Pith. sign in

REVIEW 1 cited by

Distilled ChatGPT Topic & Sentiment Modeling with Applications in Finance

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.02185 v1 pith:YFWA2NHZ submitted 2024-03-04 cs.LG cs.CEcs.CL

Olivier Gandouet , Mouloud Belbahri , Armelle Jezequel , Yuriy Bodjov This is my paper
classification cs.LGcs.CEcs.CL
keywords featuresmodelschatgptsentimenttopicutilizedaccuracyannotated
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In this study, ChatGPT is utilized to create streamlined models that generate easily interpretable features. These features are then used to evaluate financial outcomes from earnings calls. We detail a training approach that merges knowledge distillation and transfer learning, resulting in lightweight topic and sentiment classification models without significant loss in accuracy. These models are assessed through a dataset annotated by experts. The paper also delves into two practical case studies, highlighting how the generated features can be effectively utilized in quantitative investing scenarios.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Agentic Retrieval of Topics and Insights from Earnings Calls

    cs.LG 2025-07 conditional novelty 6.0 of 10

    An LLM agent extracts financial topics from earnings calls, builds a hierarchical topic ontology, and uses topic mention trends to flag rising and falling themes.

Pith tools