CLM Dictionary

Overview

The CLM Vocabulary system is the semantic foundation of CLLM compression. It defines mappings from natural language words and phrases to compressed semantic tokens, enabling intelligent compression that preserves meaning while dramatically reducing token count.

Purpose:

• Maps verbose phrases to concise tokens
• Identifies important vs. redundant words
• Provides language-specific semantic understanding
• Enables consistent token generation across compression types

Structure: Each language has its own Vocabulary class (e.g., ENVocabulary, PTVocabulary) that inherits from BaseVocabulary and defines language-specific word lists, mappings, and patterns.

Vocabulary Architecture

Language-Specific Vocabularies

# Available vocabularies
ENVocabulary()  # English - Complete
PTVocabulary()  # Portuguese - Complete
ESVocabulary()  # Spanish - Complete
FRVocabulary()  # French - Complete
# Others in development

Access via CLMConfig:

from clm_core import CLMConfig, CLMEncoder

config = CLMConfig(lang="en")
vocab = config.vocab  # ENVocabulary instance

# Use vocabulary for compression
encoder = CLMEncoder(cfg=config)

Core Vocabulary Categories

The vocabulary system is organized into 25+ categories, each serving a specific purpose in compression.

1. CODE_INDICATORS

Purpose: Identifies code and technical content

Examples:

CODE_INDICATORS = (
    "code", "script", "function", "program", "algorithm",
    "api", "class", "method", "variable", "git", "commit",
    "unittest", "test case", "debug", "refactor"
)

Use case:

Input: "Review the code in the function and check for bugs"
Detection: "code", "function" → CODE domain
Output: [REQ:REVIEW] [TARGET:CODE:DOMAIN=CODE] [EXTRACT:BUGS]

2. ACTION_VERBS

Purpose: Comprehensive list of action verbs for intent detection

Categories:

• Modification: reduce, increase, improve, optimize, enhance, update, modify
• Problem-solving: fix, solve, resolve, debug, repair, troubleshoot, diagnose
• Creation: create, generate, build, make, produce, design, develop
• Analysis: analyze, examine, review, evaluate, assess, validate, verify
• Explanation: explain, describe, clarify, define, document
• Processing: calculate, compute, determine, find, process
• Organization: compare, contrast, classify, categorize, sort, organize
• Data handling: extract, transform, load, aggregate, filter
• Operations: deploy, release, rollback, scale, provision

Examples (60+ verbs):

ACTION_VERBS = (
    "reduce", "increase", "improve", "optimize", "enhance",
    "fix", "solve", "resolve", "debug", "repair",
    "create", "generate", "build", "make", "produce",
    "analyze", "examine", "review", "evaluate", "assess",
    "explain", "describe", "clarify", "define", "document",
    # ... 40+ more
)

Use case:

Input: "Analyze the data and generate a report"
Verbs detected: "analyze" → ANALYZE, "generate" → GENERATE
Output: [REQ:ANALYZE,GENERATE] [TARGET:DATA] [OUT:REPORT]

3. STOPWORDS

Purpose: Words to remove during compression (no semantic value)

Examples:

STOPWORDS = (
    "it", "this", "that", "these", "those",
    "a", "an", "the",
    "some", "any", "all", "none",
    "something", "anything", "everything", "nothing",
    "someone", "anyone", "everyone", "no one"
)

Use case:

Input: "Please analyze this data and provide the results"
Removal: "Please", "this", "the" → filtered out
Output: [REQ:ANALYZE] [TARGET:DATA] [OUT:RESULTS]

4. PRONOUNS

Purpose: Personal pronouns to filter or simplify

Examples:

PRONOUNS = (
    "i", "we", "you", "they", "he", "she", "it",
    "me", "us", "them", "him", "her",
    "my", "our", "your", "their", "his", "her", "its"
)

Use case:

Input: "I need you to analyze my data"
Simplified: "I", "you", "my" → filtered
Output: [REQ:ANALYZE] [TARGET:DATA]

5. MODALS

Purpose: Modal verbs that add no semantic content

Examples:

MODALS = (
    "can", "could", "should", "would", "will", "shall",
    "may", "might", "must",
    "do", "does", "did"
)

Use case:

Input: "You should analyze the code and you could check for bugs"
Removed: "should", "could" → no impact on meaning
Output: [REQ:ANALYZE] [TARGET:CODE] [EXTRACT:BUGS]

6. COMPOUND_PHRASES

Purpose: Multi-word phrases mapped to single tokens

Examples:

COMPOUND_PHRASES = {
    "customer support": "TICKET",
    "customer service": "TICKET",
    "support ticket": "TICKET",
    "help desk": "TICKET",
    
    "chat clm_core": "TRANSCRIPT",
    "conversation clm_core": "TRANSCRIPT",
    
    "source code": "CODE",
    "code review": "CODE",
    "pull request": "CODE",
    
    "error message": "LOG",
    "stack trace": "LOG",
    "system log": "LOG",
    
    "api endpoint": "ENDPOINT",
    "rest api": "ENDPOINT",
    
    "unit test": "TEST",
    "test case": "TEST",
    
    "business plan": "PLAN",
    "project plan": "PLAN",
    
    "database query": "QUERY",
    "sql query": "QUERY"
}

Use case:

Input: "Analyze the customer support ticket and check the error message"
Mapping: "customer support" → TICKET, "error message" → LOG
Output: [REQ:ANALYZE] [TARGET:TICKET] [EXTRACT:LOG]

Impact: Reduces multi-word phrases to single tokens, significant compression

7. TYPE_MAP

Purpose: Document/content type identification

Examples:

TYPE_MAP = {
    "call": "CALL",
    "phone call": "CALL",
    "meeting": "MEETING",
    "chat": "CHAT",
    "email": "EMAIL",
    "message": "EMAIL",
    "conversation": "CONVERSATION",
    "report": "REPORT",
    "document": "DOCUMENT",
    "article": "ARTICLE",
    "clm_core": "TRANSCRIPT",
    "ticket": "TICKET",
    "case": "TICKET",
    "complaint": "COMPLAINT",
    "feedback": "FEEDBACK",
    "inquiry": "INQUIRY",
    "request": "REQUEST"
}

Use case:

Input: "Analyze the customer support call transcript"
Type detection: "call" → CALL, "transcript" → TRANSCRIPT
Output: [REQ:ANALYZE] [TARGET:CALL,TRANSCRIPT:DOMAIN=SUPPORT]

8. CONTEXT_MAP

Purpose: Domain/context identification

Examples:

CONTEXT_MAP = {
    "customer": "CUSTOMER",
    "support": "SUPPORT",
    "sales": "SALES",
    "technical": "TECHNICAL",
    "engineering": "TECHNICAL",
    "product": "PRODUCT",
    "marketing": "MARKETING",
    "business": "BUSINESS",
    "finance": "FINANCE",
    "legal": "LEGAL",
    "hr": "HR",
    "operations": "OPERATIONS"
}

Use case:

Input: "Analyze the technical support ticket"
Context: "technical" → TECHNICAL, "support" → SUPPORT
Output: [REQ:ANALYZE] [TARGET:TICKET:DOMAIN=TECHNICAL,SUPPORT]

9. domain_candidates

Purpose: Keywords that indicate specific domains

Domains (15 total):

CODE:

"bug", "error", "security", "performance", "code", "script",
"function", "algorithm", "debug", "compile", "library", "api"

ENTITIES:

"names", "dates", "amounts", "addresses", "emails", "phones"

QA:

"verification", "policy", "soft_skills", "accuracy", 
"compliance", "disclosures"

SUPPORT:

"issue", "sentiment", "actions", "urgency", "priority",
"ticket", "customer", "agent", "troubleshoot"

TECHNICAL:

"bug", "error", "stacktrace", "api", "server", "log",
"debug", "crash", "deployment", "backend"

DOCUMENT:

"document", "article", "manual", "guide", "clm_core",
"notes", "summary", "instructions"

BUSINESS:

"report", "analysis", "executive", "management", "dashboard",
"kpi", "roi", "quarterly", "presentation"

LEGAL:

"contract", "policy", "compliance", "gdpr", "clause",
"agreement", "terms", "privacy"

FINANCE:

"invoice", "billing", "payment", "transaction", "refund",
"expense", "balance", "statement"

SECURITY:

"breach", "risk", "threat", "alert", "malware", "phishing",
"permissions", "access control", "audit"

MEDICAL:

"patient", "diagnosis", "prescription", "clinical", "symptoms",
"treatment", "doctor"

SALES:

"lead", "crm", "opportunity", "pipeline", "prospect", "deal", "quote"

EDUCATION:

"lesson", "curriculum", "teacher", "student", "training", "course"

Use case:

Input: "Analyze the invoice for compliance with GDPR policy"
Keywords: "invoice" → FINANCE, "compliance", "gdpr", "policy" → LEGAL
Output: [REQ:ANALYZE] [TARGET:INVOICE:DOMAIN=FINANCE,LEGAL]

10. REQ_TOKENS

Purpose: Maps action phrases to standardized request tokens

Main categories (23 total):

ANALYZE:

"analyze", "review", "examine", "evaluate", "assess",
"inspect", "check out", "audit", "investigate"

EXTRACT:

"extract", "pull out", "identify", "find", "locate",
"get", "retrieve", "return", "include", "select"

GENERATE:

"generate", "create", "write", "draft", "compose",
"produce", "build", "develop", "suggest", "formulate"

SUMMARIZE:

"summarize", "condense", "brief", "synopsis", "sum up",
"digest", "recap"

TRANSFORM:

"convert", "transform", "change", "rewrite", "translate",
"modify", "adapt", "rephrase", "edit", "add", "remove"

EXPLAIN:

"explain", "describe", "clarify", "elaborate", "tell me about",
"detail", "illustrate", "discuss", "define"

COMPARE:

"compare", "contrast", "versus", "vs", "difference between",
"differentiate", "distinguish"

CLASSIFY:

"classify", "categorize", "sort", "group", "label",
"organize", "arrange", "segment"

DEBUG:

"debug", "troubleshoot", "diagnose", "fix bug", "investigate bug",
"find bug", "track down", "identify issue"

OPTIMIZE:

"optimize", "improve", "enhance", "refactor", "speed up",
"streamline", "maximize", "minimize", "reduce", "increase"

VALIDATE:

"validate", "verify", "check", "confirm", "test",
"ensure", "certify", "authenticate"

And 12 more categories: SEARCH, RANK, PREDICT, FORMAT, DETECT, CALCULATE, AGGREGATE, DETERMINE, ROUTE, EXECUTE, LIST, MATCH, SELECT

Use case:

Input: "Please summarize the document and extract key entities"
Mapping: "summarize" → SUMMARIZE, "extract" → EXTRACT
Output: [REQ:SUMMARIZE,EXTRACT] [TARGET:DOCUMENT] [EXTRACT:ENTITIES]

11. TARGET_TOKENS

Purpose: Maps object descriptions to standardized target tokens

Categories (40+ targets):

Code & Technical:

"CODE": ["code", "script", "program", "function"],
"QUERY": ["query", "sql", "database query"],
"ENDPOINT": ["endpoint", "api", "rest endpoint"],
"COMPONENT": ["component", "module", "package"],
"SYSTEM": ["system", "application", "software"],
"TEST": ["test", "unit test", "test case"],
"LOG": ["log", "logs", "error log"]

Documents:

"DOCUMENT": ["document", "doc", "file", "report"],
"EMAIL": ["email", "message", "correspondence"],
"REPORT": ["report", "analysis", "findings"],
"TRANSCRIPT": ["clm_core", "conversation", "chat log"]

Customer Service:

"TICKET": ["ticket", "support ticket", "issue", "case"],
"COMPLAINT": ["complaint", "issue", "problem"],
"REQUEST": ["request", "service request"],
"INQUIRY": ["inquiry", "question", "query"],
"CALL": ["call", "phone call", "support call"]

Business:

"PLAN": ["plan", "business plan", "project plan"],
"POST": ["post", "linkedin post", "blog post"],
"SUMMARY": ["summary", "executive summary"],
"METRICS": ["revenue", "metrics", "statistics"]

Specialized:

"NBA_CATALOG": ["nba", "next best action", "predefined actions"],
"CUSTOMER_INTENT": ["customer intent", "customer need"],
"CORRELATION": ["correlation", "relationship"],
"TRADEOFF": ["trade-off", "tradeoffs"],
"PATTERN": ["pattern", "patterns", "trend"],
"CHURN": ["churn", "customer churn", "attrition"]

Use case:

Input: "Analyze the NBA catalog and identify patterns in customer churn"
Mapping: "nba catalog" → NBA_CATALOG, "patterns" → PATTERN, "customer churn" → CHURN
Output: [REQ:ANALYZE] [TARGET:NBA_CATALOG] [EXTRACT:PATTERN,CHURN]

12. EXTRACT_FIELDS

Purpose: Standardized field names for extraction

Categories (50+ fields):

Customer Service:

"ISSUE", "SENTIMENT", "ACTIONS", "NEXT_STEPS",
"URGENCY", "PRIORITY", "CUSTOMER_INTENT"

Entities:

"NAMES", "DATES", "AMOUNTS", "EMAILS", "PHONES", "ADDRESSES"

Technical:

"BUGS", "SECURITY", "PERFORMANCE", "ERRORS", "WARNINGS"

Analysis:

"KEYWORDS", "TOPICS", "ENTITIES", "FACTS", "DECISIONS",
"REQUIREMENTS", "FEATURES", "PROBLEMS", "SOLUTIONS", "RISKS"

Metrics:

"METRICS", "KPI", "SCORES", "RATINGS", "RELEVANCE_SCORE",
"MATCH_CONFIDENCE", "SEMANTIC_SIMILARITY"

Metadata:

"OWNERS", "ASSIGNEES", "STAKEHOLDERS", "PARTICIPANTS",
"TIMESTAMPS", "DURATIONS", "FREQUENCIES", "QUANTITIES",
"CATEGORIES", "TAGS", "LABELS", "STATUS", "TYPE"

Use case:

Input: "Extract sentiment, urgency, and next steps from the ticket"
Fields: "sentiment" → SENTIMENT, "urgency" → URGENCY, "next steps" → NEXT_STEPS
Output: [REQ:EXTRACT] [TARGET:TICKET] [EXTRACT:SENTIMENT,URGENCY,NEXT_STEPS]

13. OUTPUT_FORMATS

Purpose: Specifies desired output format

Examples:

OUTPUT_FORMATS = {
    "JSON": ["json", "json format"],
    "MARKDOWN": ["markdown", "md"],
    "TABLE": ["table", "tabular"],
    "LIST": ["list", "bullet points", "bullets"],
    "PLAIN": ["plain text", "text only"],
    "CSV": ["csv", "comma-separated"]
}

Use case:

Input: "Analyze the data and return results as JSON"
Format: "json" → JSON
Output: [REQ:ANALYZE] [TARGET:DATA] [OUT:JSON]

14. rank_triggers

Purpose: Identifies ranking/sorting requests

Examples:

rank_triggers = {
    "rank", "ranking", "sort", "sort by", "order", "order by",
    "prioritize", "priority", "most important", "least important",
    "top", "bottom", "first", "last", "highest", "lowest",
    "best", "worst", "greatest", "least", "maximum", "minimum",
    "arrange"
}

Use case:

Input: "Rank the tickets by priority and sort by urgency"
Triggers: "rank", "sort" → REQ:RANK
Output: [REQ:RANK] [TARGET:TICKETS] [BY:PRIORITY,URGENCY]

15. NOISE_VERBS

Purpose: Common verbs with little semantic value to filter

Examples:

NOISE_VERBS = {
    "be", "have", "do", "can", "could", "should", "would",
    "may", "might", "must", "will", "shall",
    "go", "come", "take", "get", "make", "work", "live",
    "know", "think", "feel", "say", "call"
}

Use case:

Input: "I would like you to have the system analyze the data"
Filter: "would", "have" → removed
Output: [REQ:ANALYZE] [TARGET:DATA]

16. IMPERATIVE_PATTERNS

Purpose: Command patterns that map to actions

Examples:

IMPERATIVE_PATTERNS = [
    (["list", "enumerate", "itemize"], "LIST", "ITEMS"),
    (["name", "identify"], "GENERATE", "ITEMS"),
    (["give", "provide", "suggest"], "GENERATE", "ITEMS"),
    (["tell", "explain", "describe"], "EXPLAIN", "CONCEPT")
]

Use case:

Input: "List the top 5 issues"
Pattern: "list" → LIST + ITEMS
Output: [REQ:LIST] [TARGET:ITEMS] [LIMIT:5]

17. QUESTION_WORDS

Purpose: Question word detection for query analysis

Examples:

QUESTION_WORDS = ["what", "who", "where", "when", "why", "how", "which"]

Use case:

Input: "What are the main issues?"
Detection: "what" → question pattern
Output: [REQ:EXTRACT] [TARGET:ISSUES:TYPE=MAIN]

Vocabulary Usage in Compression

Flow: Natural Language → Compressed Tokens

Step 1: Text Input

"Please analyze the customer support transcript and extract 
sentiment, urgency, and next steps"

Step 2: Vocabulary Matching

"analyze" → REQ_TOKENS["ANALYZE"]
"customer support transcript" → COMPOUND_PHRASES["customer support"] + TYPE_MAP["transcript"]
"extract" → REQ_TOKENS["EXTRACT"]
"sentiment" → EXTRACT_FIELDS["SENTIMENT"]
"urgency" → EXTRACT_FIELDS["URGENCY"]
"next steps" → EXTRACT_FIELDS["NEXT_STEPS"]

Step 3: Token Generation

[REQ:ANALYZE,EXTRACT] 
[TARGET:TRANSCRIPT:DOMAIN=SUPPORT] 
[EXTRACT:SENTIMENT,URGENCY,NEXT_STEPS]

Step 4: Compression

Original: 87 tokens
Compressed: 23 tokens
Reduction: 73.6%

Language-Specific Vocabularies

English (ENVocabulary)

Status: ✅ Complete
Coverage: All 25+ categories fully populated
Quality: Production-ready
Size: 1,000+ mappings

Strengths:

• Comprehensive technical vocabulary
• Extensive business terminology
• Rich customer service terms
• Complete domain coverage

Portuguese (PTVocabulary)

Status: ✅ Complete
Coverage: All categories with Portuguese translations
Quality: Production-ready

Example mappings:

REQ_TOKENS = {
    "ANALYZE": ["analisar", "revisar", "examinar", "avaliar"],
    "EXTRACT": ["extrair", "obter", "identificar", "localizar"],
    "GENERATE": ["gerar", "criar", "escrever", "produzir"]
}

TARGET_TOKENS = {
    "DOCUMENTO": ["documento", "doc", "arquivo"],
    "TRANSCRIPT": ["transcrição", "conversa", "diálogo"]
}

Spanish (ESVocabulary)

Status: ✅ Complete
Coverage: All categories with Spanish translations
Quality: Production-ready

Example mappings:

REQ_TOKENS = {
    "ANALYZE": ["analizar", "revisar", "examinar", "evaluar"],
    "EXTRACT": ["extraer", "obtener", "identificar", "localizar"],
    "GENERATE": ["generar", "crear", "escribir", "producir"]
}

French (FRVocabulary)

Status: ✅ Complete
Coverage: All categories with French translations
Quality: Production-ready

Example mappings:

REQ_TOKENS = {
    "ANALYZE": ["analyser", "examiner", "évaluer", "inspecter"],
    "EXTRACT": ["extraire", "obtenir", "identifier", "localiser"],
    "GENERATE": ["générer", "créer", "écrire", "produire"]
}

Extending Vocabulary

Understanding Vocabulary Categories

Each category serves a specific purpose:

Best Practices

1. Leverage Compound Phrases

Impact: Highest compression ratio

# Without compound phrase
"customer support ticket" → 3 tokens

# With COMPOUND_PHRASES mapping
"customer support" → TICKET → 1 token
Total: "customer support ticket" → TICKET → 1 token (66% reduction)

2. Use Domain-Specific Vocabularies

# Generic
config = CLMConfig(lang="en")

# Add domain context helps
# (domain is inferred from vocabulary matches)

3. Understand Token Priority

Priority order:

1. Required tokens (REQ, TARGET) - Always present
2. Extract fields - When extraction requested
3. Context - When domain detected
4. Output - When format specified
5. Metadata - When available

4. Test Vocabulary Coverage

# Check if vocabulary handles your domain
test_phrases = [
    "analyze customer support ticket",
    "extract sentiment from clm_core",
    "generate next best action"
]

for phrase in test_phrases:
    result = encoder.encode(phrase)
    print(f"{phrase} → {result.compressed}")
    print(f"Compression: {result.compression_ratio:.1%}")

Advanced: Vocabulary Statistics

English Vocabulary Size

Coverage by Domain

Based on domain_candidates:

Troubleshooting

Issue: Low Compression in Specific Domain

Symptom: Compression ratio lower than expected for your content type

Solution: Check if vocabulary has coverage for your domain

# Check domain candidates
config = CLMConfig(lang="en")
vocab = config.vocab

# Does your domain exist?
domains = vocab.domain_candidates.keys()
print(f"Available domains: {domains}")

# If missing: content may not compress well
# Consider contributing to vocabulary or using web feedback

Issue: Important Terms Not Recognized

Symptom: Specific jargon or terminology not mapping correctly

Solution: Use explicit field names that match EXTRACT_FIELDS

# Instead of domain jargon
"Get the customer NPS score"

# Use vocabulary terms
"Extract the satisfaction rating"  # Maps to RATINGS

Issue: Over-Compression Losing Meaning

Symptom: Compressed output lacks necessary detail

Solution: Vocabulary prioritizes semantic preservation

# The vocabulary is designed to preserve meaning
# If compression seems too aggressive, it may be working correctly
# Test with LLM to verify understanding

Next Steps

• CLM Configuration - Using vocabularies via config
• Token Hierarchy - Understanding token structure
• System Prompt Encoder - Vocabulary in system prompts
• Transcript Encoder - Vocabulary in transcripts