When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain
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Raj Shah, Kunal Chawla, Dheeraj Eidnani, Agam Shah, Wendi Du, Sudheer Chava, Natraj Raman, Charese Smiley, Jiaao Chen, Diyi Yang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.
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SciBench involved manual data extraction and complex problem formulations, complicating the installation process.
“For example, SciBench [46] involved manual data extraction and complex problem formulations, further complicating the installation process.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench involved manual data extraction and complex problem formulations, which significantly extended the evaluation process.
“For instance, SciBench [46] involved manual data extraction and complex problem formulations, which significantly extended the evaluation process, making it difficult to keep pace with the rapid advancements in LLM development.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
The SciBench benchmark demonstrated challenges in evaluating true reasoning capabilities of LLMs due to reliance on technical optimization, as seen when LLMs, prompted with external tools, incorrectly derived equations, highlighting their struggle to understand mathematical relationships beyond mere optimization.
“SciBench [46]: The study demonstrated challenges in evaluating the true reasoning capabilities of LLMs due to reliance on technical optimization, as seen when LLMs, prompted with external tools, incorrectly derived equations, highlighting their struggle to understand mathematical relationships beyond mere optimization.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
The SciBench benchmark demonstrated a tension between helpfulness and harmlessness in LLM benchmarks by emphasizing benchmarks' struggle to quantify a balance between providing helpful information and avoiding harmful outputs, particularly through use of A/B tests and incorporation of human evaluation methods, without a clear criterion for this balance.
“SciBench [46]: The study demonstrated a tension between helpfulness and harmlessness in LLM benchmarks by emphasizing benchmarks' struggle to quantify a balance between providing helpful information and avoiding harmful outputs, particularly through the use of A/B tests and the incorporation of human evaluation methods, without a clear criterion for this balance.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench demonstrated complexity in implementing and scaling benchmarks, involving intricate processes like manual data extraction from PDFs to LaTeX, complex problem formulations, and use of external tools for evaluation.
“The study demonstrated complexity in implementing and scaling benchmarks, as it involved intricate processes like manual data extraction from PDFs to LaTeX, complex problem formulations, and the use of external tools for evaluation, which aligned with the stated concerns of installation and scalability challenges.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench introduced SciBench to systematically examine complex scientific problem-solving capabilities of LLMs, but encountered inconsistencies in benchmark implementation; it acknowledged the challenge of ensuring uniform assessment across different LLM configurations without detailing explicit measures to address these inconsistencies directly.
“The study introduced SciBench to systematically examine complex scientific problem-solving capabilities of LLMs, but it encountered inconsistencies in benchmark implementation.The benchmarks, derived from collegiate-level textbooks and exams, required advanced computations like integration and differentiation, which varied in execution complexity.Despite efforts to minimize data leakage and use”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench introduced a benchmark requiring detailed solutions and complex reasoning, involving manual LaTeX formatting from PDFs; despite efforts to mitigate slow iteration time, extensive manual data processing indicated potential slow test iteration time.
“The study introduced SciBench, a benchmark requiring detailed solutions and complex reasoning, involving manual LaTeX formatting from PDFs to ensure data integrity and minimize training data leakage.Despite efforts to mitigate slow iteration time through detailed problem and solution documentation, the study's extensive and manual data processing indicated potential slow test iteration time for”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench involved human annotators in data collection and verification, emphasizing influence of human diversity, but acknowledged challenges of diverse human interpretation without detailing efforts to address this specific inadequacy.
“The study involved human annotators in data collection and verification, emphasizing the influence of human diversity on the benchmark's development and evaluation.It acknowledged the challenges of diverse human interpretation, but did not detail efforts to address this specific benchmark inadequacy.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
SciBench relied on standardized approaches for benchmarking LLMs through complex scientific problem-solving tasks without explicitly addressing or incorporating diverse cultural and ideological considerations into evaluation or benchmark construction.
“The study demonstrated a reliance on standardized approaches for benchmarking LLMs through complex scientific problem-solving tasks without explicitly addressing or incorporating diverse cultural and ideological considerations into the evaluation process or the construction of benchmarks.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [46]
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- Authors
- Shah, R., Chawla, K., Eidnani, D., Shah, A., Du, W., Chava, S., Raman, N., Smiley, C., Chen, J., & Yang, D.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Published
- 2022
- Pages
- 2322-2335
- OpenAlex
- W4385572697
- Cited by
- 74
- References
- 38
- Citations by year
- 2026: 14 · 2025: 33 · 2024: 10 · 2023: 17
- FWCI
- 8.30
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- Georgia Institute of Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Keywords
- pre-trained language models, benchmark dataset, financial domain, flues
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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