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Glenn Matlin
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·
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Parent(s):
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flame
Browse files- README.md +74 -15
- index.html +85 -257
- static/css/index.css +6 -0
README.md
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---
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title:
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colorFrom:
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license: cc-by-
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---
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#
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```
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@article{
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author = {
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title = {
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journal = {
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year = {
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}
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```
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<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This
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---
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title: FLaME - Financial Language Model Evaluation
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emoji: 📊
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colorFrom: blue
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colorTo: orange
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sdk: static
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license: cc-by-sa-4.0
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---
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# FLaME: Holistic Financial Language Model Evaluation
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Website for the FLaME research project showcasing our benchmarking suite for evaluating language models on financial NLP tasks.
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## Local Development
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To serve the website locally, run:
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```bash
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python -m http.server 8000
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```
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Then open your browser to http://localhost:8000
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## Deploying to HuggingFace Spaces
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1. Create a new Space on HuggingFace:
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- Visit https://huggingface.co/spaces
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- Click "Create new Space"
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- Choose a name for your space (e.g., "flame-benchmark")
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- Select "Static" as the Space SDK
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- Set visibility as desired (public/private)
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2. Push your code to the Space:
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```bash
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# Initialize git if not already done
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git init
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# Add the HuggingFace Space as a remote
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git remote add space https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
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# Add and commit your files
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git add .
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git commit -m "Initial commit"
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# Push to HuggingFace Spaces
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git push space main
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```
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3. Your website will be deployed at: https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
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## Required Updates Before Deployment
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1. **Convert PDF figures to web-friendly formats**:
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- Convert `content/figures/fig_overview_tech.pdf` to JPG or PNG
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- Place in `static/images/` folder
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- Update the image src in `index.html`
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2. **Update author information**:
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- Replace placeholder author names with actual authors
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- Add correct affiliations
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3. **Add links**:
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- Update the Paper link to point to your actual PDF
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- Add GitHub repository link if available
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- Add arXiv link when available
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- Add dataset link if applicable
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4. **Update BibTeX**:
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- Add the correct citation information
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## Citation
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If you find FLaME useful for your work please cite:
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```
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@article{flame2025,
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author = {Authors},
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title = {FLaME: Holistic Financial Language Model Evaluation},
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journal = {ACL},
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year = {2025},
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}
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```
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## Website License
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<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
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index.html
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<a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
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<a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
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</span>
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<a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
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<span>Paper</span>
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<span>arXiv</span>
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deforming scene using photos/videos captured casually from mobile phones.
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Our approach augments neural radiance fields
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(NeRF) by optimizing an
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additional continuous volumetric deformation field that warps each observed point into a
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canonical 5D NeRF.
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We observe that these NeRF-like deformation fields are prone to local minima, and
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propose a coarse-to-fine optimization method for coordinate-based models that allows for
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more robust optimization.
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models, we propose an elastic regularization of the deformation field that further
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photos/videos into deformable NeRF
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models that allow for photorealistic renderings of the subject from arbitrary
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viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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using a
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fidelity.
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As a byproduct of our method, we can also solve the matting problem by ignoring
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<span class="flame">FLaME</span> is the first comprehensive benchmarking suite for evaluating language models on financial NLP tasks.
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Language Models (LMs) have demonstrated impressive capabilities with core Natural Language Processing (NLP) tasks. The effectiveness of LMs for highly specialized knowledge-intensive tasks in finance remains difficult to assess due to major gaps in the methodologies of existing evaluation frameworks, which have caused an erroneous belief in a far lower bound of LMs' performance on common Finance NLP (FinNLP) tasks.
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To demonstrate the potential of LMs for these FinNLP tasks, we present the first <strong>Holistic</strong> benchmarking suite for <strong>Financial Language Model Evaluation</strong> (FLaME). We are the first research paper to comprehensively study LMs against 'reasoning-reinforced' LMs, with an empirical study of foundation LMs over core NLP tasks in finance. We open-source our framework software along with all data and results.
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Our research reveals important insights about language model performance on financial tasks:
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<li>We conducted a comprehensive evaluation of language models across multiple financial NLP tasks</li>
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<li>We identified gaps in existing evaluation frameworks that led to underestimating LM performance</li>
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<li>Our methodology provides a more accurate assessment of LM capabilities in the financial domain</li>
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<li>We found that with proper evaluation, LMs demonstrate significantly higher performance on FinNLP tasks than previously thought</li>
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Our methodological framework consists of:
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<li>A comprehensive taxonomy of financial NLP tasks</li>
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<li>Carefully selected datasets covering various financial domains</li>
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<li>Evaluation of multiple foundation language models</li>
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<li>Comparison between standard LMs and reasoning-reinforced LMs</li>
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<li>Rigorous benchmarking protocols to ensure fair comparison</li>
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We provide detailed documentation of our methodology to ensure reproducibility and facilitate future research in this area.
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<!--/ Methodology -->
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<h2 class="title is-3">Results</h2>
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Our comprehensive evaluation provides valuable insights into the performance of language models on financial NLP tasks. The results highlight the importance of proper evaluation methodologies and demonstrate that language models have significant potential for specialized financial applications.
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For detailed results, please refer to our paper. We provide full results and analysis in the accompanying documentation.
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Our work makes several key contributions to the field:
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</p>
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<ul>
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+
<li>The first holistic benchmarking suite specifically designed for financial language model evaluation</li>
|
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<li>A comprehensive taxonomy of financial NLP tasks</li>
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| 214 |
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<li>Empirical evidence challenging existing assumptions about LM performance on financial tasks</li>
|
| 215 |
+
<li>An open-source framework for evaluating language models on financial NLP tasks</li>
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| 216 |
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<li>A thorough comparison of standard and reasoning-reinforced language models</li>
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</ul>
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</div>
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</div>
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<section class="section" id="BibTeX">
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<div class="container is-max-desktop content">
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{flame2025,
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| 229 |
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author = {Authors},
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| 230 |
+
title = {FLaME: Holistic Financial Language Model Evaluation},
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| 231 |
+
journal = {ACL},
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| 232 |
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year = {2025},
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| 233 |
}</code></pre>
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href="FLaME/FLaME__ACL_AAR_Feb_2025_.pdf">
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<i class="fas fa-file-pdf"></i>
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</a>
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This website is licensed under a <a rel="license" target="_blank"
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href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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