arXiv subjects

Sara Rezaeimanesh

Publications and source records attributed to Sara Rezaeimanesh.

3 recordsLinked to original sources

arXiv2026

Ordinal Gates, Cardinal Bets: Matching LLM Confidence to the Financial Decision Operator

LLM confidence scores are not independently deployable objects: their decision value depends on the downstream operator and exposure controller that consume them. Monotone recalibration cannot change a coverage-matched rank-based gate, whereas position sizing consumes score magnitude, so changing a confidence map can invalidate a scale fitted to the previous score distribution. We test this on FactSet news for Nasdaq-100 equities, fitting maps and scales on 2021 and evaluating nine open-weight LLMs out-of-sample on 2022--2023. Cross-applying raw and correctness maps with independently fitted scales shows that the two components are not portable alone: scale transfer reduces certainty-equivalent return (CER) in $8/9$ models and produces large risk-target errors. Matching each map with its fitted scale improves ensemble CER by $9.2$ percentage points per year under frozen-scale control ($p<0.001$), and the effect remains significant when the single largest-contributing model is excluded ($+5.5$pp/yr), so it is not driven by one case. Under an identical adaptive-volatility controller, however, the incremental effect falls to $+1.6$pp/yr, with a significant controller interaction. Annual walk-forward effects are smaller, although map--scale interaction remains positive in every fold. Confidence transformations should therefore be evaluated jointly with the downstream controllers that consume them.

cs.CE↗

arXiv2026

What Language Models Know But Don't Say: Non-Generative Prior Extraction for Generalization

In domains like medicine and finance, large-scale labeled data is costly and often unavailable, leading to models trained on small datasets that struggle to generalize to real-world populations. Large language models contain extensive knowledge from years of research across these domains. We propose LoID (Logit-Informed Distributions), a deterministic method for extracting informative prior distributions for Bayesian logistic regression by directly accessing their token-level predictions. Rather than relying on generated text, we probe the model's confidence in opposing semantic directions (positive vs. negative impact) through carefully constructed sentences. By measuring how consistently the LLM favors one direction across diverse phrasings, we extract the strength and reliability of the model's belief about each feature's influence. We evaluate LoID on ten real-world tabular datasets under synthetic out-of-distribution (OOD) settings characterized by covariate shift, where the training data represents only a subset of the population. We compare our approach against (1) standard uninformative priors, (2) AutoElicit, a recent method that prompts LLMs to generate priors via text completions, (3) LLMProcesses, a method that uses LLMs to generate numerical predictions through in-context learning and (4) an oracle-style upper bound derived from fitting logistic regression on the full dataset. We assess performance using Area Under the Curve (AUC). Across datasets, LoID significantly improves performance over logistic regression trained on OOD data, recovering up to \textbf{59\%} of the performance gap relative to the oracle model. LoID outperforms AutoElicit and LLMProcessesc on 8 out of 10 datasets, while providing a reproducible and computationally efficient mechanism for integrating LLM knowledge into Bayesian inference.

cs.CL↗

arXiv2025

Large Language Models for Persian $ \leftrightarrow $ English Idiom Translation

Large language models (LLMs) have shown superior capabilities in translating figurative language compared to neural machine translation (NMT) systems. However, the impact of different prompting methods and LLM-NMT combinations on idiom translation has yet to be thoroughly investigated. This paper introduces two parallel datasets of sentences containing idiomatic expressions for Persian$\rightarrow$English and English$\rightarrow$Persian translations, with Persian idioms sampled from our PersianIdioms resource, a collection of 2,200 idioms and their meanings, with 700 including usage examples. Using these datasets, we evaluate various open- and closed-source LLMs, NMT models, and their combinations. Translation quality is assessed through idiom translation accuracy and fluency. We also find that automatic evaluation methods like LLM-as-a-judge, BLEU, and BERTScore are effective for comparing different aspects of model performance. Our experiments reveal that Claude-3.5-Sonnet delivers outstanding results in both translation directions. For English$\rightarrow$Persian, combining weaker LLMs with Google Translate improves results, while Persian$\rightarrow$English translations benefit from single prompts for simpler models and complex prompts for advanced ones.

cs.CL↗