12 citations · 14 across the 9 of their papers we have counts for
14 papers
CONFETTI: Conversational Function-Calling Evaluation Through Turn-Level Interactions
Tamer Alkhouli, Katerina Margatina, James Gung +4
We introduce Conversational Function-Calling Evaluation Through Turn-Level Interactions (CONFETTI), a conversational benchmark1 designed to evaluate the function-calling capabiliti…
A Study on Leveraging Search and Self-Feedback for Agent Reasoning
Karthikeyan K, Michelle Yuan, Elman Mansimov +6
Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the g…
Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?
Ahmed Alajrami, Katerina Margatina, Nikolaos Aletras
Understanding how and what pre-trained language models (PLMs) learn about language is an open challenge in natural language processing. Previous work has focused on identifying whe…
On the Limitations of Simulating Active Learning
Katerina Margatina, Nikolaos Aletras
Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve over random sampling. How…
Active Learning Principles for In-Context Learning with Large Language Models
Katerina Margatina, Timo Schick, Nikolaos Aletras +1
The remarkable advancements in large language models (LLMs) have significantly enhanced the performance in few-shot learning settings. By using only a small number of labeled examp…
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Katerina Margatina, Shuai Wang, Yogarshi Vyas +3
Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g.…