The Program in Writing and Rhetoric
Invites you to
A Rhetorical/Deliberative Framework for AI Language Model Alignment
featuring
Prof Zoltan Majdik Professor
North Dakota State University
In this talk, Prof. Majdik proposes a framework for aligning LLMs with values grounded in the norms of rhetorical culture and deliberative democracy. Alongside long-standing AI alignment value targets like safety and transparency, this AI alignment framework assesses to what extent a language model exhibits human and humane values that foster communicative engagement, and it codifies approaches to tuning existing models to better align with such values.

Location: Humanities 1008
Abstract: Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search agents via reinforcement learning (RL), improving information acquisition through multi-turn interactions with retrieval engines. However, existing approaches either optimize retrieval using search-only metrics (e.g., NDCG) that ignore downstream utility or fine-tune the entire LLM to jointly reason and retrieve--entangling retrieval with generation and limiting the real search utility and compatibility with frozen or proprietary models. In this work, we propose s3, a lightweight, model-agnostic framework that decouples the searcher from the generator and trains the searcher using a Gain Beyond RAG reward: the improvement in generation accuracy over naïve RAG. s3 requires only 2.4k training samples to outperform baselines trained on over 70 × more data, consistently delivering stronger downstream performance across six general QA and five medical QA benchmarks.

Speaker: Peter Zeng

Location: CS2311
Title: Class visual similarity based noisy sample removal in generative Few Shot Learning
Time: Thursday, Feb 4, 11:30am - 1:00pm
Zoom:
https://stonybrook.zoom.us/j/8563646526?pwd=anJna1gzUStXNlNVSUIzdDRUSC9CUT09

Meeting ID: 856 364 6526
Passcode: 203791



Abstract:  

Over the past decade, larger datasets, hardware accelerations, and network architecture improvements have contributed to phenomenal achievements in many tasks of computer

vision. However, in the absence of large datasets, computer vision models struggle to learn

general representations which results in poor performance. Few-shot learning tries to address 

this problem by proposing models which learn from a few examples.


I first give an overall review of few-shot learning methods. I particularly focus on generative Few Shot Learning(FSL) methods, which augment the scarce categories in a dataset by generating samples for those rare categories. As the actual class distribution can be complex and lie very close to each other, the sample generated for one class can be noisy or lie close to another class.  However, none of the current FS generative methods perform any form of quality control of the generated samples.


In this work, I propose to identify and remove the generated samples that are less likely to be in the distribution of the few-shot class. Here I particularly deal with few-shot scenarios where the

prior information of the relationship between the classes based on visual  similarity is available. The main idea is to exploit these priors to better identify the unreliable generated samples.


Particularly, I have proposed two methods based on class relationship to detect noisy generated samples. In the first method, we assume that the embedding space of each class follows a Gaussian distribution.  From this assumption, I propose Gaussian Neighborhood (GN), a method to estimate how likely a generated sample is drawn from the estimated distribution of a few-shot class.  We evaluate this method on the Hematopoiesis dataset.  By simply eliminating samples based on thresholding our proposed GN scores, the few-shot  classification  performance  is  improved by 5% and 2% in five shot and one shot respectively, compared to the model trained on all generated images. 


The GN scores represent the similarity distances from the generated samples to their classes, based on the assumption that each class is a Gaussian distribution.  However, this assumption might be strict in many scenarios since the real distributions of data can be arbitrarily complex.  Thus in my second proposed method, I aim to learn such similarity distances directly from data via metric learning. I propose to train a deep-network to regress the similarity distance between a pair of samples.  This network is trained using both the class-level  visual  similarity  information  and  the  class  labels.   This method improves the 1-shot and 5-shot classification performances by 0.5% and 1% respectively, compared to GN.

The Challenges of Machine Learning in Adversarial Settings by Patrick McDaniel, Pennsylvania State University

Abstract: Advances in AI and machine learning have enabled new applications and services to interpret and process inputs in previously unthinkable complex environments. Autonomous cars, data analytics, adaptive communication and self-aware software systems are now revolutionizing markets by achieving or exceeding human performance. In this talk, I consider the evolving use of machine learning in security-sensitive contexts and explore why many systems are vulnerable to nonobvious and potentially dangerous manipulation. Here, we examine sensitivity in any application whose misuse might lead to harm--for instance, forcing adaptive network in an unstable state, crashing an autonomous vehicle or bypassing an adult content filter. I explore the use of machine learning in this area particularly in light of recent discoveries in the creation of adversarial samples and defenses against them and posit on future attacks on machine learning. The talk is concluded with a discussion of the technological and societal challenges we face as a result of current and future advances in intelligent computing.

Bio: Patrick McDaniel is the William L. Weiss Professor of Information and Communications Technology and Director of the Institute for Networking and Security Research in the School of Electrical Engineering and Computer Science at the Pennsylvania State University. Professor McDaniel is also a Fellow of the IEEE and ACM and the director of the NSF Frontier Center for Trustworthy Machine Learning. He also served as the program manager and lead scientist for the Army Research Laboratory's Cyber-Security Collaborative Research Alliance from 2013 to 2018. Patrick's research centrally focuses on a wide range of topics in computer and network security and technical public policy. Prior to joining Penn State in 2004, he was a senior research staff member at AT&T Labs-Research.

An interactive session to discover how to create ALT text tags from images and create high-impact visuals, from identification to communicating ideas with images.

Discover how to use AI to create ALT text from images as well as identify objects in your environment, and build relatable visuals for high-impact presentations. Images communicate ideas as a way to understand concepts. AI-generated images have helped allow anyone to create these.

In this session, you will

  1. Creating image ALT Tags
  2. Transform ideas into images that are visually appealing
  3. Identify objects from visuals

Register here.

Abstract: This talk is about the two ends of LLM training: pre-training and in-deployment learning. I will present an approach to disentangle knowledge from skill in model pre-training. This brings about a new class of LLMs that externalize knowledge, with dramatically different characteristics from common LLMs along dimensions of scale, factuality, and updateability. On the other end, I will discuss two in-deployment learning methods. I will describe how in-context learning abilities extend beyond supervised settings, showing that LLMs display in-context reinforcement learning from rewards. Finally, if time allows, I will describe continual learning from implicit interaction signals, demonstrating that LLMs can retrospectively decode latent interaction cues by observing how humans respond to their outputs.

Bio: Yoav Artzi is an Associate Professor in the Department of Computer Science and Cornell Tech at Cornell University, a visiting faculty researcher at Google DeepMind, and arXiv's associate faculty director. His research focuses on language modeling and learning in interactive and situated scenarios. His work was acknowledged by awards and honorable mentions at ACL, EMNLP, NAACL, and IROS, as well as a TACL test-of-time award. Yoav holds a B.Sc. from Tel Aviv University and a Ph.D. from the University of Washington.

Location: NCS 120