Implicit Representations of Meaning in Neural Language Models (2106.00737v1)

Abstract: Does the effectiveness of neural LLMs derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe? In BART and T5 transformer LLMs, we identify contextual word representations that function as models of entities and situations as they evolve throughout a discourse. These neural representations have functional similarities to linguistic models of dynamic semantics: they support a linear readout of each entity's current properties and relations, and can be manipulated with predictable effects on language generation. Our results indicate that prediction in pretrained neural LLMs is supported, at least in part, by dynamic representations of meaning and implicit simulation of entity state, and that this behavior can be learned with only text as training data. Code and data are available at https://github.com/belindal/state-probes .

Implicit Representations of Meaning in Neural LLMs

In the paper of neural LLMs (NLMs), a key area of inquiry is the extent to which these models capture implicit representations of meaning beyond mere word co-occurrence statistics. The paper "Implicit Representations of Meaning in Neural LLMs" by Belinda Z. Li, Maxwell Nye, and Jacob Andreas critically examines this issue by analyzing how transformer architectures such as BART and T5 develop contextual representations that functionally resemble models of dynamic semantics.

The researchers focus on two distinct models, BART and T5, and utilize datasets derived from the English-language Alchemy and TextWorld tasks to probe the dynamics of NLMs. The authors posit that such LLMs, when pretrained on expansive text corpora, implicitly learn representations of entity states and semantic knowledge influence generation outcomes.

Methodology and Findings

The authors employ a probing technique designed to test whether the representations of sentences as output by the LLMs align with the state descriptions of entities within these sentences. They propose that probing can reveal if NLMs encode dynamic facts about objects and relationships in a discourse. In practical terms, this involves extracting the semantic state information from the word representations (referred to as embeddings) generated by LLMs.

The results show that the NLMs partially succeed in encoding dynamic entity states, with T5 achieving a 53.8% state-level exact match (EM) on TextWorld data, suggesting that it can predict the full entity state with a reasonable degree of accuracy. This capability primarily derives from open-domain pretraining rather than domain-specific fine-tuning, highlighting the significance of extensive pretraining in developing nuanced semantic understandings.

NLM representations of concepts such as object location or possession within a game environment can be linearly decoded, hinting at the presence of implicit semantic structures. Additionally, the paper reveals a relative localization of entity information within token representations, most notably in final mentions of entities in a text.

Implications

These findings suggest several implications for the broader field of AI. The ability of NLMs to implicitly simulate aspects of meaning has potential applications in improving coherence and factual consistency in language generation tasks. Additionally, the research offers insights into how linguistic representation can potentially contribute to reducing biases in AI systems by enabling targeted corrections in generated outputs.

Moreover, understanding the degree of semantic modeling in current NLMs could pave the way for future advancements in language understanding systems, wherein more explicitly structured representations may be developed to handle complex domains involving intricate semantic states.

Future Directions

Given the limitations identified, including the models' partial success rate and the relatively simple nature of the test environments, further research is necessary to explore richer worlds with more complex semantics and interactions. Additionally, investigating more sophisticated probing techniques and non-linear models to decode complex state representations could enhance the interpretability and performance of NLMs in diverse applications.

In conclusion, this paper outlines a significant step toward understanding the implicit semantic capabilities of neural LLMs, pointing to the future potential of such models in generating coherent, contextually-informed discourse in AI systems. As neural architectures continue to evolve, so too will the sophistication of their semantic representations, promising exciting advancements in AI's understanding and generation of human language.