Can Language Models Learn Semantics Through Next-Word Prediction? Investigating Entailment Relations

Core Concepts

The authors investigate the ability of language models to predict entailment relations through next-word prediction, finding that LMs implicitly model aspects of semantics to predict semantic effects on sentence co-occurrence patterns.

Abstract

The study explores whether language models can infer semantics from text co-occurrences, focusing on entailment. It evaluates the distributional entailment test and discusses the implications of noise tolerance and explanations in human redundancy. The findings suggest a need for nuanced pragmatic theories beyond Gricean speakers. Content Summary: Authors explore if LMs learn semantics through next-word prediction. Study investigates entailment detection using co-occurrence probabilities. Results show LMs model semantic properties to predict sentence relationships. Discussion includes noise tolerance and explanations in human redundancy. Findings indicate a requirement for more comprehensive pragmatic theories.

Stats

p(z | w) ∝ E[e^iℓ(e | w)] exp(-c(z)) p(e | w, z) = Π(1 - ϵzt if t ∈ e; ϵzt otherwise) ˆEn p(x, y) = log p(xny) - log p(xn$) - log p(yn+1) + log p(yn$)

Quotes

"LMs implicitly model aspects of semantics to predict semantic effects on sentence co-occurrence patterns." "The flipped test detects entailment better than the original one." "Human speakers produce more contextually entailed sentences than idealized Gricean speakers."

Key Insights Distilled From

Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment

by William Merr... at arxiv.org 03-04-2024

https://arxiv.org/pdf/2402.13956.pdf

Deeper Inquiries

How might noise tolerance explain the flipped entailment test pattern?

Noise tolerance can potentially explain the flipped entailment test pattern by accounting for redundancy in human speech acts. In a noisy communication channel model, where there is a probability that certain sentences may not be fully interpreted by the listener, speakers may repeat important information to hedge against misinterpretation. This repetition can lead to an increase in the likelihood of entailed continuations being more probable than non-entailed ones. The noise-tolerant speaker model allows for this redundancy and provides a mechanism for understanding why certain sentences are repeated in natural language.

What are the implications of human redundancy in natural corpora for linguistic theories?

The presence of human redundancy in natural corpora has significant implications for linguistic theories, particularly those related to pragmatics and semantics. By observing instances of redundant text produced by humans, we gain insights into how speakers use language beyond mere information transfer. Redundancy serves various functions such as emphasizing key points, providing summaries or conclusions, and aiding comprehension through explanations. These findings suggest that linguistic theories need to evolve to better account for these aspects of human speech acts. Understanding how and why humans produce redundant text can lead to more nuanced models that capture the complexities of communication beyond just semantic content.

How can pragmatic theories better account for explanations in human speech acts?

Pragmatic theories can better account for explanations in human speech acts by incorporating context-dependent cost functions into their models. When speakers provide explanations or summaries after presenting detailed information (as seen in examples from natural corpora), there is a shift in processing cost dynamics that influences their utterances. By considering factors like cognitive load reduction through concise explanations following detailed premises, pragmatic theories can predict patterns observed in real-world communication more accurately. These extensions allow models to capture how speakers strategically structure their discourse based on contextual relevance and informational hierarchy within conversations.