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Found 11 papers, 4 papers with code
TaskLAMA: Probing the Complex Task Understanding of Language Models
Our experiments reveal that LLMs are able to decompose complex tasks into individual steps effectively, with a relative improvement of 15% to 280% over the best baseline.
BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
One widely-applicable way of resolving conflicts is to impose preferences over information sources (e. g., based on source credibility or information recency) and adopt the source with higher preference.
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play
Standard empirical risk minimization (ERM) training can produce deep neural network (DNN) models that are accurate on average but under-perform in under-represented population subgroups, especially when there are imbalanced group distributions in the long-tailed training data.
Understanding Finetuning for Factual Knowledge Extraction from Language Models
Recently, it has been shown that finetuning LMs on a set of factual knowledge makes them produce better answers to queries from a different set, thus making finetuned LMs a good candidate for knowledge extraction and, consequently, knowledge graph construction.
LAMBADA: Backward Chaining for Automated Reasoning in Natural Language
Remarkable progress has been made on automated reasoning with natural text, by using Language Models (LMs) and methods such as Chain-of-Thought and Selection-Inference.
Tackling Provably Hard Representative Selection via Graph Neural Networks
Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors
Interactive recommender systems have emerged as a promising paradigm to overcome the limitations of the primitive user feedback used by traditional recommender systems (e. g., clicks, item consumption, ratings).
Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering
Through a user preference study, we demonstrate that the oracle behavior of our proposed system that provides responses based on presupposition failure is preferred over the oracle behavior of existing QA systems.
Do Language Embeddings Capture Scales?
Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense, and factual knowledge.
How Large Are Lions? Inducing Distributions over Quantitative Attributes
Most current NLP systems have little knowledge about quantitative attributes of objects and events.
