Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic
  Architectures
Despite their capabilities, Large Language Models (LLMs) remain opaque with
limited understanding of their internal representations. Current
interpretability methods, such as direct logit attribution (DLA) and sparse
autoencoders (SAEs), provide restricted insight due to limitations such as the
model's output vocabulary or unclear feature names. This work introduces
Hyperdimensional Probe, a novel paradigm for decoding information from the LLM
vector space. It combines ideas from symbolic representations and neural
probing to project the model's residual stream into interpretable concepts via
Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs
and conventional probes while overcoming their key limitations. We validate our
decoding paradigm with controlled input-completion tasks, probing the model's
final state before next-token prediction on inputs spanning syntactic pattern
recognition, key-value associations, and abstract inference. We further assess
it in a question-answering setting, examining the state of the model both
before and after text generation. Our experiments show that our probe reliably
extracts meaningful concepts across varied LLMs, embedding sizes, and input
domains, also helping identify LLM failures. Our work advances information
decoding in LLM vector space, enabling extracting more informative,
interpretable, and structured features from neural representations.
68de2c31a9129e507bf4a43d
Marco Bronzini
claimed_verified
2025-10-02T13:54:27.735Z
642585e7120a3ed32332e7ff
/avatars/cbc8ce70e090a3e3243b0e42fa00b8a4.svg
Marco Bronzini
user
saturnMars
68de2c31a9129e507bf4a43e
Carlo Nicolini
68de2c31a9129e507bf4a43f
Bruno Lepri
68de2c31a9129e507bf4a440
Jacopo Staiano
68de2c31a9129e507bf4a441
Andrea Passerini
Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic
  Architectures
Despite their capabilities, Large Language Models (LLMs) remain opaque with
limited understanding of their internal representations. Current
interpretability methods, such as direct logit attribution (DLA) and sparse
autoencoders (SAEs), provide restricted insight due to limitations such as the
model's output vocabulary or unclear feature names. This work introduces
Hyperdimensional Probe, a novel paradigm for decoding information from the LLM
vector space. It combines ideas from symbolic representations and neural
probing to project the model's residual stream into interpretable concepts via
Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs
and conventional probes while overcoming their key limitations. We validate our
decoding paradigm with controlled input-completion tasks, probing the model's
final state before next-token prediction on inputs spanning syntactic pattern
recognition, key-value associations, and abstract inference. We further assess
it in a question-answering setting, examining the state of the model both
before and after text generation. Our experiments show that our probe reliably
extracts meaningful concepts across varied LLMs, embedding sizes, and input
domains, also helping identify LLM failures. Our work advances information
decoding in LLM vector space, enabling extracting more informative,
interpretable, and structured features from neural representations.
A novel Hyperdimensional Probe method decodes information from LLM vector spaces using Vector Symbolic Architectures, providing interpretable insights into model states and failures.
Large Language Models Hyperdimensional Probe Vector Symbolic Architectures direct logit attribution sparse autoencoders residual stream next-token prediction syntactic pattern recognition key-value associations abstract inference question-answering neural probing symbolic representations