most citedDr.ICL: Demonstration-Retrieved In-context Learning

7 citations · 26 across the 10 of their papers we have counts for

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cs.CL2024

SocialQuotes: Learning Contextual Roles of Social Media Quotes on the Web

John Palowitch, Hamidreza Alvari, Mehran Kazemi +2

Web authors frequently embed social media to support and enrich their content, creating the potential to derive web-based, cross-platform social media representations that can enab…

cs.CL20242 cited

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning

Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin +6

Large language models (LLMs) have showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal reasoning tasks involving complex tem…

cs.CL2024

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.…

cs.CL20246 cited

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…

cs.CL2023

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…

cs.CL20237 cited

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…