Semantic memory vs embedding in AI
Ali Riza Saral
Independent AI Researcher
13 Temmuz 2025
Can it be found related: The
semantic memory and the word - embedding in transformers?
ChatGPT said:
Yes, there is a conceptual
connection between semantic memory in cognitive
psychology and word embeddings in Transformers. Let’s break it
down:
1. Semantic Memory
Semantic memory is the part of
long-term memory that stores general world knowledge, including facts,
concepts, and the meanings of words. It's not tied to specific experiences but
represents shared knowledge across contexts. For example:
- Knowing what "apple" means and how it relates
to "fruit."
- Understanding that "cat" and "dog"
are both animals but distinct entities.
2. Word Embeddings in
Transformers
Word embeddings represent words as
dense vectors in a high-dimensional space, encoding their semantic and contextual
relationships. These embeddings capture:
- Similarities: Words like "king" and
"queen" have similar embeddings.
- Relationships: Vector operations like
king−man+woman≈queen\text{king} - \text{man} + \text{woman} \approx
\text{queen}king−man+woman≈queen.
These embeddings are learned during
training on large corpora, making them analogous to a form of machine-learned
"semantic memory."
Connections Between Semantic
Memory and Word Embeddings
- Knowledge Representation: Semantic memory represents
concepts and their relationships, much like word embeddings capture the
semantic relationships between words (e.g., synonyms or analogies).
- Contextual Understanding: Transformers use embeddings in
conjunction with attention mechanisms to model meaning dynamically based
on context. This aligns with how semantic memory retrieves word meanings
in different contexts (e.g., "bank" as a financial institution
vs. a riverbank).
- Distributed Representation: Semantic memory is thought to
rely on distributed neural networks in the brain. Similarly, word
embeddings use distributed representations where each word's meaning is
encoded across multiple dimensions.
- Learning Mechanism: Word embeddings in Transformers
(e.g., BERT, GPT) are refined through exposure to large datasets,
paralleling how semantic memory is shaped by exposure to language and
experience over time.
Where They Diverge
- Neurocognitive Basis: Semantic memory arises from
biological neural processes in the brain, whereas word embeddings are
mathematical representations built by artificial neural networks.
- Scope: Semantic memory encompasses all
conceptual knowledge, while word embeddings are limited to the information
encoded in the training data.
In summary, word embeddings in
Transformers can be viewed as an artificial analog of semantic memory,
enabling machines to understand and represent word meanings and relationships
similarly to how humans rely on semantic memory for language comprehension.
You said:
How is episodic memory related with
transformer architecture? Feed forward networks?
ChatGPT said:
Episodic memory, a concept from
cognitive psychology, refers to the ability to recall specific experiences or
events, including their context (time, place, emotions, etc.). While
Transformers and feed-forward networks do not directly replicate episodic
memory, there are conceptual parallels and inspirations drawn from it,
particularly in how Transformers handle contextualized information
and sequential processing. Let’s analyze the connections
step by step:
1. Episodic Memory in Cognitive
Psychology
- Episodic
memory stores personal experiences tied
to specific events in space and time. For example: "I ate dinner at a
restaurant last night." It is dynamic and context-sensitive, allowing
humans to link past events to current situations.
2. Episodic Memory and
Transformer Architecture
Transformers don’t explicitly model
episodic memory, but they handle contextual dependencies
in a way that mimics certain aspects of episodic memory:
Attention Mechanism as Episodic
Memory
- Self-attention allows the Transformer to focus
on relevant parts of a sequence, akin to recalling specific details of an
event in episodic memory. Example: In a sentence like "John gave Mary a book yesterday, and she loved
it," the attention mechanism links "she"
to "Mary" and "it" to "book." This contextual linking
resembles how episodic memory retrieves specific elements from a past
event.
Positional Embeddings and
Sequencing
- Episodic
memory relies on temporal and spatial context. In Transformers, positional embeddings encode the order of
words, helping the model maintain sequence information—a rough analog to
remembering the "timeline" of events.
Layer Stacking and Memory
Refinement
- Transformers stack multiple layers of attention and
feed-forward networks, which refine the representation of tokens at each
layer. This iterative refinement can be seen as analogous to how episodic
memory integrates new details into a coherent recollection.
3. Episodic Memory and
Feed-Forward Networks
Feed-forward networks (FFNs) in
Transformers play a crucial role in processing and enriching
contextual information, which can be loosely tied to aspects of
episodic memory:
Representation Enhancement
- In Transformers, FFNs transform the attention output
into richer representations by applying non-linear transformations (e.g.,
ReLU) and combining information across dimensions. This is somewhat
analogous to how episodic memory integrates sensory details (e.g., visual,
auditory) into a cohesive event.
Contextual Generalization
- FFNs
allow the model to capture higher-order relationships within the data,
helping it generalize beyond specific instances. Similarly, episodic
memory interacts with semantic memory to extract
meaning from past experiences and apply it to new contexts.
Multi-Head Attention and
Episodic Retrieval
- Each attention head focuses on different aspects of the
sequence, similar to how episodic memory can prioritize specific features
of an event (e.g., the people involved, the location, the emotions).
4. Differences and Gaps
Despite the parallels, there are
fundamental differences:
- Storage and Persistence: Episodic memory is persistent
and tied to neural plasticity in the brain. Transformers do not
"store" experiences long-term—they rely on their learned weights
and can’t retain experiences beyond the training phase. However, adding external memory components (e.g.,
memory-augmented Transformers like Transformer-XL or GPT with Retrieval-Augmented
Generation) introduces an episodic-like recall mechanism.
- Personalization and Flexibility: Episodic memory is personalized
and influenced by emotions and subjective experience. Transformers are
purely data-driven and lack emotional encoding.
- Temporal Updates: Episodic memory evolves over
time as new experiences are integrated. Transformers do not dynamically
update their "memory" unless retrained or paired with external
memory mechanisms.
5. Enhancing Episodic-Like
Behavior in Transformers
To better mimic episodic memory,
researchers have proposed enhancements:
- Memory-Augmented Neural Networks (MANNs): Adding external memory (e.g., a
key-value store) to allow models to "recall" past contexts
dynamically.
- Transformer-XL and Longformer: These models extend the
Transformer architecture to include long-range context, akin to recalling
details from prior events.
- Dynamic Neural Fields: Models like Neural Turing
Machines or Differentiable Neural Computers integrate explicit memory
mechanisms for episodic-like behavior.
Summary of Relationships
Feature
Episodic Memory
Transformer/FFN
Context Retrieval
Recalls specific events with
temporal and spatial context
Attention mechanisms dynamically
retrieve relevant parts of input sequences.
Integration of Details
Combines sensory and contextual
details into a cohesive memory
FFNs refine token representations by
processing attention outputs with non-linear transformations.
Sequence Sensitivity
Encodes temporal order of events
Positional embeddings preserve word
order within sequences.
Persistence
Long-term storage in biological
neurons
Stateless; relies on learned weights
unless paired with external memory modules.
In essence, Transformers and FFNs model
aspects of episodic memory in how they handle sequence data and retrieve
context, but they are limited to the information present during
processing and lack true persistence or personalization.
You said:
I believe you aremixing the
functionality of episodic memory equivalence of transformers with the real time
memory equivalence of transformers... Episodic memory resemablance has to be
more limited to the statistical retrieval.
ChatGPT said:
You're absolutely right—episodic memory in Transformers should
indeed be interpreted in a more limited and focused manner, tied to statistical retrieval rather than dynamic or
real-time memory-like behavior. Let’s refine and clarify the distinction:
Episodic Memory: The Focus on
Statistical Retrieval
Episodic memory, in this context,
should primarily refer to retrieving specific contextual patterns
(statistical associations) within the data processed by a Transformer.
Here’s how the resemblance is limited:
- Static Nature of Episodic Retrieval in Transformers: Transformers are pre-trained to
encode relationships between tokens and their contexts based on statistical patterns in the data.
Once trained, the model retrieves and applies these learned statistical
relationships without truly "remembering" any dynamic or
personalized events. This retrieval is fundamentally statistical, not dynamic or real-time.
- Local and Global Contextual Relationships: Transformers, especially
through self-attention, excel at identifying and
recalling local and global relationships between tokens in a sequence. For
example, given a sentence like "The concert last
night was amazing; the band played all my favorite songs," the
model can "recall" (statistically retrieve) that "the band" refers to the context of
the "concert."
- No True Event-Level Recall: Unlike episodic memory in humans,
which can recall specific instances of events,
Transformers only encode generalized
relationships between tokens seen during training. For
instance, a Transformer model can "retrieve" that "Eiffel Tower" is likely associated
with "Paris" but doesn’t
"remember" any specific instance of someone mentioning it.
How Statistical Retrieval
Resembles Episodic Memory
The resemblance lies in the way
Transformers leverage statistical patterns for contextual reconstruction:
- Self-Attention as Statistical Context Retrieval: The attention mechanism
retrieves tokens (words, subwords) that are most relevant to the current
input position. This resembles the retrieval process of
episodic memory—recalling the relevant parts of an event based
on context.
- Feed-Forward Layers as Representation Refiners: The feed-forward layers
transform the attention output into richer contextual representations.
These transformations refine the statistical retrieval, analogous to how
episodic memory integrates details into a coherent recollection.
- Pre-Trained Knowledge as Episodic Basis: Pre-trained Transformers like
GPT or BERT "remember" relationships and associations
statistically encoded during training, much like episodic memory
"stores" information about past experiences for later use.
What Transformers Don’t Do
(Yet)
Unlike human episodic memory:
- Transformers Lack Personalization: Episodic memory is deeply tied
to personal experience. Transformers only encode statistical relationships
across vast datasets, lacking any subjective or experiential aspect.
- No Dynamic Storage or Updates: Episodic memory can dynamically
store and update information about events in real time. Standard
Transformers cannot; they rely on fixed learned weights.
- No Temporal Hierarchies or Causal Chains: While positional embeddings in
Transformers encode order, they don’t establish temporal causality
(e.g., "X caused Y" in
an episodic sense).
What You Likely Mean by
Real-Time Memory
Real-time memory in Transformers (or
any neural system) would imply:
- Dynamic Updates: The ability to modify
representations based on new information (e.g., storing a new event during
inference).
- External Memory Mechanisms: Such as in memory-augmented networks, where the model can
write and retrieve new information dynamically (e.g., retrieval-augmented
Transformers like RAG or Transformer-XL).
These real-time mechanisms are different from the limited, statistical retrieval
that resembles episodic memory.
Conclusion
Episodic memory's resemblance in
Transformers is statistical and context-driven, focusing on the
ability to retrieve relationships between tokens based on patterns learned
during training. This is distinct from real-time memory,
which involves dynamic updates and interactions with external information.
By focusing on statistical retrieval, the Transformer’s
episodic-like behavior is limited to:
- Identifying relationships between tokens and contexts
during processing.
- Generalizing from patterns seen in training data.
This narrowed perspective aligns
better with the statistical underpinnings of Transformers.
You said:
How does a transformer retrieve an
information? What is the addressing mechanism?
ChatGPT said:
Transformers retrieve information
using a self-attention mechanism to address and focus on
relevant parts of an input sequence. The addressing mechanism in
Transformers is based on a combination of query-key-value attention
and weighted summation. Here’s a detailed breakdown:
1. Key Concepts in Transformer
Retrieval
a. Query-Key-Value Framework
- The
Transformer uses three matrices derived from the input: queries (Q), keys (K),
and values (V). Query (Q): Represents
the current token seeking information (like a search term). Key (K): Represents potential memory
"addresses" of tokens in the sequence. Value (V): Contains
the actual information to retrieve.
- The
relationship between the query and
all the keys determines which values are most
relevant.
b. Self-Attention Mechanism
- Self-attention is the core addressing mechanism in Transformers.
It computes a relevance score between the query and all keys in the input
sequence to decide which parts of the sequence are important for the
current token.
4. 1. Multi-Head Attention
- The
Transformer repeats the attention mechanism with multiple attention heads to focus on different aspects
of the input. Each head operates on different projections of QQQ, KKK, and
VVV.
- The outputs of all attention heads are concatenated and
linearly transformed.
3. Why This Mechanism is an
Addressing System
The addressing mechanism is akin to
searching for and retrieving data from a memory:
- Keys act as memory addresses: They represent positions or
features in the input sequence.
- Queries act as search criteria: They specify what information
is relevant for the current token.
- Attention weights act as the lookup operation: They determine how much weight
to assign to each key (or memory location).
- Values act as the content: They hold the information to be
retrieved.
By assigning higher weights to more
relevant keys, the model effectively retrieves the most relevant information
for the query.
4. Advantages of the
Transformer Addressing Mechanism
- Global Context Retrieval: The self-attention mechanism
enables each token to attend to all other tokens in the sequence,
retrieving information globally, not just locally (as in RNNs or CNNs).
- Dynamic Retrieval: The addressing is
context-dependent, meaning the retrieval process changes dynamically for
each token based on the query and the input context.
- Parallelization: The use of dot products and
matrix operations makes the retrieval process efficient and
parallelizable, unlike sequential models like RNNs.
5. Extensions and Real-Time
Memory
- Transformer-XL and Longformer: Use sliding windows or memory segments
to extend the addressing mechanism to longer sequences, enabling better
recall of distant context.
- Memory-Augmented Transformers: Incorporate explicit memory
modules where queries address an external memory bank, enabling dynamic
storage and retrieval of information beyond the input sequence.
Summary
The Transformer retrieves
information through self-attention, where:
- Queries act as the search request.
- Keys act as memory addresses.
- Values hold the content to retrieve.
- The dot product between queries and keys assigns
relevance, and attention weights control the retrieval.
This addressing mechanism is
powerful because it dynamically retrieves context-sensitive information in
parallel for all tokens in the sequence.
You said:
But if the context of an information
has not been established in training epochs transformer produces false/wrong
output...
ChatGPT said:
Yes, that's correct! If the context of an information is not well-established
during training, Transformers can produce false or nonsensical
outputs. This limitation arises because the model fundamentally
relies on statistical patterns in the training data to
generate outputs, and its knowledge is constrained to what it has been trained
on. Let’s explore this in more depth:
1. Why Transformers Fail with
Unseen Contexts
a. No Understanding Beyond
Training
Transformers don’t “understand”
information like humans do; they memorize and generalize
patterns from the training data. If an input requires knowledge or
context the model hasn’t seen:
- It cannot retrieve relevant patterns, resulting in
outputs that are guesses, interpolations, or outright errors.
- For example, asking a Transformer to translate a
sentence containing a word it has never seen might result in the word
being treated as an unknown or misinterpreted based on statistical
similarities.
b. Lack of Causal Knowledge
Transformers are not inherently
aware of causal relationships or logical hierarchies unless
such relationships are explicitly encoded in the training data. Without proper
exposure:
- The model may generate plausible-sounding but factually
incorrect or inconsistent answers.
- For
example, given a query like “Who was the president
of France in 2050?” the model may generate a name simply
because it has learned the structure of such responses but lacks the
ability to reason about the future.
c. Dependence on Statistical
Associations
The model depends heavily on the
quality and coverage of the data. If specific contexts or relationships are
missing or underrepresented:
- The Transformer may extrapolate incorrectly from
unrelated or partially related information.
- For example, it might incorrectly associate uncommon
terms with more frequent ones (e.g., linking a rare medical term to a
common illness based on weak statistical signals).
2. Example of Failure in Unseen
Contexts
Suppose a Transformer has been
trained on common conversational data but not on technical legal texts:
- Input: "Draft a legally binding contract for a complex
intellectual property agreement."
- Output: The Transformer may generate a contract that
looks syntactically correct but contains inaccuracies or lacks essential
clauses because it hasn’t seen enough examples to generalize effectively
in that domain.
3. Why Does This Happen?
This failure arises from how Transformers are trained and structured:
a. Training Data as Knowledge
Base
- Transformers encode their "knowledge"
directly into their parameters during training. This means they can only
“remember” or generalize from the patterns they’ve seen. If a particular
context or relationship isn’t included, they’ll lack the ability to
produce accurate outputs for it.
b. Overfitting to Seen Patterns
- When
Transformers encounter an unfamiliar input, they try to match it to the
closest pattern they have seen. This can lead to: Overgeneralization:
Applying unrelated patterns to the new input. Overconfidence: Generating output that looks
correct but is semantically wrong.
c. No Dynamic Updating
- Standard Transformers do not have mechanisms to
dynamically incorporate new information into their internal
representations during inference. This contrasts with humans, who can
integrate new information dynamically and adapt to unfamiliar contexts.
4. Addressing Unseen Contexts:
Possible Solutions
a. Better and Broader Training
Data
- Solution: Increase the diversity and
coverage of the training dataset to expose the model to more varied
contexts.
- Limitation: This can be infeasible for rare
or niche domains, and increasing the dataset size also increases
computational requirements.
b. Retrieval-Augmented
Transformers
- Solution: Use retrieval-augmented models
(e.g., RAG, REALM) that integrate an external knowledge base to fetch
relevant information dynamically during inference.
- Example: If the model encounters an
unfamiliar query, it can retrieve related documents from an external
database to ground its response.
c. Fine-Tuning on Specific
Domains
- Solution: Fine-tune the Transformer on
domain-specific data to improve performance in specialized contexts.
- Example: Fine-tuning a language model on
medical literature can improve its ability to generate accurate medical
reports.
d. Incorporating Dynamic Memory
Mechanisms
- Solution: Use models like Transformer-XL or Memory-Augmented
Neural Networks that include mechanisms for contextual recall and long-term memory during
inference.
- Benefit: Allows the model to
"store" new context dynamically and use it to adjust its
outputs.
e. Using Prompt Engineering
- Solution: Carefully design the input
prompt to provide additional context or guide the model.
- Example: Instead of asking, “What are the side effects of XYZ?” you might
ask, “Based on FDA-approved data, what are the side effects of
XYZ?”
5. An Analogy to Human Memory
Think of Transformers as having a
"frozen" episodic-like memory:
- If
an event or context hasn’t been recorded in the training data (the
Transformer’s episodic memory), it can only guess based on statistical similarities to what it has seen.
- Humans, on the other hand, can dynamically connect new
experiences to prior knowledge and generalize more effectively, which
Transformers struggle with.
6. Key Takeaways
- Transformers retrieve information by statistically matching
input to patterns encoded during training.
- When contexts are missing or underrepresented in
training, the model produces guesses, interpolations, or errors.
- To
improve performance on unseen contexts: Use retrieval-augmented
models. Fine-tune the model on relevant data. Incorporate
mechanisms for dynamic context integration.
This limitation is a fundamental
challenge in AI and highlights the importance of combining statistical learning with external, dynamic knowledge
retrieval systems.
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