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20232025
most citedNo LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models

4 citations · 4 across the 7 of their papers we have counts for

collaborators

7 papers

cs.AI2025

Agent Ideate: A Framework for Product Idea Generation from Patents Using Agentic AI

Gopichand Kanumolu, Ashok Urlana, Charaka Vinayak Kumar +1

Patents contain rich technical knowledge that can inspire innovative product ideas, yet accessing and interpreting this information remains a challenge. This work explores the use…

cs.CL2025★ 4 cited

No LLM is Free From Bias: A Comprehensive Study of Bias Evaluation in Large Language Models

Charaka Vinayak Kumar, Ashok Urlana, Gopichand Kanumolu +2

Advancements in Large Language Models (LLMs) have increased the performance of different natural language understanding as well as generation tasks. Although LLMs have breached the…

cs.CL2025

HalluCounter: Reference-free LLM Hallucination Detection in the Wild!

Ashok Urlana, Gopichand Kanumolu, Charaka Vinayak Kumar +2

Response consistency-based, reference-free hallucination detection (RFHD) methods do not depend on internal model states, such as generation probabilities or gradients, which Grey-…

cs.CL2024

TeClass: A Human-Annotated Relevance-based Headline Classification and Generation Dataset for Telugu

Gopichand Kanumolu, Lokesh Madasu, Nirmal Surange +1

News headline generation is a crucial task in increasing productivity for both the readers and producers of news. This task can easily be aided by automated News headline-generatio…

cs.CL2024

SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages

Nedjma Ousidhoum, Shamsuddeen Hassan Muhammad, Mohamed Abdalla +24

Exploring and quantifying semantic relatedness is central to representing language and holds significant implications across various NLP tasks. While earlier NLP research primarily…

cs.CL2023

Unsupervised Approach to Evaluate Sentence-Level Fluency: Do We Really Need Reference?

Gopichand Kanumolu, Lokesh Madasu, Pavan Baswani +2

Fluency is a crucial goal of all Natural Language Generation (NLG) systems. Widely used automatic evaluation metrics fall short in capturing the fluency of machine-generated text.…