You Can't Fight in Here! This is BBS!

Leverage Large Language Models (LLMs) to advance fundamental linguistic research, bridging theoretical linguistics and computational language science. This pack guides AI practitioners in using LLMs to investigate complex linguistic phenomena and foster interdisciplinary collaboration.

intermediate30 min6 steps

The play

Understand the Research Landscape
Familiarize yourself with current research at the intersection of LLMs and theoretical linguistics. Identify key questions LLMs are being used to explore (e.g., syntax, semantics, language acquisition, cognitive modeling).
Define a Linguistic Hypothesis
In collaboration with a linguist (or based on existing literature), formulate a specific linguistic hypothesis that an LLM could help investigate. For example, 'Does LLM X exhibit knowledge of syntactic island constraints?'
Select an LLM and Task
Choose an appropriate LLM (e.g., GPT-4, Llama 3) and design a task (e.g., grammaticality judgment, sentence completion, paraphrase generation, anomaly detection) to probe your hypothesis.
Develop Prompts and Evaluate
Craft specific prompts to elicit data relevant to your hypothesis. Define clear metrics (e.g., accuracy, consistency, adherence to linguistic principles) to systematically evaluate the LLM's responses against established linguistic theories.
Analyze and Interpret Results
Systematically analyze the LLM's output. Interpret the findings in the context of linguistic theory, noting where the LLM aligns with, challenges, or offers novel insights into existing linguistic understanding.
Engage with Linguists
Share your methodology and results with theoretical linguists. Discuss implications, limitations, and future research directions to foster genuine interdisciplinary collaboration and refine research questions.

Starter code

import os
from openai import OpenAI # Or any other LLM client

# To run with OpenAI, uncomment the following lines and set your API key:
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
# client = OpenAI()

def evaluate_grammaticality(sentence):
    """
    Evaluates the grammaticality of a sentence using an LLM.
    """
    prompt = f"Is the following English sentence grammatically correct? Explain your reasoning briefly and identify any errors.\nSentence: '{sentence}'"
    
    # Placeholder for actual LLM API call
    # try:
    #     response = client.chat.completions.create(
    #         model="gpt-3.5-turbo", # Or another suitable model
    #         messages=[
    #             {"role": "system", "content": "You are a helpful linguistic assistant. Analyze sentences for grammatical correctness and provide concise explanations."}, 
    #             {"role": "user", "content": prompt}
    #         ]
    #     )
    #     return response.choices[0].message.content
    # except Exception as e:
    #     return f"Error calling LLM API: {e}. Please ensure your API key is set and valid."
    
    # Mock response for demonstration without an API key
    if "colorless green ideas sleep furiously" in sentence.lower():
        return "The sentence 'colorless green ideas sleep furiously' is grammatically correct but semantically nonsensical. This is a classic example from Chomsky illustrating that syntax can be independent of semantics."
    elif "I likes apples" in sentence:
        return "The sentence 'I likes apples' is grammatically incorrect. The verb 'likes' should be 'like' to agree with the first-person singular subject 'I'."
    else:
        return "This is a placeholder response. Integrate with an actual LLM API for real evaluation."

# Example usage for linguistic inquiry
sentences_to_test = [
    "Colorless green ideas sleep furiously.",
    "I likes apples.",
    "The cat sat on the mat."
]

print("--- Linguistic Inquiry with LLM ---")
for s in sentences_to_test:
    print(f"\nSentence: '{s}'")
    print(f"LLM Evaluation: {evaluate_grammaticality(s)}")

print("\nTo run this with a real LLM, uncomment the OpenAI API calls and set your API key.")

Source

Paperarxiv.org