Taming LLM workshop

The 1st Workshop on Taming Large Language Models: Controllability in the era of Interactive Assistants

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📚 Calling all researchers & practitioners! Our workshop invites submissions that contribute to controlled language generation. Share your improvements, analysis, surveys, system demos, and practical system reports. #CallForPapers

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📣 Excited to announce the 1st Workshop on Taming Large Language Models: Controllability in the Era of Interactive Assistants! Join us as we explore the rapidly evolving LLM and the challenges of ensuring controllable NLG systems. @sigdial & @inlgmeeting

https://t.co/oTKZ7lBEER

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance!
Zoom: https://t.co/DhokuAjm2l
Mark your calendars for an inspiring keynote with @tydsh: Understanding Training Dynamics in 1-layer Transformer.

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance! Zoom: https://t.co/DhokuAjm2l
Honored to have @ericmalmi, give a keynote on Fast Text Generation with Text-Editing models.😀 #TLLM2023

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance! Zoom: https://t.co/DhokuAjm2l
Excited to announce @violetnpeng will be a keynote speaker at our workshop. We can't wait to hear her insights! @sigdial

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance! Zoom: https://t.co/DhokuAjm2l
Join us as @nancyfchen1 shares her wisdom on controllable text generation at our #TLLM2023. @sigdial @inlgmeeting

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance! Zoom: https://t.co/DhokuAjm2l
Looking forward to @daphneipp discussing her work at #TLLM2023. Catch her in-person keynote!

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📢 Announcing the 1st Workshop on Taming Large Language Models #SIGDIALxINLG2023 Join us on Sep. 12 with free virtual attendance! Zoom: https://t.co/DhokuAjm2l
Thrilled to have @jotyshafiq as a keynote speaker in #TLLM2023 Hear his insights and Don't miss it!

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Our workshop is 2 days away! Thank you for your participation in the TLLM workshop and we are looking forward to seeing you on September 12th.

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We have extended our deadline to July 7, if you missed (or will miss) the EMNLP deadline, welcome to submit to the 1st Workshop on Taming Large Language Models!

[AK @_akhaliq]

Meta AI released Scaling Speech Technology to 1,000+ Languages

demo: https://t.co/uZNTywU6HB

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.

[Percy Liang @percyliang]

In HELM, we evaluated language models. Now, we evaluate organizations that build language models. Just like model evaluations incentivize improvement in model quality, we hope that these evaluations will incentivize improvement in development and deployment practices.

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@Swarooprm7 @XiangruTang seems very interesting

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@arankomatsuzaki great work!

[Aran Komatsuzaki @arankomatsuzaki]

Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

Presents a comprehensive dataset of 4,550 questions and solutions from all MIT EECS courses required for obtaining a degree

https://t.co/cZfjNRu4AL

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Our call for papers is on aclweb! 🔥
Thanks to @hdevamanyu
#ACL #INLG @inlgmeeting
https://t.co/FmveqfImKG

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Thank you to @ZhiruoW, @YilunZhao_NLP, @yichenjiang9, @prakhariitr, and Guo Zhang from MIT for accepting our invitation to join our Program Committee! 🎉🙌

Your expertise and insights will be invaluable.#ProgramCommittee #AcademicExcellence

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Only 10 days left, submit now! 🚀
Just 10 days until the submission deadline! Don't miss out on this amazing workshop!
#NLProc #LLMs

[Taming LLM workshop @ctrl_nlg]

Only 10 days left, submit now! 🚀
Just 10 days until the submission deadline! Don't miss out on this amazing workshop!
#NLProc #LLMs

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https://t.co/lxf9Q95jOQ

[AK @_akhaliq]

What did you do with generative AI?

[Together AI @togethercompute]

Announcing RedPajama 7B trained on 1T tokens! 🚀

• Instruct, chat, base, and interim checkpoints on
@huggingface
• The instruct model outperforms all open 7B models on HELM benchmarks
• The 5TB dataset has been used to train over 100 models

Details👇

https://t.co/oUNKqYBmlS

[AK @_akhaliq]

PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge

paper page: https://t.co/9a9Oz3RGCQ

probe ChatGPT for its conversational understanding and introduce a conversational framework (protocol) that can be adopted in future studies. The Pok'emon universe serves as an ideal testing ground for auditing ChatGPT's reasoning capabilities due to its closed world assumption. After bringing ChatGPT's background knowledge (on the Pok'emon universe) to light, we test its reasoning process when using these concepts in battle scenarios. We then evaluate its ability to acquire new knowledge and include it in its reasoning process. Our ultimate goal is to assess ChatGPT's ability to generalize, combine features, and to acquire and reason over newly introduced knowledge from human feedback. We find that ChatGPT has prior knowledge of the Pokemon universe, which can reason upon in battle scenarios to a great extent, even when new information is introduced. The model performs better with collaborative feedback and if there is an initial phase of information retrieval, but also hallucinates occasionally and is susceptible to adversarial attacks.