7 citations · 24 across the 6 of their papers we have counts for
6 papers
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
Connor Pryor, Quan Yuan, Jeremiah Liu +4
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.…
In-context Learning with Retrieved Demonstrations for Language Models: A Survey
Man Luo, Xin Xu, Yue Liu +2
Language models, especially pre-trained large language models, have showcased remarkable abilities as few-shot in-context learners (ICL), adept at adapting to new tasks with just a…
TaskLAMA: Probing the Complex Task Understanding of Language Models
Quan Yuan, Mehran Kazemi, Xin Xu +3
Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual st…
BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
Mehran Kazemi, Quan Yuan, Deepti Bhatia +4
Automated reasoning with unstructured natural text is a key requirement for many potential applications of NLP and for developing robust AI systems. Recently, Language Models (LMs)…
Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai +5
In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong…
Understanding Finetuning for Factual Knowledge Extraction from Language Models
Mehran Kazemi, Sid Mittal, Deepak Ramachandran
Language models (LMs) pretrained on large corpora of text from the web have been observed to contain large amounts of various types of knowledge about the world. This observation h…