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            <h2 class="col-md-12 text-center">
                <b><font size="+4">Neuron to Graph</font></b>
                <br>
                <b><font size="+2">Interpreting Language Model Neurons at Scale</font></b>
            </h2>
            </br> 
                <!--<small>
                    CoRL 2021
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                <ul class="list-inline">
                <br>
                <li>Alex Foote<sup>1</sup>*, Neel Nanda<sup>2</sup>, Esben Kran<sup>1</sup>, Ionnis Konstas<sup>3</sup>, Shay Cohen<sup>4</sup>, Fazl Barez<sup>1,4,5</sup>*</li>
                <br><br>
                    <!-- <a href="http://g.co/robotics">
                    <image src="img/robotics-at-google.png" height="40px"> Apart Research</a>
                    <a href="https://everydayrobots.com">
                    <image src="img/EverydayRobots2.gif" height="40px"> Everyday Robots</a> <br><br> -->
                    <sup>1</sup>Apart Research&#8287;&#8287;<sup>2</sup>Independent&#8287;&#8287;<sup>3</sup>School of Mathematical and Computer Sciences Heriot-Watt University&#8287;&#8287;<sup>4</sup>School of Informatics, University of Edinburgh&#8287;&#8287;<sup>5</sup>University of Oxford <br> * Equal contribution
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                        <li>
                            <a href="https://arxiv.org/abs/2305.19911">
                            <image src="img/paper-tb.png" height="60px" width="40px">
                                <h4><strong>arXiv</strong></h4>
                            </a>
                        </li>
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                            <a href="https://youtu.be/ysFav0b472w">
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                                <h4><strong>Video</strong></h4>
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                        <li>
                            <a href="https://ai.googleblog.com/2022/08/towards-helpful-robots-grounding.html">
                            <image src="img/google-ai-blog-small.png" height="60px">
                                <image src="img/new.png" height="20px" class="imtip">
                                <h4><strong>Blogpost</strong></h4>
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                         <li>
                            <a href="https://github.com/google-research/google-research/tree/master/saycan">
                            <image src="img/github.png" height="60px">
                                <h4><strong>Code</strong></h4>
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                <h3>
                    What's New
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                <p class="text-justify">

                <ul>
                    <li> <font color="#5a00b4">[8/16/2022]</font> We integrated SayCan with <a href="https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html">Pathways Language Model (PaLM)</a>, and updated the results. We also added <a href="#new-capability"> new capabilities</a> including drawer manipulation, chain of thought prompting and multilingual instructions. You can see all the new results in the updated <a href="assets/Neuron2Graph.pdf">paper</a>.</li>
                    <li><font color="#5a00b4">[8/16/2022]</font> Our updated results show that SayCan combined with the improved language model (PaLM), which we refer to as PaLM-SayCan, improves the <b>robotics performance</b> of the entire system compared to a previous LLM (FLAN). PaLM-SayCan chooses the correct sequence of skills 84% of the time and executes them successfully 74% of the time, reducing errors by a half compared to FLAN.  This is particularly exciting because it represents the first time we can see how an improvement in language models translates to a similar improvement in robotics.  </li>
                    <li><font color="#5a00b4">[8/16/2022]</font> We <a href="#open-source">open-sourced</a> a version of SayCan on a simulated tabletop environment. </li>
                    <li> [4/4/2022] Initial release of SayCan. </li>
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                <h3>
                    Abstract
                </h3>
                <p class="text-justify">
                    Understanding the function of individual neurons within language models is essential for mechanistic interpretability research. We propose Neuron to Graph
(N2G), a tool which takes a neuron and its dataset examples, and automatically
distills the neuron's behaviour on those examples to an interpretable graph. This
presents a less labour intensive approach to interpreting neurons than current manual methods, that will better scale these methods to large language models (LLMs). We use truncation and
saliency methods to only present the important tokens, and augment the dataset examples with more diverse samples to better capture the extent of neuron behaviour.
These graphs can be visualised to aid manual interpretation by researchers, but
can also output token activations on text to compare to the neuron's ground truth
activations for automatic validation. N2G represents a step towards scalable interpretability methods by allowing us to convert neurons in an LLM to interpretable
representations of measurable quality.
                </p>
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                    Video
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<p style="text-align:center;">
    <image src="img/architecture.png"  class="img-responsive" height="600px">
        <i>Figure 1: Overall architecture of N2G. Activations of the target neuron on the dataset examples
are retrieved (neuron and activating tokens in red). Prompts are pruned and the importance of each
token for neuron activation is measured (important tokens in blue). Pruned prompts are augmented
by replacing important tokens with high-probability substitutes using BERT. The augmented set of
prompts are converted to a graph. The output graph is a real example which activates on the token
“except” when preceded by any of the other tokens.</i>

</p>

<br>
<p style="text-align:center;">
    <image src="img/example_with.png"  class="img-responsive" height="600px">
        <i>Figure 2: An example of a graph built from Neuron 2 of Layer 1 of the model.</i>

</p>
<br>
<p style="text-align:center;">
    <image src="img/eval_table.png"  class="img-responsive" height="600px">
        <i>Table 1: Precision, recall and F1-score of the neuron graphs' token-level predictions of neuron firing
            compared to ground truth on held-out test data, for 50 random neurons from each layer of the model.
            Tokens on which the real neuron fired and tokens on which it didn't fire are evaluated separately as
            there are generally many more tokens on which a neuron didn't fire, making it trivially easy to get
            near-perfect scores by always predicting the neuron will not fire.</i>

</p>
            
<br><br>

<p><b><i>Work in progress</i></b></p>

<br><br>

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                <h3>
                    Results
                </h3>
		<p class="text-justify">
		We found that when we ran the code, the results came out.
		</p>
		
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                    <image src="img/graph_10_examples_n_20.png"  class="img-responsive" height="600px">
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                <h3>
                    Citation 
                </h3> <a href="https://arxiv.org/abs/2204.01691">[arxiv version]</a>
                <div class="form-group col-md-10 col-md-offset-1">
                    <textarea id="bibtex" class="form-control" readonly>
@inproceedings{acl2023interpretllmn,
    title={Interpreting Large Language Model Neurons},
    author={Anonymous authors},
    booktitle={ACL submission},
    year={2023}
}</textarea>
                </div>
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        </div> -->


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                <h3>
                    <font color="#5a00b4">Models used in the paper</font>
                </h3>
               <font color="#5a00b4">The models are available...<a href="https://huggingface.co/gpt2">[gpt-2]</a></font>
            </div>
            <br/><br/>
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            <div id="open-source" class="col-md-8 col-md-offset-2">
                <h3>
                    <font color="#5a00b4">Open Source</font>
                </h3>
              <font color="#5a00b4">We open source a version of SayCan that works with a simulated tabletop environment. <a href="https://github.com/google-research/google-research/tree/master/saycan">[tabletop saycan] </a> </font>
              <p style="text-align:center;">
                    <img src="img/open_source_tabletop.png" class="img-responsive" height="600px">
                </p>
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                <h3>
                    Acknowledgements
                </h3>
                <p class="text-justify">
The authors would like to thank Fred Alcober, Yunfei Bai, Matt Bennice, Maarten Bosma, Justin Boyd, Bill Byrne, Kendra Byrne, Noah Constant, Pete Florence, Laura Graesser, Rico Jonschkowski, Daniel Kappler, Hugo Larochelle, Benjamin Lee, Adrian Li, Maysam Moussalem, Suraj Nair, Jane Park, Evan Rapoport, Krista Reymann, Jeff Seto, Dhruv Shah, Ian Storz, Razvan Surdulescu, Tom Small, Jason Wei, and Vincent Zhao for their help and support in various aspects of the project.
                    <br><br>
                The website template was borrowed from <a href="http://jonbarron.info/">Jon Barron</a>.
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