From Benchmarking to Reasoning: A Dual-Aspect, Large-Scale Evaluation of LLMs on Vietnamese Legal Text

1. 1
   Define Domain-Specific Challenges

   Identify the unique complexities, nuances, and ambiguities present in your target specialized domain (e.g., legal terminology, cultural context, specific regulations). Understand why generic LLM evaluations fall short for these areas.
2. 2
   Curate High-Quality Domain Data

   Assemble a large, representative dataset of domain-specific texts (e.g., legal documents, medical journals, technical manuals). Annotate it for key entities, relationships, and reasoning tasks relevant to your evaluation goals.
3. 3
   Design Dual-Aspect Evaluation Metrics

   Develop evaluation metrics that go beyond simple accuracy. Include measures for reasoning ability, contextual understanding, legal soundness (for legal texts), coherence, and factual correctness, reflecting the domain's demands.
4. 4
   Implement Domain-Adaptive LLM Techniques

   Apply techniques like Retrieval-Augmented Generation (RAG) or fine-tuning with your curated domain-specific dataset. Prioritize methods that enhance the LLM's ability to interpret nuance and handle ambiguity effectively.
5. 5
   Conduct Reasoning-Focused Evaluation

   Systematically evaluate your adapted LLM using the dual-aspect metrics. Focus on its ability to perform complex reasoning tasks, interpret subtle meanings, and provide coherent, contextually sound outputs aligned with domain standards.
6. 6
   Iterate and Refine

   Analyze evaluation results to identify weaknesses. Refine your dataset, prompt engineering, RAG configuration, or fine-tuning strategy to continuously improve the LLM's domain-specific performance and reasoning capabilities.

import os
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Ensure you have OPENAI_API_KEY set in your environment variables
# Install necessary libraries: pip install langchain openai chromadb tiktoken

# 1. Create a dummy domain-specific document
with open("legal_document.txt", "w") as f:
    f.write("Article 1. Definitions. 'Contract' means this agreement. 'Party' refers to a signatory hereto. All disputes shall be settled by arbitration in Ho Chi Minh City, Vietnam. This document is governed by Vietnamese law.")

# 2. Load your domain-specific document
loader = TextLoader("legal_document.txt")
documents = loader.load()

# 3. Split documents into chunks
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# 4. Create embeddings and store in a vector database
# Requires OPENAI_API_KEY environment variable to be set
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)

# 5. Set up a Retrieval-Augmented Generation (RAG) chain
retriever = db.as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever)

# 6. Query the RAG system with a reasoning-focused question
query = "Based on the document, what is the governing law and where would disputes be settled?"
response = qa_chain.run(query)

print(f"\nQuery: {query}")
print(f"RAG Response: {response}")

# Clean up the dummy file
os.remove("legal_document.txt")

• Paperarxiv.org