

3. Form Eliciting Frames

Forms Evoking Frames:

Words, gestures, sights, and grammatical patterns all elicit cognitive frames, launching mental webs matching to certain situations or concepts.

Syntactic Patterns and Frames:?

Syntactic structures such as argument constructions use frame activation to encode specific meanings (e.g., causality, transfer).

4. Range of Constructions

From Morphology to Abstract Structures:?

Constructions include morphological templates, word constructions, and abstract constructs such as the "X is the Y of Z" pattern, and they use blending and compression to create meaning.

Complex Constructions:?

The XYZ construction is an example of a clausal structure that necessitates the mixing of frames to communicate metaphorical meanings.

5. Construction Expansion

Discourse-Level Constructions:

?Researchers propose schematic discourse-level constructs in a variety of contexts, including sports chants, Knock Knock jokes, and TED speeches.

6. Artificial Intelligence LLM Chatbots in Constructionist Research

Assistance in Construction Analysis:?

AI LLM chatbots can help with the earlier described constructions by acting as case studies for constructionist research in language.

This section describes the wide kinds of constructions studied in Construction Grammar, ranging from numerous techniques to various forms and syntactic patterns invoking cognitive frameworks, concluding in the prospective role of AI LLM chatbots in assisting constructionist research.

3 Using an AI to Help Study Constructions

An AI LLM chatbot, for example, can be trained to provide further examples and analyses of structures like the un-VERB construction. ChatGPT and OpenAssistant can be asked to offer 10 more construction examples. The models generalize the pattern "to reverse a V-ing action" to each of the ten examples, but they fail to provide polysemous meanings. The frequency of the verb lemma UNVERB_v* and VERB_* may influence the output of the model. Because the precise content of the training data is unknown, a proxy query was used as a substitute. The chatGPT findings are typically satisfactory, however the frequency of un-V words and related V words has an effect on the model.

Examples and analyses of prompts and replies to the "un-VERB" construction and caused-motion constructions are provided.

Questions:?

1. What are the two artificial intelligence chatbots discussed in the context??

2. What kind of linguistic construction is employed in the first example prompt concerning a "un" construction??

3. How many examples of the "un-VERB" construction does the first prompt request from ChatGPT??

4. Against which corpus is the frequency of verb lemmas in the chatbot samples compared??

5. In the third example prompt about intransitive verbs, what form of linguistic frame is discussed??

4 Limitations of LLMs for Constructional Analysis

The difficulties of employing AI LLM chatbots for constructional analysis, notably in multilinguality and semantics, are discussed in the section. The ChatGPT and OpenAssistant tests highlight two key obstacles for their use as a tool for constructionist analysis. The section presents two prompts given to both chatbots in order for them to examine structures in various languages, including Brazilian Portuguese. The first part is about the Split Argument construction in Brazilian Portuguese, which follows the NP V NP syntactic pattern and gives both NP and patient-like object NP a patient interpretation. In their first effort, the chatbots failed to provide a single right example for the construction, and when corrected by the user, they failed again.?

5 Cognitive Frames and FrameNet

The section examines the prospect of deploying AI as a copilot for Construction Grammar research. It emphasizes the significance of Frame Semantics and FrameNet, a global computer lexicographic research initiative. The publication also includes techniques for instructing AI chatbots to help linguists with frame building. The section does, however, underline the limitations of chatbots and the importance of thorough human inspection of their output.

5.1 Data-Driven Approaches to FrameNet Expansion

The three main dimensions of FrameNet growth are: producing new frames, annotating known frames with more examples, and discovering new lexical units (LUs) for existing frames. To avoid misinterpretation, creating new frames necessitates deep conceptualization, rigorous description, and formulation. Annotating known frames necessitates comprehensive text annotations by lexicographers. Exploring diverse language resources to increase the database's lexical coverage is required while discovering new LUs. Different resource categories provide distinct expansion strategies. FrameNet+, an enhanced version, used a paradatabase to identify over 22K new Frame/LU pairs by comparing equivalence between original and paradatabase annotations. Models such as SDEC-AD, while assisting in LU discovery, require careful evaluation because to FrameNet's changing nature. Other techniques, such as SemEval-2019 models, can help with identifying verb frames and argument structures, but they have limits in terms of annotation scope and training constraints. While data-driven algorithms show promise in LU discovery, producing complete annotations remains a difficult undertaking that frequently necessitates manual intervention. AI language models such as OpenAssistant and ChatGPT could potentially help linguists navigate these difficulties. Furthermore, tools like "Lutma" focus on making it easier to create entire frame entries, which contributes significantly to FrameNet expansion efforts.

5.2 Lutma: A Frame Maker Tool

"Lutma," a technology developed by FrameNet Brasil, aids in the expansion of FrameNets and the creation of a Global FrameNet database. It directs frame augmentation and generation, combining community-contributed frames with existing ones using established parameters based on Berkeley FrameNet patterns. Lutma simplifies frame construction with guided stages, although it lacks advanced pattern recognition capabilities. Linguists could benefit from integrating AI systems such as ChatGPT and OpenAssistant. Five experiments investigate the potential of AI LLM chatbots in supporting frame semanticists.

6 Prompt Engineering for Building FrameNet

The use of AI LLM chatbots such as ChatGPT and OpenAssistant has advantages over traditional tools for FrameNet extension in that it requires no coding expertise and provides intuitive conversation. The emphasis when prompting these chatbots is on frame components: names, definitions, FE types, and LU part-of-speech. Due to the intricacy of extensive full-text annotations, frame-to-frame relations are selectively added to prompts, boosting interactivity. A Python script automates template generation, guiding chatbots to generate frame elements or suggest additional frames or LUs, and providing responses in a variety of formats for easy processing.

6.1 Experiment 1: FrameNet Augmentation via New Lexical Units

Experiment 1 investigates the performance of AI chatbots in proposing new English lexical units (LUs) for current FrameNet frames. The prompts disclose frame specifics and request further LUs, preventing data duplication. Controlling the number of LUs requested aids in balancing exploratory responses and accuracy. The effectiveness of chatbots is influenced by factors such as frame complexity and LU count. Despite their differences, both chatbots provide unique LUs not available in FrameNet+. ChatGPT outperforms, demonstrating potential as a copilot by recommending proper LUs in the majority of instances. The performance of OpenAssistant is marginally poorer but still useful, indicating that there is space for improvement through prompt engineering and user involvement. Human review is still required when inserting suggested LUs into FrameNet's database.

6.2 Experiment 2: FrameNet Expansion into Other Languages

In the second experiment, the potential of AI LLM chatbots to aid FrameNet's spread into other languages was assessed. Traditional methods translate English LUs into other languages to elicit similar frames. Chatbots, on the other hand, provide user-friendly, sensible suggestions, as seen in Experiment 1. We wanted to see how well they could propose lexical units in Brazilian Portuguese for fully defined English frames, thus we compared them to FrameNet+ and FrameNet Brasil. Due to its training data in Brazilian Portuguese, OpenAssistant struggled, suggesting only 42% valid LUs for entity frames. ChatGPT outperformed, implying semantically aligned LUs, especially in circumstances with more lexical units, while it had several drawbacks. Despite language-specific obstacles, chatbots show promise as copilots, underlining the importance of human supervision and rapid refining for effective FrameNet extension into other languages.

6.3 Experiment 3: Entity Frame Building

Experiment 2 attempted to use AI LLM chatbots to expand FrameNet into different languages. It tested the ability of ChatGPT and OpenAssistant to propose Brazilian Portuguese lexical units (LUs) for English frames by translating English LUs. Due to a lack of Portuguese training data, OpenAssistant struggled, providing proper LUs in just 42% of entity frames. ChatGPT fared better, following the tendencies of Experiment 1 but faltering in specific instances. Notably, the proposed LUs using ChatGPT frequently echoed FN-Br entries, showing a 23-22% overlap. Despite their limitations, AI LLMs can help, but training data gaps impede performance. Experiment 3 looked at frame building, revealing that chatbots struggled to generate FrameNet-like frames but improved with targeted instructions. Prompt engineering has a huge impact on chatbot output, emphasizing their lack of language awareness yet assisting with linguistic analysis.

6.4 Experiment 4: Attribute Frame Building

The experiment investigates FrameNet frames connected to entity attributes. It examines frames that inherit from "Gradable_attributes," a nonlexical frame used for entity attributes. ChatGPT and OpenAssistant both provide frames that include essential aspects such as "Entity" and "Degree." ChatGPT displays frames with single attributes (e.g., Size, Loudness) and excludes superfluous frame elements, whereas OpenAssistant displays frames with inconsistent elements. Adding "Attribute" and "Value" to the prompt increases their performance. The issue is a lack of understanding of the intricacies of inheriting frames, emphasizing the difficulty in forecasting frames merely based on distributional patterns of lexical elements, particularly for more abstract frames.

6.5 Experiment 5: Eventive Frame Building

Experiment 6.5 dives into FrameNet's Eventive Frame Building, stressing its frequency. The Event frame, which includes Place, Time, and Event (often left unsaid), represents the changing character of occurrences throughout time and location. This study looks into frames that inherit from Event and divides them into two groups: those that form subtype networks and those that build smaller inheritance networks. ChatGPT and OpenAssistant were prompted using frames inherited from Event at two levels of inheritance: Objective_influence and Transitive_action. The goal of the experiment was to compare their performance in terms of FE complexity and LU productivity. The second frame produced better results. Both models had errors when it came to suggesting frames, focusing on core and unexpressed FEs and LU options. Corrections improved some FEs but did not eliminate all inaccuracies. The tests demonstrate the influence of minor prompt adjustments on model performance and emphasize the difficulty in refining the outputs of AI LLM copilots in language applications.

7 Final Safety Instructions: Risks and Limitations Revisited

This section revisits the safety recommendations for AI LLM chatbots, stressing their utility in linguistic research and noting their limitations. ChatGPT and OpenAssistant generate responses without validating their accuracy, which could lead to incorrect results. Private systems, such as ChatGPT, lack openness in training data and impede research control. Open-source models, such as OpenAssistant, provide stability and enable domain-specific training, allowing for comparison studies for greater performance in language research.

8 Imagining the Future of Copilots for Linguists

Linguists collaborate with AI helpers in this article, exploiting their huge data understanding. Despite possible drawbacks, these AI copilots provide vital assistance by evaluating vast amounts of data from many ages and civilizations. However, effective use necessitates verbal mastery in order to prompt these assistance. Transparency and shared knowledge play a critical role in developing this partnership and forging a new frontier in linguistic study.

Summary:

The book analyzes the possibilities of AI, specifically LLMs, in assisting Construction Grammar and Frame Semantics, turning the conversation away from AI comprehension and toward its utility for linguists. It investigates the usage of proprietary (ChatGPT) and open-source (OpenAssistant) LLM chatbots, demonstrating their utility as conversational tools and as a means of expanding FrameNet. The debate dives into AI's mimicking of human language, its limitations, and the importance of responsible use in linguistic study. The book discusses a variety of language constructs, cognitive frames, form-eliciting frames, and the significance of AI in constructionist research. It also describes research with AI chatbots for FrameNet augmentation, language expansion, and frame creation.

Critique:

Clarity & Structure:

The book presents a thorough examination of AI integration into language study, with a well-organized breakdown of subjects. The sectioning helps to understand the varied nature of AI's engagement in Construction Grammar.

AI in Linguistics Research:

The work skillfully switches the emphasis from AI comprehension to its pragmatic application in language research, providing subtle insights on how AI can help linguists. However, for a more practical understanding, it could dig deeper into specific case studies or real-world applications.

AI LLM Chatbot Evaluation:

With examples and experiments, the book successfully examines the strengths and limitations of AI LLM chatbots, discussing their potential in supporting linguists. A more critical examination of the chatbots' flaws and potential for growth, on the other hand, could enrich the discussion.

Cognitive Frames and Constructions:

The book provides a thorough introduction of language constructions and cognitive frames, encompassing a wide range of techniques and theoretical viewpoints. A more compact presentation, on the other hand, may improve readability without sacrificing content richness.

Experiments and Real-World Applications:

Experiments with AI LLM chatbots to enhance FrameNet extension and constructionist research yield useful insights. However, a more in-depth examination of the actual applications of these findings in language studies could help the reader understand better.

Safety and Responsible AI Use:

The book handles safety concerns appropriately and supports for responsible AI use in language research. A more in-depth examination of ethical considerations and solutions for minimizing AI biases, on the other hand, could be advantageous.

Prospects for the Future:

The text successfully finishes by imagining a future in which AI copilots help linguists analyze data. However, it could be beneficial to examine the potential problems and ethical implications of further AI integration in linguistic study.

Overall Assessment:

AI, Constructions, and Frames is a remarkable examination of AI's integration into linguistic study, providing extensive insights into the promise, limitations, and ethical implications of AI LLM chatbots. While the book gives an organized and instructive overview, more in-depth coverage of real-world applications and a critical examination of AI's position in linguistics could increase its influence.

Recommendation:

This book is an essential resource for academics and practitioners interested in the interface of AI and linguistics. It lays the groundwork for future research and practical applications in the sector. Supplementing theoretical discussions with specific case studies or actual scenarios may help a broader audience gain a more practical grasp.

Conclusion

While acknowledging their limits, this study proposes AI LLM chatbots as interesting instruments for linguistic investigation within Construction Grammar and frame semantics. While these chatbots provide extensive data insights, they must be used responsibly due to content dependability difficulties. Moving forward, transparent prompt engineering, refined human supervision, and shared information will be required to effectively utilize these AI copilots in language research. This partnership opens up new opportunities for linguistics, but it takes linguistic skill to effectively guide these tools.

Acknowledgments:

The authors thank the Case Western Reserve AI Strategy Group and the Case Western Research University HPC Cluster for their assistance. This work was made possible by NSF award 2117439, CNPq Research award no. 315749/2021-0, and CAPES Grant no. 88887.816228/2023-00.

Thomas Hoffmann:

Full Professor Thomas Hoffmann focuses in Construction Grammar, language diversity, and linguistic creativity at the Catholic University of Eichst?tt-Ingolstadt. He is also a Hunan Normal University Furong Scholar Distinguished Chair Professor.

Alexander Bergs:

Alexander Bergs, Full Professor at the University of Osnabrück, has published extensively in international journals on language variation, constructional approaches, syntax/pragmatics interaction, and cognitive poetics.

About the Series:

This Elements series digs into Construction Grammar, a major cognitive theory of syntax, and investigates theoretical underpinnings, linguistic phenomena, and developing frontier subjects in a variety of languages.

Reference:

Timponi Torrent, T., Hoffmann, T., Almeida, A. L., & Turner, M. (2023). AI, Constructions, and Frames. Elements in Construction Grammar. Cambridge University Press. https://doi.org/10.1017/9781009439190

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