activity
20112026
most citedAcquiring Correct Knowledge for Natural Language Generation

83 citations · 83 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework

Steven Wallace, William D. Harcourt, Richard Hann +3

Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain. Yet, pixel-level annotation of glacier sur…

cs.AI2026

Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs

Milan Markovic, Goutham Indukuri, Somayajulu Sripada +7

Systematic reviews of Randomised Controlled Trials (RCTs) are routinely used as evidence for clinical care guidelines. Such evidence has to meet high research integrity standards t…

cs.CL2025

Input Matters: Evaluating Input Structure's Impact on LLM Summaries of Sports Play-by-Play

Barkavi Sundararajan, Somayajulu Sripada, Ehud Reiter

A major concern when deploying LLMs in accuracy-critical domains such as sports reporting is that the generated text may not faithfully reflect the input data. We quantify how inpu…

cs.CL2025

Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models

Gagan Bhatia, Somayajulu G Sripada, Kevin Allan +1

Hallucinations in large language models (LLMs) produce fluent continuations that are not supported by the prompt, especially under minimal contextual cues and ambiguity. We introdu…

cs.CL2024

Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo

Barkavi Sundararajan, Somayajulu Sripada, Ehud Reiter

Neural Table-to-Text models tend to hallucinate, producing texts that contain factual errors. We investigate whether such errors in the output can be traced back to problems with t…

cs.CL2011★ 83 cited

Acquiring Correct Knowledge for Natural Language Generation

E. Reiter, R. Robertson, S. G. Sripada

Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems,…