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Found 17 papers, 8 papers with code
Bayesian Preference Elicitation with Language Models
We introduce OPEN (Optimal Preference Elicitation with Natural language) a framework that uses BOED to guide the choice of informative questions and an LM to extract features and translate abstract BOED queries into natural language questions.
Learning with Language-Guided State Abstractions
We describe a framework for using natural language to design state abstractions for imitation learning.
Preference-Conditioned Language-Guided Abstraction
We observe that how humans behave reveals how they see the world.
Eliciting Human Preferences with Language Models
Language models (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts.
LaMPP: Language Models as Probabilistic Priors for Perception and Action
Language models trained on large text corpora encode rich distributional information about real-world environments and action sequences.
Language Modeling with Latent Situations
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.
Quantifying Adaptability in Pre-trained Language Models with 500 Tasks
When a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model?
Implicit Representations of Meaning in Neural Language Models
Does the effectiveness of neural language models derive entirely from accurate modeling of surface word co-occurrence statistics, or do these models represent and reason about the world they describe?
On the Influence of Masking Policies in Intermediate Pre-training
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline.
On Unifying Misinformation Detection
In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup.
Studying Strategically: Learning to Mask for Closed-book QA
Thus, our policy packs task-relevant knowledge into the parameters of a language model.
Efficient One-Pass End-to-End Entity Linking for Questions
We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass.
Linformer: Self-Attention with Linear Complexity
Large transformer models have shown extraordinary success in achieving state-of-the-art results in many natural language processing applications.
Language Models as Fact Checkers?
Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data.
Active Learning for Coreference Resolution using Discrete Annotation
We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.
