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Found 10 papers, 5 papers with code
Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs
In this work, we study such behavior in LMs by proposing a task-agnostic framework for resolving ambiguity by asking users clarifying questions.
Propagating Knowledge Updates to LMs Through Distillation
Then, we update the model parameters so that the distribution of the LM (the student) matches the distribution of the LM conditioned on the definition (the teacher) on the transfer set.
Selectively Answering Ambiguous Questions
We investigate question answering from this perspective, focusing on answering a subset of questions with a high degree of accuracy, from a set of questions in which many are inherently ambiguous.
Mitigating Temporal Misalignment by Discarding Outdated Facts
While large language models are able to retain vast amounts of world knowledge seen during pretraining, such knowledge is prone to going out of date and is nontrivial to update.
Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge
Pre-trained language models (LMs) are used for knowledge intensive tasks like question answering, but their knowledge gets continuously outdated as the world changes.
DIFFQG: Generating Questions to Summarize Factual Changes
We propose representing factual changes between paired documents as question-answer pairs, where the answer to the same question differs between two versions.
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence
Question answering models can use rich knowledge sources -- up to one hundred retrieved passages and parametric knowledge in the large-scale language model (LM).
Entity Cloze By Date: What LMs Know About Unseen Entities
Given its wide coverage on entity knowledge and temporal indexing, our dataset can be used to evaluate LMs and techniques designed to modify or extend their knowledge.
SituatedQA: Incorporating Extra-Linguistic Contexts into QA
To construct SituatedQA, we first identify such questions in existing QA datasets.
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
We introduce CREAK, a testbed for commonsense reasoning about entity knowledge, bridging fact-checking about entities (Harry Potter is a wizard and is skilled at riding a broomstick) with commonsense inferences (if you're good at a skill you can teach others how to do it).
