Language Modeling with Latent Situations
This addresses coherence issues in language models for NLP applications, representing a novel method for a known bottleneck rather than a foundational breakthrough.
The paper tackles the problem of language models generating incoherent outputs by introducing SituationSupervision, a method that trains models to construct and condition on explicit representations of entities and their states, resulting in coherence improvements of 4-11% with minimal annotations.
Language models (LMs) often generate incoherent outputs: they refer to events and entity states that are incompatible with the state of the world described in their inputs. We introduce SituationSupervision, a family of approaches for improving coherence in LMs by training them to construct and condition on explicit representations of entities and their states. SituationSupervision has two components: an auxiliary situation modeling task that trains models to predict state representations in context, and a latent state inference procedure that imputes these states from partially annotated training data. SituationSupervision can be applied to both fine-tuning (by supervising LMs to encode state variables in their hidden representations) and prompting (by inducing LMs to interleave textual descriptions of entity states with output text). In both cases, SituationSupervision requires only a small number of state annotations to produce major coherence improvements (between 4-11%), showing that standard LMs can be sample-efficiently trained to model not just language but the situations it describes.