@inproceedings{bairi-krishnamurthy-2026-lost,
title = "Lost the Negation or Lost in Negation",
author = "Bairi, Vennela and
Krishnamurthy, Parameswari",
editor = "Sarveswaran, Kengatharaiyer and
Vaidya, Ashwini",
booktitle = "Proceedings of the Second workshop on Challenges in Processing {S}outh {A}sian Languages ({CH}i{PSAL}2026)",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.chipsal-1.3/",
doi = "10.63317/2erp7saprer9",
pages = "18--31",
abstract = "Despite negation being one of the core element in any language, it remains a challenging phenomenon for modern Large Language Models(LLMs). Recently, there have been growing efforts to evaluate how models handle negation. However, the existing probing datasets are mostly English-centric. To facilitate evaluation for Indian Languages especially Telugu, which has complex morphological features, we present NEGTEG benchmark. This benchmark is a test suite that contains 5 tasks: Negation Detection, Negation Translation, Paraphrase Detection, Sentiment Analysis and Polarity Flipping. The test suite is designed based on strong linguistic analysis and includes annotations of different negation types. This helps us evaluate how models perform across various forms of negation. We use the benchmark to probe the negation handling capabilities of multilingual language models at different levels and our evaluation reveals that most of the models struggle significantly with Telugu negation across all tasks."
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<abstract>Despite negation being one of the core element in any language, it remains a challenging phenomenon for modern Large Language Models(LLMs). Recently, there have been growing efforts to evaluate how models handle negation. However, the existing probing datasets are mostly English-centric. To facilitate evaluation for Indian Languages especially Telugu, which has complex morphological features, we present NEGTEG benchmark. This benchmark is a test suite that contains 5 tasks: Negation Detection, Negation Translation, Paraphrase Detection, Sentiment Analysis and Polarity Flipping. The test suite is designed based on strong linguistic analysis and includes annotations of different negation types. This helps us evaluate how models perform across various forms of negation. We use the benchmark to probe the negation handling capabilities of multilingual language models at different levels and our evaluation reveals that most of the models struggle significantly with Telugu negation across all tasks.</abstract>
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%0 Conference Proceedings
%T Lost the Negation or Lost in Negation
%A Bairi, Vennela
%A Krishnamurthy, Parameswari
%Y Sarveswaran, Kengatharaiyer
%Y Vaidya, Ashwini
%S Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026)
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F bairi-krishnamurthy-2026-lost
%X Despite negation being one of the core element in any language, it remains a challenging phenomenon for modern Large Language Models(LLMs). Recently, there have been growing efforts to evaluate how models handle negation. However, the existing probing datasets are mostly English-centric. To facilitate evaluation for Indian Languages especially Telugu, which has complex morphological features, we present NEGTEG benchmark. This benchmark is a test suite that contains 5 tasks: Negation Detection, Negation Translation, Paraphrase Detection, Sentiment Analysis and Polarity Flipping. The test suite is designed based on strong linguistic analysis and includes annotations of different negation types. This helps us evaluate how models perform across various forms of negation. We use the benchmark to probe the negation handling capabilities of multilingual language models at different levels and our evaluation reveals that most of the models struggle significantly with Telugu negation across all tasks.
%R 10.63317/2erp7saprer9
%U https://aclanthology.org/2026.chipsal-1.3/
%U https://doi.org/10.63317/2erp7saprer9
%P 18-31
Markdown (Informal)
[Lost the Negation or Lost in Negation](https://aclanthology.org/2026.chipsal-1.3/) (Bairi & Krishnamurthy, CHiPSAL 2026)
ACL
- Vennela Bairi and Parameswari Krishnamurthy. 2026. Lost the Negation or Lost in Negation. In Proceedings of the Second workshop on Challenges in Processing South Asian Languages (CHiPSAL2026), pages 18–31, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).