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Balaji Ganesan

4 accepted papers

2025

SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models

EMNLP 2025

When does a large language model (LLM) know what it does not know? Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output, and is therefore increasingly recognized as a crucial component of trusted AI systems. Black-box UQ

Cited by 0SourcePDFScholar
2025

The Consistency Hypothesis in Uncertainty Quantification for Large Language Models

UAI 2025

Estimating the confidence of large language model (LLM) outputs is essential for real-world applications requiring high user trust. Black-box uncertainty quantification (UQ) methods, relying solely on model API access, have gained popularity due to their practical benefits. In this paper, we examine

Cited by 0SourcePDFScholar
2024

LLM-powered GraphQL Generator for Data Retrieval

IJCAI 2024poster

GraphQL offers an efficient, powerful, and flexible alternative to REST APIs. However, application developers writing GraphQL clients need both technical and domain-specific expertise to reap its benefits, and avoid over-fetching or under-fetching data. Automated GraphQL generation has so far proven…

2024

Sequential API Function Calling Using GraphQL Schema

EMNLP 2024main

Function calling using Large Language Models (LLMs) is an active research area that aims to empower LLMs with the ability to execute APIs to perform real-world tasks. However, sequential function calling using LLMs with interdependence between functions is still under-explored. To this end, we intro…

Cited by 1SourcePDFScholar