Meaning Representations from Trajectories in Autoregressive Models (2310.18348v3)

Abstract: We propose to extract meaning representations from autoregressive LLMs by considering the distribution of all possible trajectories extending an input text. This strategy is prompt-free, does not require fine-tuning, and is applicable to any pre-trained autoregressive model. Moreover, unlike vector-based representations, distribution-based representations can also model asymmetric relations (e.g., direction of logical entailment, hypernym/hyponym relations) by using algebraic operations between likelihood functions. These ideas are grounded in distributional perspectives on semantics and are connected to standard constructions in automata theory, but to our knowledge they have not been applied to modern LLMs. We empirically show that the representations obtained from large models align well with human annotations, outperform other zero-shot and prompt-free methods on semantic similarity tasks, and can be used to solve more complex entailment and containment tasks that standard embeddings cannot handle. Finally, we extend our method to represent data from different modalities (e.g., image and text) using multimodal autoregressive models. Our code is available at: https://github.com/tianyu139/meaning-as-trajectories

An Analysis of Meaning Representations from Trajectories in Autoregressive Models

The paper "Meaning Representations from Trajectories in Autoregressive Models" explores an innovative approach to semantic interpretation within autoregressive LLMs. The researchers propose a novel framework for representing the meaning of prompts by considering the distribution of all potential continuations (or trajectories) of an input text. This proposal departs from traditional vector-based methodologies and offers distinct advantages, notably in modeling asymmetric linguistic relationships such as logical entailment and hypernym/hyponym correspondences.

Methodology and Core Contributions

The authors detail a mechanism in which a sentence is represented not by a fixed vector but by the probability distribution of its possible continuations as predicted by a pre-trained LLM. This strategy, described as prompt-free and fine-tune independent, leverages algebraic operations among likelihood functions to enable a nuanced understanding of meaning that incorporates directional semantic relationships. The concept aligns with distributional semantics theories, suggesting that meaning is intrinsically tied to usage statistics, and draws inspiration from formal language and automata theory.

Several empirical results highlight the efficacy of this approach. The paper demonstrates that these distributional representations, when compared across prominent autoregressive models such as GPT-2, Falcon, and LLaMA, align well with human linguistic judgments on semantic tasks. The authors report superior performance on zero-shot and prompt-free semantic similarity assessments relative to conventional methods like BERT-based embeddings. Furthermore, this methodology successfully handles entailment and containment tasks unattainable through conventional embeddings.

Theoretical and Practical Implications

From a theoretical standpoint, this research offers a fresh perspective on how meaning can be extracted from autoregressive models, challenging the prevalent notion that fixed vector representations suffice for capturing semantics.

Practically, the paper argues for the method’s versatility across different data modalities, extending its application potential to image and text datasets within multimodal autoregressive models. This capability to seamlessly integrate across varied data forms is exemplified by its superior performance on the Crisscrossed Captions dataset, surpassing CLIP embeddings in tasks involving semantic image and text comparisons.

Future Developments

The implications of this work are substantial, suggesting new paths for refining human-computer interaction paradigms, enhancing AI systems' interpretability, and developing richer multimodal interaction models. Future avenues may explore how prompt-based and distribution-based representations can be synergized, perform comprehensive benchmarks on various linguistic structures, and enhance computational efficiency, especially in large-scale data environments.

This paper not only contributes valuable insights into modeling and understanding language with autoregressive models but also sets a foundation for more extensive applications in cognitive computing and cross-modal semantic representation. As AI continues to burgeon as a transformative technology, understanding the semantics of meaning within intelligent systems will remain a pivotal research trajectory, with this paper providing a potent tool in the lexicon of natural language processing methodologies.