Ipotesi della Rappresentazione del Frame: Interpretabilità del Modello Linguistico a Lungo Termine con Token Multipli e Generazione di Testo Guidata dal Concetto

Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation

December 10, 2024
Autori: Pedro H. V. Valois, Lincon S. Souza, Erica K. Shimomoto, Kazuhiro Fukui
cs.AI

Abstract

L'interpretabilità è una sfida chiave nel favorire la fiducia nei Large Language Models (LLM), che deriva dalla complessità dell'estrazione del ragionamento dai parametri del modello. Presentiamo l'Ipotesi della Rappresentazione a Cornice, un quadro teoricamente robusto basato sull'Ipotesi della Rappresentazione Lineare (LRH) per interpretare e controllare i LLM modellando parole multi-token. Ricerche precedenti hanno esplorato la LRH per collegare le rappresentazioni dei LLM a concetti linguistici, ma erano limitate all'analisi di singoli token. Poiché la maggior parte delle parole è composta da diversi token, estendiamo la LRH a parole multi-token, consentendo così l'uso su qualsiasi dato testuale con migliaia di concetti. A tal fine, proponiamo che le parole possano essere interpretate come cornici, sequenze ordinate di vettori che catturano meglio le relazioni tra token e parole. Successivamente, i concetti possono essere rappresentati come la media delle cornici delle parole che condividono un concetto comune. Mostriamo questi strumenti attraverso la Decodifica Guidata da Concetto Top-k, che può guidare in modo intuitivo la generazione di testo utilizzando i concetti scelti. Verifichiamo tali idee sui modelli Llama 3.1, Gemma 2 e Phi 3, dimostrando pregiudizi di genere e linguistici, esponendo contenuti dannosi, ma anche il potenziale per rimediare ad essi, portando a LLM più sicuri e trasparenti. Il codice è disponibile su https://github.com/phvv-me/frame-representation-hypothesis.git
English
Interpretability is a key challenge in fostering trust for Large Language Models (LLMs), which stems from the complexity of extracting reasoning from model's parameters. We present the Frame Representation Hypothesis, a theoretically robust framework grounded in the Linear Representation Hypothesis (LRH) to interpret and control LLMs by modeling multi-token words. Prior research explored LRH to connect LLM representations with linguistic concepts, but was limited to single token analysis. As most words are composed of several tokens, we extend LRH to multi-token words, thereby enabling usage on any textual data with thousands of concepts. To this end, we propose words can be interpreted as frames, ordered sequences of vectors that better capture token-word relationships. Then, concepts can be represented as the average of word frames sharing a common concept. We showcase these tools through Top-k Concept-Guided Decoding, which can intuitively steer text generation using concepts of choice. We verify said ideas on Llama 3.1, Gemma 2, and Phi 3 families, demonstrating gender and language biases, exposing harmful content, but also potential to remediate them, leading to safer and more transparent LLMs. Code is available at https://github.com/phvv-me/frame-representation-hypothesis.git

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