@inproceedings{li-wang-2026-selective,
title = "Selective Compliance in Financial Rule Reasoning: A Solver-Verified {C}hinese Benchmark and Empirical Analysis",
author = "Li, Peng and
Wang, Yinchun",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.44/",
pages = "549--565",
ISBN = "979-8-89176-466-8",
abstract = "Selective compliance is important for financial LLM applications: an applicable context rule should override the LLM{'}s parametric rule, while an inapplicable rule should be ignored. Though previous studies recognize the importance of rule selection, dedicated evaluations tailored specifically to the financial domain remain absent. To bridge this gap, we build a controlled benchmark based on the CFLUE dataset, where intervention rules are synthesized and FOL logic is applied to ensure the correctness of the new derived answer. Furthermore, we designed three evaluation settings covering clean inputs, noisy inputs, and multi-hop reasoning scenarios. We conduct a systematic comparison across different rule selection strategies and models. Experiments show that: (1) For recent advanced LLMs, single-pass integrated reasoning approach (CAR-Joint) consistently outperforms the decoupled `route-then-answer' approach (CAR-Route). (2) Our proposed post-hoc review method, CAR-Joint-Review, consistently outperforms CAR-Joint. Similarly, pre-hoc routing via the parametric rule elicitation method CAR-Elicit-Route surpasses CAR-Route in multi-hop settings. The performance gains are model-dependent. These findings reveal the trade-offs across various methods and LLMs in financial selective compliance."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-wang-2026-selective">
<titleInfo>
<title>Selective Compliance in Financial Rule Reasoning: A Solver-Verified Chinese Benchmark and Empirical Analysis</title>
</titleInfo>
<name type="personal">
<namePart type="given">Peng</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yinchun</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jimin</namePart>
<namePart type="family">Huang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yongjae</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sophia</namePart>
<namePart type="family">Ananiadou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hsin-Hsi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hiroya</namePart>
<namePart type="family">Takamura</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chung-Chi</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-466-8</identifier>
</relatedItem>
<abstract>Selective compliance is important for financial LLM applications: an applicable context rule should override the LLM’s parametric rule, while an inapplicable rule should be ignored. Though previous studies recognize the importance of rule selection, dedicated evaluations tailored specifically to the financial domain remain absent. To bridge this gap, we build a controlled benchmark based on the CFLUE dataset, where intervention rules are synthesized and FOL logic is applied to ensure the correctness of the new derived answer. Furthermore, we designed three evaluation settings covering clean inputs, noisy inputs, and multi-hop reasoning scenarios. We conduct a systematic comparison across different rule selection strategies and models. Experiments show that: (1) For recent advanced LLMs, single-pass integrated reasoning approach (CAR-Joint) consistently outperforms the decoupled ‘route-then-answer’ approach (CAR-Route). (2) Our proposed post-hoc review method, CAR-Joint-Review, consistently outperforms CAR-Joint. Similarly, pre-hoc routing via the parametric rule elicitation method CAR-Elicit-Route surpasses CAR-Route in multi-hop settings. The performance gains are model-dependent. These findings reveal the trade-offs across various methods and LLMs in financial selective compliance.</abstract>
<identifier type="citekey">li-wang-2026-selective</identifier>
<location>
<url>https://aclanthology.org/2026.finnlp-1.44/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>549</start>
<end>565</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Selective Compliance in Financial Rule Reasoning: A Solver-Verified Chinese Benchmark and Empirical Analysis
%A Li, Peng
%A Wang, Yinchun
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F li-wang-2026-selective
%X Selective compliance is important for financial LLM applications: an applicable context rule should override the LLM’s parametric rule, while an inapplicable rule should be ignored. Though previous studies recognize the importance of rule selection, dedicated evaluations tailored specifically to the financial domain remain absent. To bridge this gap, we build a controlled benchmark based on the CFLUE dataset, where intervention rules are synthesized and FOL logic is applied to ensure the correctness of the new derived answer. Furthermore, we designed three evaluation settings covering clean inputs, noisy inputs, and multi-hop reasoning scenarios. We conduct a systematic comparison across different rule selection strategies and models. Experiments show that: (1) For recent advanced LLMs, single-pass integrated reasoning approach (CAR-Joint) consistently outperforms the decoupled ‘route-then-answer’ approach (CAR-Route). (2) Our proposed post-hoc review method, CAR-Joint-Review, consistently outperforms CAR-Joint. Similarly, pre-hoc routing via the parametric rule elicitation method CAR-Elicit-Route surpasses CAR-Route in multi-hop settings. The performance gains are model-dependent. These findings reveal the trade-offs across various methods and LLMs in financial selective compliance.
%U https://aclanthology.org/2026.finnlp-1.44/
%P 549-565
Markdown (Informal)
[Selective Compliance in Financial Rule Reasoning: A Solver-Verified Chinese Benchmark and Empirical Analysis](https://aclanthology.org/2026.finnlp-1.44/) (Li & Wang, FinNLP 2026)
ACL