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Nicolò Felicioni

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3 papers

1 author row

TMLR Journal 2025 Journal Article

Hallucination Detection on a Budget: Efficient Bayesian Estimation of Semantic Entropy

Kamil Ciosek
Nicolò Felicioni
Sina Ghiassian

Detecting whether an LLM hallucinates is an important research challenge. One promising way of doing so is to estimate the semantic entropy (Farquhar et al., 2024) of the distribution of generated sequences. We propose a new algorithm for doing that, with two main advantages. First, due to us taking the Bayesian approach, we achieve a much better quality of semantic entropy estimates for a given budget of samples from the LLM. Second, we are able to tune the number of samples adaptively so that `harder' contexts receive more samples. We demonstrate empirically that our approach systematically beats the baselines, requiring only 53% of samples used by Farquhar et al. (2024) to achieve the same quality of hallucination detection as measured by AUROC. Moreover, quite counterintuitively, our estimator is useful even with just one sample from the LLM.

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TMLR Journal 2024 Journal Article

On the Importance of Uncertainty in Decision-Making with Large Language Models

Nicolò Felicioni
Lucas Maystre
Sina Ghiassian
Kamil Ciosek

We investigate the role of uncertainty in decision-making problems with natural language as input. For such tasks, using Large Language Models as agents has become the norm. However, none of the recent approaches employ any additional phase for estimating the uncertainty the agent has about the world during the decision-making task. We focus on a fundamental decision-making framework with natural language as input, which is the one of contextual bandits, where the context information consists of text. As a representative of the approaches with no uncertainty estimation, we consider an LLM agent with a greedy policy, which picks the action corresponding to the largest predicted reward. We compare this baseline to LLM agents that make active use of uncertainty estimation by integrating the uncertainty in a Thompson Sampling policy. We employ different techniques for uncertainty estimation, such as Laplace Approximation, Dropout, and Epinets. We empirically show on real-world data that the greedy policy performs worse than the Thompson Sampling policies. These findings suggest that, while overlooked in the LLM literature, uncertainty improves performance on bandit tasks with LLM agents.

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NeurIPS Conference 2022 Conference Paper

Off-Policy Evaluation with Deficient Support Using Side Information

Nicolò Felicioni
Maurizio Ferrari Dacrema
Marcello Restelli
Paolo Cremonesi

The Off-Policy Evaluation (OPE) problem consists in evaluating the performance of new policies from the data collected by another one. OPE is crucial when evaluating a new policy online is too expensive or risky. Many of the state-of-the-art OPE estimators are based on the Inverse Propensity Scoring (IPS) technique, which provides an unbiased estimator when the full support assumption holds, i. e. , when the logging policy assigns a non-zero probability to each action. However, there are several scenarios where this assumption does not hold in practice, i. e. , there is deficient support, and the IPS estimator is biased in the general case. In this paper, we consider two alternative estimators for the deficient support OPE problem. We first show how to adapt an estimator that was originally proposed for a different domain to the deficient support setting. Then, we propose another estimator, which is a novel contribution of this paper. These estimators exploit additional information about the actions, which we call side information, in order to make reliable estimates on the unsupported actions. Under alternative assumptions that do not require full support, we show that the considered estimators are unbiased. We also provide a theoretical analysis of the concentration when relaxing all the assumptions. Finally, we provide an experimental evaluation showing how the considered estimators are better suited for the deficient support setting compared to the baselines.

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