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Yingshan Chang @EMNLP🌴

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Pinned

Can a Transformer count inductively? ▶️ Yes, but different schema for positional embeddings are required for different forms of counting. Can we treat counting as a primitive operation of Transformer computation? ▶️ No, because it requires a non-trivial computation budget and…


Yingshan Chang @EMNLP🌴 Reposted

OMG, this is killing me 😂😂😂

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Yingshan Chang @EMNLP🌴 Reposted

Just landed in Miami to attend #EMNLP2024 🐊 I’ll be presenting the poster of our “Tools fail” paper on Wednesday Nov 13th, 16:00-17:30 at Jasmine — come check out our poster for a chat!

Tools augment LLMs but can also introduce errors without explicit messages. Can LLMs detect these "silent" tool-based errors? We investigate this challenge and present an initial approach to failure recovery. Work w/ @SoYeonTiffMin @_Yingshan @ybisk 🗞️ arxiv.org/abs/2406.19228

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Yingshan Chang @EMNLP🌴 Reposted

Our method breaks down the Mutual Information into the Redundancy (R), Synergy (S), and Uniqueness (U) of the conditioning tokens. R is the redundant info from multiple tokens, S is the info from token interactions, and U is the unique information from each token. 4/n


Yingshan Chang @EMNLP🌴 Reposted

Diffusion models have advanced significantly, but how well do we understand their workings? How do textual tokens impact output, and where do biases and failures occur? In our @NeurIPS 2024 paper, we introduce DiffusionPID to answer these questions and more. #neurips2024 1/n


Yingshan Chang @EMNLP🌴 Reposted

We introduce Situated Instruction Following (SIF), to appear in ECCV 2024! There is inherent underspecification in instructions when humans act as they speak. SIF is addresses these dynamic, temporally evolving intent of instructions in the context of physical human actions.(1/7)

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Yingshan Chang @EMNLP🌴 Reposted

I’m thrilled to be joining @CarnegieMellon’s Machine Learning Department (@mldcmu) as an Assistant Professor this Fall! My lab will work at the intersection of neuroscience & AI to reverse-engineer animal intelligence and build the next generation of autonomous agents. Learn…

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Yingshan Chang @EMNLP🌴 Reposted

❓Are there any unique advantages of diffusion-based LMs over autoregressive LMs? ❓Can we scale and instruction-tune diffusion LMs? ​ Presenting "David helps Goliath: Inference-Time Collaboration Between Small Specialized and Large General Diffusion LMs" at #NAACL2024! ​ 📖…

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Yingshan Chang @EMNLP🌴 Reposted

Something to read for the weekend: link.springer.com/epdf/10.1007/s…

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Yingshan Chang @EMNLP🌴 Reposted

The average overworked PhD student

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Yingshan Chang @EMNLP🌴 Reposted

LLMs excel in math. Introducing a new benchmark, we observe: They struggle with creative and many-step questions (even with CoT), their performance varies widely even on similar topics, and they engage in genuine reasoning only in about half of cases. 1/n arxiv.org/abs/2406.05194

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Yingshan Chang @EMNLP🌴 Reposted

We should call models like Llama 3, Mixtral, etc. “open-weight models”, not “open-source models”. For a model to be open-source, the code and training data need to be public (good examples: GPT-J, OLMo, RedPajama, StarCoder, K2, etc.). Weights are like an exe file, which would be…


Yingshan Chang @EMNLP🌴 Reposted

So excited to see this fascinating work by my labmate Artem🤩. This is an inspiration for everyone who loves animal 🤩. arxiv.org/abs/2404.18739

🎞 Prof. @radamihalcea appeared on @CBSDetroit to discuss a new #AI tool used to interpret the meaning behind a dog's bark. 🔽🔊 Hear what she has to say on this innovative way of connecting with our pets! youtube.com/watch?v=nToCDO…



Yingshan Chang @EMNLP🌴 Reposted

1/We've nailed a framework to reliably detect if an LLM was trained on your dataset: LLM Dataset Inference. After over a year of thinking of writing about how hard this is, we had a breakthrough that made me quite literally jump from my seat! 📝: arxiv.org/abs/2406.06443 Long🧵

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Yingshan Chang @EMNLP🌴 Reposted

[LG] How Far Can Transformers Reason? The Locality Barrier and Inductive Scratchpad E Abbe, S Bengio, A Lotfi, C Sandon, O Saremi [Apple & EPFL] (2024) arxiv.org/abs/2406.06467 - Transformers can be Turing-complete in expressivity, but this does not address learnability. This…

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Yingshan Chang @EMNLP🌴 Reposted

Looking for the best color palette? 😔 Check the tool we just created with @joseph_barbier: 🎨 2500+ palettes 🐍 Python Library to get them 🔍 Easy-to-use application to find your perfect match python-graph-gallery.com/color-palette-… Feedback welcome, we're working hard on this right now!


Yingshan Chang @EMNLP🌴 Reposted

We are looking for more reviewers for the Cognitive Modeling and Computational Linguistics Workshop (CMCL @ ACL 2024). The deadline for reviews is June 25. Please contact me or cmclorganizers2024@gmail.com if you would like to be a reviewer! cmclorg.github.io #nlproc


Yingshan Chang @EMNLP🌴 Reposted

I'm thrilled to share that I will become the next Director of the Machine Learning Department at Carnegie Mellon. MLD is a true gem, a department dedicated entirely to ML. Faculty and past directors have been personal role models in my career. cs.cmu.edu/news/2024/kolt…


Yingshan Chang @EMNLP🌴 Reposted

📌 This paper investigates the dramatic breakdown of state-of-the-art LLMs' reasoning capabilities when confronted with a simple common sense problem called the "Alice In Wonderland (AIW) problem". This is despite their strong performance on standardized reasoning benchmarks.…

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Yingshan Chang @EMNLP🌴 Reposted

Excited to share that our paper "Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs" has been accepted to #acl2024 findings. We just released an updated version: arxiv.org/pdf/2402.12424

🚀 Excited to share our latest work: "Tables as Images? Exploring the Strengths and Limitations of LLMs on Multimodal Representations of Tabular Data" on #MLLMs #LLMs & tabular data! We explore image vs. text representations & their impact on model performance



Yingshan Chang @EMNLP🌴 Reposted

Wrote up some thoughts on a growing problem I see with HCI conference submissions: the influx of what can only be called LLM wrapper papers, and what we might do about it. Here is "LLM Wrapper Papers are Hurting HCI Research": ianarawjo.medium.com/llm-wrapper-pa…


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