26 citations · 39 across the 19 of their papers we have counts for
6 papers · 1 filter
Evaluating CxG Generalisation in LLMs via Construction-Based NLI Fine Tuning
Tom Mackintosh, Harish Tayyar Madabushi, Claire Bonial
We probe large language models' ability to learn deep form-meaning mappings as defined by construction grammars. We introduce the ConTest-NLI benchmark of 80k sentences covering ei…
Dancing with Deer: A Constructional Perspective on MWEs in the Era of LLMs
Claire Bonial, Julia Bonn, Harish Tayyar Madabushi
In this chapter, we argue for the benefits of understanding multiword expressions from the perspective of usage-based, construction grammar approaches. We begin with a historical o…
Neither Stochastic Parroting nor AGI: LLMs Solve Tasks through Context-Directed Extrapolation from Training Data Priors
Harish Tayyar Madabushi, Melissa Torgbi, Claire Bonial
This position paper argues that many behaviours framed as either "stochastic parroting" or "advanced reasoning" are better explained as context-directed extrapolation from training…
Understanding Common Ground Misalignment in Goal-Oriented Dialog: A Case-Study with Ubuntu Chat Logs
Rupak Sarkar, Neha Srikanth, Taylor Hudson +3
While it is commonly accepted that maintaining common ground plays a role in conversational success, little prior research exists connecting conversational grounding to success in…
FRIDA to the Rescue! Analyzing Synthetic Data Effectiveness in Object-Based Common Sense Reasoning for Disaster Response
Mollie Shichman, Claire Bonial, Austin Blodgett +3
During Human Robot Interactions in disaster relief scenarios, Large Language Models (LLMs) have the potential for substantial physical reasoning to assist in mission objectives. Ho…
Beyond Memorization: Assessing Semantic Generalization in Large Language Models Using Phrasal Constructions
Wesley Scivetti, Melissa Torgbi, Austin Blodgett +4
The web-scale of pretraining data has created an important evaluation challenge: to disentangle linguistic competence on cases well-represented in pretraining data from generalizat…