1 citations · 1 across the 2 of their papers we have counts for
4 papers
RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
Ádám Kovács, Nadia Verdha, Gábor Recski
We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named Entity Recognition (NER), or r…
Beyond Document Grounding: Span-Level Hallucination Detection over Code, Tool Output, and Documents
Ádám Kovács, Bowei He, Xue Liu +3
Hallucination detection for retrieval-augmented generation (RAG) is usually evaluated on natural-language document evidence. However, grounded generation systems increasingly rely…
Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents
Ádám Kovács
Coding agents repeatedly consume long tool observations even though only a small fraction of each observation matters for the next step. We study task-conditioned tool-output pruni…
LettuceDetect: A Hallucination Detection Framework for RAG Applications
Ádám Kovács, Gábor Recski
Retrieval Augmented Generation (RAG) systems remain vulnerable to hallucinated answers despite incorporating external knowledge sources. We present LettuceDetect a framework that a…