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    <title>Language Models | Neuro-Mechanistic Modeling</title>
    <link>https://mosi.uni-saarland.de/tag/language-models/</link>
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    <description>Language Models</description>
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      <url>https://mosi.uni-saarland.de/media/icon_hu_79d10899b013012f.png</url>
      <title>Language Models</title>
      <link>https://mosi.uni-saarland.de/tag/language-models/</link>
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    <item>
      <title>Bridging the Gap – Language Models and Structured Knowledge in AI</title>
      <link>https://mosi.uni-saarland.de/teaching/24-s-bridging-the-gap/</link>
      <pubDate>Thu, 19 Sep 2024 00:00:00 +0000</pubDate>
      <guid>https://mosi.uni-saarland.de/teaching/24-s-bridging-the-gap/</guid>
      <description>&lt;h2 id=&#34;organizers&#34;&gt;Organizers&lt;/h2&gt;
&lt;p&gt;&lt;a href=&#34;https://mosi.uni-saarland.de/person/gerrit-gro%C3%9Fmann/&#34;&gt;Dr. Gerrit Großmann&lt;/a&gt;&lt;br&gt;
&lt;a href=&#34;https://www.dfki.de/web/ueber-uns/mitarbeiter/person/ceog01/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Cennet Oguz, M.Sc.&lt;/a&gt;&lt;br&gt;
&lt;a href=&#34;https://www.dfki.de/web/ueber-uns/mitarbeiter/person/sios01&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Dr. Simon Ostermann&lt;/a&gt;&lt;br&gt;
&lt;a href=&#34;https://mosi.uni-saarland.de/person/verena-wolf/&#34;&gt;Prof. Dr. Verena Wolf&lt;/a&gt;&lt;br&gt;&lt;/p&gt;
&lt;h2 id=&#34;organization&#34;&gt;Organization&lt;/h2&gt;
&lt;p&gt;For any issues regarding the seminar, please e-mail &lt;a href=&#34;https://mosi.uni-saarland.de/person/gerrit-gro%C3%9Fmann/&#34;&gt;Gerrit Großmann&lt;/a&gt; and have &lt;code&gt;[LanguageModelsSeminar2024]&lt;/code&gt; (including the brackets) in the subject line.&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;This block seminar is open to CoLi and CS students (and related courses).&lt;/li&gt;
&lt;li&gt;Depending on the study regulations, you need to register in HISPOS/LSF.&lt;/li&gt;
&lt;li&gt;CS students must have registered in HISPOS/LSF by &lt;code&gt;June 26&lt;/code&gt;! Otherwise we cannot issue a certificate (&amp;ldquo;Schein&amp;rdquo;).&lt;/li&gt;
&lt;li&gt;If you are a CS student, please use the &lt;strong&gt;&lt;a href=&#34;https://seminars.cs.uni-saarland.de/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;seminar assignment system&lt;/a&gt;&lt;/strong&gt; to register.&lt;/li&gt;
&lt;li&gt;If you are a CoLi student, please write us an email to register.&lt;/li&gt;
&lt;li&gt;Please register only if you are available at the time slot of the seminar.&lt;/li&gt;
&lt;li&gt;If you want to take the seminar but were not selected by the assignment system, please apply for the &lt;code&gt;waiting list&lt;/code&gt; by emailing us.&lt;/li&gt;
&lt;li&gt;The seminar takes place on &lt;code&gt;September 19 (Thursday) and 20 (Friday)&lt;/code&gt;, in person (10:00 sharp, DFKI, D3.2, room &lt;em&gt;Reuse&lt;/em&gt;, next to the main entrance).&lt;/li&gt;
&lt;li&gt;The seminar language is English.&lt;/li&gt;
&lt;li&gt;The seminar earns you 7 ECTS.&lt;/li&gt;
&lt;/ul&gt;
&lt;h3 id=&#34;grading&#34;&gt;Grading&lt;/h3&gt;
&lt;p&gt;To pass the seminar, you have to attend all sessions and:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;give a presentation (CoLi and CS studenets, worth 3 ECTS);&lt;/li&gt;
&lt;li&gt;write reports in which you critically examine the topics in the seminar (only CS students, worth 4 ECTS);&lt;/li&gt;
&lt;li&gt;participate in discussions during the seminar;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&amp;hellip; with a passing grade.&lt;/p&gt;
&lt;p&gt;For CS students, the final grade is calculated from the weighted average.
Both CoLi and CS students have the opportunity to earn a &lt;strong&gt;bonus&lt;/strong&gt; for submitting an optional practical project. A good project can improve your final grade by 0.3 points (e.g., from 1.7 to 1.3), an excellent project can boost your grade by 0.7 points (e.g., from 2.3 to 1.7).&lt;/p&gt;
&lt;h2 id=&#34;topic-overview&#34;&gt;Topic Overview&lt;/h2&gt;
&lt;p&gt;This is a block seminar in September 2024, which will be held in cooperation between the Department of &lt;a href=&#34;https://www.dfki.de/web/forschung/forschungsbereiche/sprachtechnologie-und-multilingualitaet&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Multilingual Technologies (MLT)&lt;/a&gt; and &lt;a href=&#34;https://www.dfki.de/web/forschung/forschungsbereiche/neuro-mechanistische-modellierung&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Neuro-Mechanistic Modeling (NMM)&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Large language models (LLMs) have swiftly become a cornerstone in AI research, capturing the attention of the public as the most accessible gateway to artificial intelligence. Despite their groundbreaking impact, LLMs are not without their imperfections. Notably, the occurrence of hallucinations and limited reasoning capabilities, particularly in specialized domains, remain significant challenges.&lt;/p&gt;
&lt;p&gt;This seminar begins by investigating the theoretical foundations of language representations, tracing the evolution of transformers and their progression towards the cutting-edge LLMs we see today. Building on this foundation, the seminar will then explore promising future directions. Special emphasis will be placed on the integration of LLMs with neuro-symbolic reasoning and the enrichment of these models through knowledge graphs and other forms of structured data.&lt;/p&gt;
&lt;h3 id=&#34;requirements&#34;&gt;Requirements&lt;/h3&gt;
&lt;p&gt;We expect no prior knowledge in language modeling. The seminar is open to CS (including related majors) and CoLi students.&lt;/p&gt;
&lt;h2 id=&#34;course-structure&#34;&gt;Course Structure&lt;/h2&gt;
&lt;h3 id=&#34;presentation&#34;&gt;Presentation&lt;/h3&gt;
&lt;p&gt;Identify the key ideas and concepts and give a self-consistent presentation explaining concepts to your
fellow students.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;The presentation should be about 20 minutes long.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Please submit your your final slides as &lt;em&gt;.pdf&lt;/em&gt; until &lt;code&gt;September 18&lt;/code&gt; (23:59, Berlin time) and have &lt;code&gt;[LanguageModelsSeminar2024] Slides Submission&lt;/code&gt; in the subject line.&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Here are some suggestions for a good presentation (we will use this as a basis for grading the presentations):&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Do not try to condense all the content into 20 minutes. Find a suitable level of abstraction and determine what you think is interesting for your fellow students.&lt;/li&gt;
&lt;li&gt;Explore and include supplementary material where it seems useful (literature, youtube, GitHub, medium articles, OpenReview, etc.). The papers provided by us can be seen as a starting point.&lt;/li&gt;
&lt;li&gt;The goal is to tell a (self-consistent and entertaining) story - not to convince us that you understand the paper.&lt;/li&gt;
&lt;li&gt;Put time and effort into creating visualizations and preparing (running) examples (Please do not rely solely on paper screenshots).&lt;/li&gt;
&lt;li&gt;Prioritize concreteness, simplicity, and clarity.&lt;/li&gt;
&lt;li&gt;Focus on intuition, high-level understanding, and contextualization, not on technical details.&lt;/li&gt;
&lt;li&gt;Don&amp;rsquo;t overcrowd your slides. Try to avoid full sentences and be cautious with bullet points.&lt;/li&gt;
&lt;li&gt;Use equations only when necessary; use color-coded equations to improve their readability (&lt;a href=&#34;https://github.com/st--/annotate-equations&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;example&lt;/a&gt;).&lt;/li&gt;
&lt;li&gt;Be critical of the authors&amp;rsquo; claims, don&amp;rsquo;t fall for overselling.&lt;/li&gt;
&lt;li&gt;Use slide numbers.&lt;/li&gt;
&lt;/ul&gt;
&lt;h3 id=&#34;reports&#34;&gt;Reports&lt;/h3&gt;
&lt;p&gt;Each CS student is required to write six reports on six different topics of their choosing (this can include your own topic).
Each report should be about two to four pages long.
The report should contain a short(!) summary of (what you consider to be) the main contribution or most intriguing idea of the papers. Otherwise, you can freely express your own thoughts on the topic. For instance: What did you like/dislike about the papers (both methodically and didactically)? What are connections to other seminar papers? Can you suggest improvements? What do you think is missing?&lt;/p&gt;
&lt;p&gt;You can use the &lt;a href=&#34;https://de.overleaf.com/latex/templates/neurips-2022/kxymzbjpwsqx&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Neurips&lt;/a&gt; or &lt;a href=&#34;https://de.overleaf.com/latex/templates/springer-lecture-notes-in-computer-science/kzwwpvhwnvfj&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Springer LNCS&lt;/a&gt; Latex template or any other reasonable format (don&amp;rsquo;t write an abstract).
Please use a spell+grammar checker like &lt;a href=&#34;https://languagetool.org/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;languagetool&lt;/a&gt; or &lt;a href=&#34;https://app.grammarly.com/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;grammarly&lt;/a&gt; before submitting.
Do not let ChatGPT do the writing for you (it is horrible to read). But you can use LLMs to catch errors or improve formulations.&lt;/p&gt;
&lt;p&gt;The time required to write the reports should approximately add up to 4 CP, which translates to 20 hours of work per report (4 CP x 30 hours per CP).&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Please email the reports as a single .pdf file (with a reasonable filename) to Gerrit and Cennet and have &lt;code&gt;[LanguageModelsSeminar2024] Report Submission&lt;/code&gt; in the subject line.&lt;/strong&gt;
The report deadline is &lt;code&gt;September 28&lt;/code&gt; (23:59, Berlin time).&lt;/p&gt;
&lt;h3 id=&#34;practical-project-bonus&#34;&gt;Practical Project (Bonus)&lt;/h3&gt;
&lt;p&gt;You can submit a practical project where you apply what you learned in the seminar. The project doesn&amp;rsquo;t have to be closely related to your presentation topic but should have a loose connection to any seminar topic. If you plan to do this, please discuss it with your supervisor before September 19 and propose your idea.&lt;/p&gt;
&lt;p&gt;Practical projects previously submitted in other university courses or already published are not allowed. Plagiarism will result in expulsion. Ensure that the code is reproducible and easy to run, for example, on Colab.
Your project also needs to be well documented (e.g., by providing a tutorial notebook), and you need to disclose all the resources you used.&lt;/p&gt;
&lt;p&gt;Deadline is &lt;code&gt;October 4&lt;/code&gt; (23:59, Berlin time).
We will make all projects publically available.
&lt;strong&gt;Please email the project as a single .zip file (with a reasonable filename) to Gerrit and Cennet and have &lt;code&gt;[LanguageModelsSeminar2024] Project Submission&lt;/code&gt; in the subject line.&lt;/strong&gt;&lt;/p&gt;
&lt;h2 id=&#34;topics&#34;&gt;Topics&lt;/h2&gt;
&lt;p&gt;&lt;del&gt;Please vote for your preferred topic: &lt;strong&gt;&lt;a href=&#34;https://forms.gle/___V8rUSV9BRYgKfUKFA&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;here&lt;/a&gt;&lt;/strong&gt;.
Deadline is &lt;code&gt;May 25&lt;/code&gt;.&lt;/del&gt;&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;Topic 1&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Pre-Attention Language Models&lt;/li&gt;
&lt;li&gt;Student: Mohammad Munem Shahriar&lt;/li&gt;
&lt;li&gt;Supervisor: Cennet&lt;/li&gt;
&lt;li&gt;Papers:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1409.3215&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Sequence to Sequence Learning with Neural Networks&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;A Neural Probabilistic Language Model&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1508.06615&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Character-Aware Neural Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://aclanthology.org/N18-1202.pdf&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Deep contextualized word representations&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 2&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Self-Attention and Transformer&lt;/li&gt;
&lt;li&gt;Student: Mayur Deshmukh&lt;/li&gt;
&lt;li&gt;Supervisor: Cennet&lt;/li&gt;
&lt;li&gt;Papers:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1902.02181&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Attention in Natural Language Processing&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1706.03762&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Attention Is All You Need&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1906.06755&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Theoretical Limitations of Self-Attention in Neural Sequence Models&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 3&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Bert and its Variants&lt;/li&gt;
&lt;li&gt;Student: Duong Nguyen&lt;/li&gt;
&lt;li&gt;Supervisor: Cennet&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1810.04805&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1907.11692&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;RoBERTa: A Robustly Optimized BERT Pretraining Approach&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/1911.02116&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Unsupervised Cross-lingual Representation Learning at Scale&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 4&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Foundations of LLMs&lt;/li&gt;
&lt;li&gt;Student: -&lt;/li&gt;
&lt;li&gt;Supervisor: Cennet&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2203.02155&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Training language models to follow instructions with human feedback&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2303.08774&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;GPT-4 Technical Report&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2211.09085&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Galactica: A Large Language Model for Science&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 5&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Properties of LLMs&lt;/li&gt;
&lt;li&gt;Student: Vianney Jerry Tchouwa Takou&lt;/li&gt;
&lt;li&gt;Supervisor: Cennet&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2001.08361&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Scaling Laws for Neural Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2203.15556&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Training Compute-Optimal Large Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2005.14165&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Language Models are Few-Shot Learners&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 6&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Multimodal LLMs&lt;/li&gt;
&lt;li&gt;Student: Yannick Gansen&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://proceedings.mlr.press/v32/kiros14.pdf&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Multimodal Neural Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2204.06125&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Hierarchical Text-Conditional Image Generation with CLIP Latents&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2304.08485&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Visual Instruction Tuning&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 7&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Evaluating LLMs&lt;/li&gt;
&lt;li&gt;Student: -&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2206.04615&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2312.11539&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;KGLens: A Parameterized Knowledge Graph Solution to Assess What an LLM Does and Doesn&amp;rsquo;t Know&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2303.17548&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Whose Opinions Do Language Models Reflect?&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 8&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Foundations of Knowledge Graph Reasoning&lt;/li&gt;
&lt;li&gt;Student: Larisa Ivanova&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2106.06935&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2310.04562&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Towards Foundation Models for Knowledge Graph Reasoning&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 9&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Graphs as Input&lt;/li&gt;
&lt;li&gt;Student: Cicy Kuriakose Agnes&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2205.08184&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;SKILL: Structured Knowledge Infusion for Large Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2402.07630&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2311.13314&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 10&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Graphs as Output&lt;/li&gt;
&lt;li&gt;Student: Mini Mariya Thomas&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2308.13916&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Exploring Large Language Models for Knowledge Graph Completion&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2307.01128&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2402.15301&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 11&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: LLM-Based Reasoning on Graphs&lt;/li&gt;
&lt;li&gt;Student: Michael Schott&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2310.01061&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2401.07105&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Graph Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2402.01805v1&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Exploring the Limitations of Graph Reasoning in Large Language Models&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;&lt;strong&gt;Topic 12&lt;/strong&gt;&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;Name: Learning to Prompt&lt;/li&gt;
&lt;li&gt;Student: Prakhar Kumar&lt;/li&gt;
&lt;li&gt;Supervisor: Gerrit&lt;/li&gt;
&lt;li&gt;Paper:
&lt;ol&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2211.11890&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;TEMPERA: Test-Time Prompting via Reinforcement Learning&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2210.03629&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;ReAct: Synergizing Reasoning and Acting in Language Models&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/pdf/2404.05449&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;RoT: Enhancing Large Language Models with Reflection on Search Trees&lt;/a&gt;&lt;/li&gt;
&lt;/ol&gt;
&lt;/li&gt;
&lt;/ul&gt;
&lt;h3 id=&#34;recommended-readingwatch-list&#34;&gt;Recommended Reading/Watch List&lt;/h3&gt;
&lt;h4 id=&#34;llm-primer&#34;&gt;LLM Primer&lt;/h4&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=zjkBMFhNj_g&amp;amp;ab_channel=AndrejKarpathy&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Andrej Karpathy - Intro to Large Language Models&lt;/a&gt; This is a classic.&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.promptingguide.ai/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;promptingguide&lt;/a&gt;: Nice and high-level overview on LLMs.&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=wjZofJX0v4M&amp;amp;ab_channel=3Blue1Brown&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;But what is a GPT? Visual intro to transformers - Chapter 5, Deep Learning&lt;/a&gt; See also the next chapters.&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=kCc8FmEb1nY&amp;amp;t=2081s&amp;amp;ab_channel=AndrejKarpathy&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Let&amp;rsquo;s build GPT: from scratch, in code, spelled out&lt;/a&gt; Karpathy again.&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://arxiv.org/abs/2404.14928&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Graph Machine Learning in the Era of Large Language Models (LLMs)&lt;/a&gt; A neat review, discussing many topics relevant to this seminar.&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/rasbt/LLMs-from-scratch&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;LLMs from scratch (Sebastian Raschka)&lt;/a&gt; with &lt;a href=&#34;https://youtu.be/quh7z1q7-uc?si=7QvktIUkFHAl312e&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;video&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;h4 id=&#34;project-inspirations-and-resources&#34;&gt;Project Inspirations and Resources&lt;/h4&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://huggingface.co/docs/transformers/v4.17.0/en/index&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;HuggingFace Transformers Tutorials&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/Lightning-AI/litgpt&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;⚡ LitGPT&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/stanfordnlp/dspy&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;DSPy&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/maitrix-org/llm-reasoners&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;llm-reasoners&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=IgcBuXFE6QE&amp;amp;ab_channel=AllAboutAI&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;From OpenAI to Open Source in 5 Minutes Tutorial (LM Studio + Python)&lt;/a&gt; or &lt;a href=&#34;https://medium.com/@ingridwickstevens/streaming-local-llm-responses-with-lm-studio-inference-server-cce3f78b2522&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Streaming Local LLM Responses with LM Studio Inference Server&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Project Inspirations&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;a href=&#34;https://www.reddit.com/r/LocalLLaMA/comments/18se771/any_interesting_llm_project_ideas/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Interesting LLM project ideas&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://pubs.rsc.org/en/content/articlehtml/2023/dd/d3dd00113j&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;14 examples of how LLMs can transform materials science and chemistry&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/yoheinakajima/instagraph&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;instagraph&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/VinciGit00/Scrapegraph-ai&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Scrapegraph-ai&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://github.com/sfreagin/arXiv_explorer/&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;arXiv_explorer&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=h_GTxRFYETY&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;LLM Finance Planer&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://www.youtube.com/watch?v=a91EPM7TBX4&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;LLM powered Game&lt;/a&gt;&lt;/li&gt;
&lt;li&gt;&lt;a href=&#34;https://cloud.dify.ai/explore/apps&#34; target=&#34;_blank&#34; rel=&#34;nofollow noopener&#34;&gt;Dify examle apps (requires registration)&lt;/a&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;!---

|     -   |    [Bayesian Flow Networks](https://arxiv.org/abs/2308.07037)    |
--&gt;
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