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Model Core Primitives

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The document outlines various core components of a model, categorized into four main areas: language understanding, knowledge operations, language generation, reasoning and decision-making, and meta-management. 1. **Language Understanding (38 components)**: This includes tools for tokenization, dependency parsing, entity recognition, semantic role labeling, and more, aimed at comprehending and processing natural language. 2. **Knowledge Operations (29 components)**: These components facilitate knowledge retrieval, reasoning about relationships and attributes, handling temporal and spatial reasoning, and integrating knowledge from various sources. 3. **Language Generation (32 components)**: This section focuses on generating coherent and contextually appropriate text, including information selection, structural planning, emotional tone adjustment, and ensuring clarity and conciseness. 4. **Reasoning and Decision-Making (18 components)**: It includes mechanisms for rule-based reasoning, probabilistic inference, causal reasoning, and ethical decision-making frameworks. 5. **Meta-Management (18 components)**: This involves managing system-level operations such as attention focus, harmful content detection, resource allocation, and ensuring the reliability and transparency of outputs. Overall, the document presents a comprehensive framework for developing advanced language processing and reasoning systems.

Core Model Primitives - Author: Wang Jiao Cheng#

Language Understanding Primitives (38 items)#

  1. Token Locator: Converts text into a sequence of morphemes (handles Chinese without spaces / English roots)
  2. Subword Segmenter: Handles out-of-vocabulary words (e.g., "ChatGPT"→["Chat","G","PT"])
  3. Dependency Parser: Constructs subject-verb-object syntax trees
  4. Phrase Structure Parser: Identifies noun phrase / verb phrase boundaries
  5. Syntactic Role Tagger: Tags subjects, verbs, objects, modifiers, and complements
  6. Entity Recognizer: Identifies person names / place names / organization names
  7. Anaphora Resolver: Resolves what "it" / "they" refers to
  8. Semantic Role Tagger: Tags agents, patients, time, and location
  9. Concept Linker: Links words to knowledge graph nodes
  10. Dialogue State Tracker: Maintains focus in multi-turn conversations
  11. Co-reference Chain Builder: Associates the same entity across sentences
  12. Implicit Reasoner: Fills in logical gaps (e.g., "It's raining"→"Need to bring an umbrella")
  13. Action Classifier: Identifies requests / statements / questions / commands
  14. Sentiment Polarity Detector: Quantifies positive / negative emotions
  15. Context-Aware Fusion Engine: Integrates multi-source contextual information
  16. Ambiguity Resolver: Addresses polysemy issues (e.g., "apple"→fruit/company)
  17. Temporal Anchor: Converts relative time to absolute time (e.g., "next week"→specific date)
  18. Metaphor Parser: Understands the true meaning of metaphorical expressions
  19. Irony Detector: Identifies ironic intent in discourse
  20. Fuzzy Modifier Parser: Handles uncertain expressions like "probably" / "possibly"
  21. Topic Boundary Detector: Determines points of topic shifts in conversation
  22. Intent Priority Evaluator: Ranks the importance of multiple requests
  23. Multilingual Aligner: Handles mixed language input
  24. Spoken Language Feature Processor: Adapts to incomplete expressions in spoken language
  25. Document Structure Parser: Identifies formatting information like titles / paragraphs / lists
  26. Formatting Error Corrector: Automatically fixes spelling / grammar errors
  27. Domain Terminology Recognizer: Locates specialized vocabulary in professional fields
  28. Dialect Adapter: Understands language variations from different regions
  29. Cultural Reference Parser: Handles expressions with specific cultural backgrounds
  30. Emotion Intensity Quantifier: Assesses the degree of joy, anger, sadness, and happiness
  31. Speech-to-Text Post-Processor: Optimizes the text quality of ASR output
  32. Non-Verbal Symbol Interpreter: Understands the implied meanings of emojis / punctuation
  33. Implicit Premise Detector: Reveals unspoken assumptions
  34. Negation Scope Analyzer: Determines the scope of negation
  35. Question Type Classifier: Distinguishes between yes/no questions and special inquiries
  36. Command Strength Evaluator: Quantifies the degree of forcefulness in instructions
  37. Stylistic Style Recognizer: Analyzes formal / casual / poetic styles
  38. Cross-Modal Aligner: Coordinates the correspondence between text and images / speech

Knowledge Operation Primitives (29 items)#

  1. Knowledge Retriever: Extracts facts from 175 billion parameters
  2. Relation Reasoner: Infers implicit relationships (A is B's teacher → B is A's student)
  3. Attribute Filler: Completes object attributes (known capital → check population)
  4. Temporal Reasoner: Handles time relationships (e.g., "last March"→2023-03)
  5. Spatial Reasoner: Handles positional relationships (e.g., "A is north of B"→coordinate calculation)
  6. Numerical Estimator: Handles vague numbers (e.g., "many"→probability distribution)
  7. Concept Classifier: Constructs classification trees (apple → fruit → plant)
  8. Counterfactual Simulator: Handles hypothetical scenarios (e.g., "if electricity had not been invented")
  9. Knowledge Conflict Resolver: Resolves contradictory information (conflicting data from different sources)
  10. Ontology Mapper: Connects concepts from different knowledge systems
  11. Common Sense Reasoner: Logic inference based on everyday experiences
  12. Event Chain Builder: Establishes causal / temporal relationship networks
  13. Analogy Engine: Transfers knowledge between similar scenarios
  14. Knowledge Completeness Checker: Identifies information gaps
  15. Cross-Domain Transferrer: Applies knowledge from domain A to solve problems in domain B
  16. Probability Fact Updater: Adjusts belief levels based on new evidence
  17. Complex System Modeler: Analyzes interactions among multiple factors
  18. Constraint Propagator: Infers constraints within a rule network
  19. Pattern Extender: Derives general rules from specific cases
  20. Knowledge Fusion Engine: Merges information from multiple sources
  21. Concept Refinement Engine: Transforms vague descriptions into precise definitions
  22. Cognitive Bias Detector: Identifies unreasonable premises
  23. Knowledge Reliability Evaluator: Assigns weights to different sources
  24. Trend Extrapolator: Predicts the future based on historical data
  25. Scenario Simulator: Constructs complete event scenarios
  26. Abstraction Level Selector: Dynamically adjusts the granularity of knowledge
  27. Knowledge Distiller: Extracts core information from complex data
  28. Multimodal Knowledge Integrator: Coordinates representations of text / images / data
  29. Knowledge Version Tracker: Records the timeliness of information

Language Generation Primitives (32 items)#

  1. Information Selector: Filters relevant knowledge points
  2. Structure Planner: Decides overall-subdivision / problem-solution structures
  3. Anaphora Expression Optimizer: Avoids repetitive nouns (using pronouns / synonyms)
  4. Connector Word Selector: Accurately uses "because" / "but" / "and" etc.
  5. Tense Consistency Engine: Maintains tense uniformity throughout the text
  6. Quantity Expression Optimizer: Handles singular/plural / quantifiers (e.g., "three apples")
  7. Formality Regulator: Controls the degree of spoken / written language
  8. Domain Terminology Adapter: Switches between medical / legal / technical terms
  9. Cultural Sensitivity Filter: Avoids culturally taboo expressions
  10. Logic Validator: Checks the rationality of causal relationships
  11. Fact Consistency Checker: Ensures generation is consistent with the knowledge base
  12. Emotion Infuser: Injects appropriate emotional tones
  13. Audience Adapter: Adjusts expressions based on user background
  14. Rhetorical Optimizer: Enhances the expressiveness of communication
  15. Redundancy Eliminator: Removes unnecessary repetitions
  16. Ambiguity Preventer: Avoids potentially misleading expressions
  17. Information Density Controller: Balances detail with conciseness
  18. Dialogue Strategy Selector: Decides on providing / asking / guiding strategies
  19. Multilingual Generator: Simultaneously processes outputs in multiple languages
  20. Multimedia Coordinator: Generates image descriptions alongside text
  21. Error Recovery Generator: Handles graceful responses to unknown queries
  22. Explanation Depth Selector: Dynamically adjusts the detail of explanations
  23. Counterfactual Describer: Accurately describes hypothetical scenarios
  24. Stance Expressor: Appropriately expresses support / opposition
  25. Vagueness Controller: Handles expressions of uncertainty
  26. Meta-Communication Generator: Explains its own thought processes
  27. Ethical Trade-off Describer: Shows the pros and cons of different choices
  28. Formatting Normalizer: Adapts paragraphs / lists / titles for formatting
  29. Context Connector: Links current and previous conversations
  30. Instant Corrector: Dynamically optimizes the content being generated
  31. Safety Boundary Controller: Avoids expressions suggesting dangerous advice
  32. Generation Diversity Selector: Adjusts the degree of creative expression

Reasoning and Decision-Making Primitives (18 items)#

  1. Rule Engine: Executes if-then-else hard rules
  2. Analogy Reasoner: A :: C:? pattern matching
  3. Probability Reasoner: Calculates probability distributions of multiple options
  4. Optimization Selector: Multi-objective weight decision (speed vs accuracy)
  5. Causal Reasoner: Infers causes from phenomena
  6. Causal Graph Builder: Constructs causal relationship networks among variables
  7. Constraint Solver: Solves problems with constraints (e.g., scheduling)
  8. Moral Trade-off Framework: Evaluates the ethical impact of decisions
  9. Cost-Benefit Analyzer: Quantifies the value-to-cost ratio of decisions
  10. Risk Predictor: Assesses potential adverse outcomes of decisions
  11. Alternative Solution Generator: Creates Plan B options
  12. Counter-Evidence Engine: Searches for evidence against hypotheses
  13. Systems Thinking Model: Considers second-order / third-order effects
  14. Bias Detector: Identifies subjective tendencies in decision-making
  15. Time Sensitivity Evaluator: Balances response speed with quality
  16. Resource Optimizer: Allocates computational resources efficiently
  17. Knowledge Gap Identifier: Recognizes information gaps that need to be avoided
  18. Feasibility Evaluator: Checks the operational feasibility of plans

Meta-Management Primitives (System-Level 18 items)#

  1. Attention Focus Enhancer: Increases the weight of key areas
  2. Attention Suppressor: Decreases the weight of noisy areas
  3. Harmful Content Detector: Identifies violent / biased / illegal content
  4. Hallucination Suppressor: Reduces the probability of fabricating facts
  5. Computational Budget Allocator: Dynamically allocates GPU memory
  6. Early Termination Predictor: Ends low-confidence branches early
  7. Decision Attribution Analyzer: Marks key input words that influence output
  8. Confidence Calibrator: Quantifies output reliability scores
  9. Contradiction Monitor: Detects logical conflicts between input and output
  10. Knowledge Timeliness Validator: Checks the recency of information
  11. Thought Chain Optimizer: Balances reasoning depth with efficiency
  12. Fairness Auditor: Checks for processing disparities among different groups
  13. Transparency Controller: Manages the exposure level of explanatory details
  14. Resource Reclaimer: Timely releases inactive memory
  15. Capability Boundary Marker: Identifies situations that exceed knowledge boundaries
  16. Robustness Enhancer: Handles noisy inputs
  17. Version Coordinator: Ensures compatibility of behavior after updates
  18. Performance-Quality Trade-off Controller: Dynamically balances response speed and accuracy
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