Papers by Balaji Ganesan

2 papers
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models (2025.findings-emnlp)

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Challenge: Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output.
Approach: They propose a black-box approach where consistency is used as a proxy for confidence in a model's output.
Outcome: The proposed methods are primarily but not necessarily entirely black- box, with consistency between output and other sampled generations used as a proxy for confidence in its correctness.
Sequential API Function Calling Using GraphQL Schema (2024.emnlp-main)

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Challenge: Function calling using Large Language Models (LLMs) is an active research area . however, sequential function calling using LLMs with interdependence between functions is still under-explored .
Approach: They propose a dataset representing real-world REST API calls with variable mapping between functions using natural language utterances paired with function call sequences.
Outcome: The proposed dataset represents real-world REST API calls with variable mapping between functions.

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