Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10128 publications
Alignment of brain embeddings and artificial contextual embeddings in natural language points to common geometric patterns
Ariel Goldstein
Avigail Grinstein-Dabush
Haocheng Wang
Zhuoqiao Hong
Bobbi Aubrey
Samuel A. Nastase
Zaid Zada
Eric Ham
Harshvardhan Gazula
Eliav Buchnik
Werner Doyle
Sasha Devore
Patricia Dugan
Roi Reichart
Daniel Friedman
Orrin Devinsky
Adeen Flinker
Uri Hasson
Nature Communications (2024)
Preview abstract
Contextual embeddings, derived from deep language models (DLMs), provide
a continuous vectorial representation of language. This embedding space
differs fundamentally from the symbolic representations posited by traditional
psycholinguistics. We hypothesize that language areas in the human brain,
similar to DLMs, rely on a continuous embedding space to represent language.
To test this hypothesis, we densely record the neural activity patterns in the
inferior frontal gyrus (IFG) of three participants using dense intracranial arrays
while they listened to a 30-minute podcast. From these fine-grained spatiotemporal neural recordings, we derive a continuous vectorial representation
for each word (i.e., a brain embedding) in each patient. We demonstrate that
brain embeddings in the IFG and the DLM contextual embedding space have
common geometric patterns using stringent zero-shot mapping. The common
geometric patterns allow us to predict the brain embedding of a given left-out
word in IFG based solely on its geometrical relationship to other nonoverlapping words in the podcast. Furthermore, we show that contextual
embeddings better capture the geometry of IFG embeddings than static word
embeddings. The continuous brain embedding space exposes a vector-based
neural code for natural language processing in the human brain.
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Conformal Language Modeling
Victor Quach
Adam Fisch
Adam Yala
Jae Ho Sohn
Tommi Jaakkola
Regina Barzilay
ICLR (2024)
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In this paper, we propose a novel approach to conformal prediction (CP) that is adapted to generative, large language models (LLMs). Conformal prediction is a popular technique for deriving prediction sets from machine learning models that have rigorous, statistical performance guarantees. We extend conformal techniques to a broad class of language models that sample from a conditional distribution over the combinatorial, unbounded space of possible text outputs, given some input prompt. Specifically, we translate the process of constructing prediction sets into calibrating a \emph{stopping rule}, under which we draw diverse samples from our model until we are confident that the growing set of candidate answers includes at least one high-quality response. At the same time, we calibrate a \emph{rejection rule} to selectively discard low-quality or redundant responses to reduce sample noise. Under minimal assumptions, we theoretically prove that our resulting output sets contain at least one high-quality answer with some desired probability that a user can set (such as $90\%$), while still remaining empirically precise on average. Furthermore, within this set of sampled candidate answers, we show that we can also accurately identify subsets of individual components (e.g., phrases or sentences) that are each independently correct (e.g., that are not ``hallucinations'')---again, with provably high probability. We demonstrate the effectiveness of our approach on multiple types of large language models applied to tasks in open-domain question answering, text summarization, and radiology report generation.
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Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan
Atilla Kiraly
Corbin Cunningham
Ryan Najafi
Jie Yang
Chuck Lau
Diego Ardila
Scott Mayer McKinney
Rory Pilgrim
Mozziyar Etemadi
Sunny Jansen
Lily Peng
Shravya Shetty
Neeral Beladia
Krish Eswaran
Radiology: Artificial Intelligence (2024)
Preview abstract
Lung cancer is the leading cause of cancer death world-wide with 1.8 million deaths in 20201. Studies have concluded that low-dose computed tomography lung cancer screening can reduce mortality by up to 61%2 and updated 2021 US guidelines expanded eligibility. As screening efforts rise, AI can play an important role, but must be unobtrusively integrated into existing clinical workflows. In this work, we introduce a state-of-the-art, cloud-based AI system providing lung cancer risk assessments without requiring any user input. We demonstrate its efficacy in assisting lung cancer screening under both US and Japanese screening settings using different patient populations and screening protocols. Technical improvements over a previously described system include a focus on earlier cancer detection for improved accuracy, introduction of an effective assistive user interface, and a system designed to integrate into typical clinical workflows. The stand-alone AI system was evaluated on 3085 individuals achieving area under the curve (AUC) scores of 91.7% (95%CI [89.6, 95.2]), 93.3% (95%CI [90.2, 95.7]), and 89.1% (95%CI [77.7, 97.3]) on three datasets (two from US and one from Japan), respectively. To evaluate the system’s assistive ability, we conducted two retrospective multi-reader multi-case studies on 627 cases read by experienced board certified radiologists (average 20 years of experience [7,40]) using local PACS systems in the respective US and Japanese screening settings. The studies measured the reader’s level of suspicion (LoS) and categorical responses for scores and management recommendations under country-specific screening protocols. The radiologists’ AUC for LoS increased with AI assistance by 2.3% (95%CI [0.1-4.5], p=0.022) for the US study and by 2.3% (95%CI [-3.5-8.1], p=0.179) for the Japan study. Specificity for recalls increased by 5.5% (95%CI [2.7-8.5], p<0.0001) for the US and 6.7% (95%CI [4.7-8.7], p<0.0001) for the Japan study. No significant reduction in other metrics occured. This work advances the state-of-the-art in lung cancer detection, introduces generalizable interface concepts that can be applicable to similar AI applications, and demonstrates its potential impact on diagnostic AI in global lung cancer screening with results suggesting a substantial drop in unnecessary follow-up procedures without impacting sensitivity.
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ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Gilles Baechler
Srinivas Sunkara
Maria Wang
Hassan Mansoor
Vincent Etter
Jason Lin
(2024)
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Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We introduce ScreenAI, a vision-language model that specializes in UI and infographics understanding. Our model improves upon the PaLI architecture with the flexible patching strategy of pix2struct and is trained on a unique mixture of datasets. At the heart of this mixture is a novel screen annotation task in which the model has to identify the type and location of UI elements. We use these text annotations to describe screens to Large Language Models and automatically generate question-answering (QA), UI navigation, and summarization training datasets at scale. We run ablation studies to demonstrate the impact of these design choices. At only 5B parameters, ScreenAI achieves new state-of-the-artresults on UI- and infographics-based tasks (Multi-page DocVQA, WebSRC, MoTIF and Widget Captioning), and new best-in-class performance on others (Chart QA, DocVQA, and InfographicVQA) compared to models of similar size. Finally, we release three new datasets: one focused on the screen annotation task and two others focused on question answering.
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We introduce SynCLR, a novel approach for learning visual representations exclusively from synthetic images and synthetic captions, without any real data. We synthesize a large dataset of image captions using LLMs, then use an off-the-shelf text-to-image model to generate multiple images corresponding to each synthetic caption. We perform visual representation learning on these synthetic images via contrastive learning, treating images sharing the same caption as positive pairs. The resulting representations transfer well to many downstream tasks, competing favorably with other general-purpose visual representation learners such as CLIP and DINO v2 in image classification tasks. Furthermore, in dense prediction tasks such as semantic segmentation, SynCLR outperforms previous self-supervised methods by a significant margin, e.g., improving over MAE and iBOT by 6.2 and 4.3 mIoU on ADE20k for ViT-B/16.
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Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM
Brenna Li
Amy Wang
Patricia Strachan
Julie Anne Seguin
Sami Lachgar
Karyn Schroeder
Renee Wong
Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, pp. 10
Preview abstract
Although skin concerns are common, access to specialist care is limited. Artificial intelligence (AI)-assisted tools to support medical decisions may provide patients with feedback on their concerns while also helping ensure the most urgent cases are routed to dermatologists. Although AI-based conversational agents have been explored recently, how they are perceived by patients and clinicians is not well understood. We conducted a Wizard-of-Oz study involving 18 participants with real skin concerns. Participants were randomly assigned to interact with either a clinician agent (portrayed by a dermatologist) or an LLM agent (supervised by a dermatologist) via synchronous multimodal chat. In both conditions, participants found the conversation to be helpful in understanding their medical situation and alleviate their concerns. Through qualitative coding of the conversation transcripts, we provide insight on the importance of empathy and effective information-seeking. We conclude with design considerations for future AI-based conversational agents in healthcare settings.
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Grounded generation aims to equip language models (LMs) with the ability to produce more credible and accountable responses by accurately citing verifiable sources. However, existing methods, by either feeding LMs with raw or preprocessed materials, remain prone to errors. To address this, we introduce CaLM, a novel verification framework. CaLM leverages the insight that a robust grounded response should be consistent with information derived solely from its cited sources. Our framework empowers smaller LMs, which rely less on parametric memory and excel at processing relevant information given a query, to validate the output of larger LMs. Larger LM responses that closely align with the smaller LMs' output, which relies exclusively on cited documents, are verified. Responses showing discrepancies are iteratively refined through a feedback loop. Experiments on three open-domain question-answering datasets demonstrate significant performance gains of 1.5% to 7% absolute average without any required model fine-tuning.
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Current approaches to Analog Layout Automation
apply ML techniques such as Graph Convolutional Neural
Networks (GCN) to translate netlist to layout. While these ML
approaches have proven to be effective, they lack the powerful
reasoning capabilities, an intuitive human interface, and standard
evaluation benchmarks that have been improving at a rapid de-
velopment pace in Large Language Models (LLMs). The GLayout
framework introduced in this work translates analog layout into
an expressive, technology generic, compact text representation.
Then, an LLM is taught to understand analog layout through
fine-tuning and in-context learning using Retrieval Augmented
Generation (RAG). The LLM is able to successfully layout unseen
circuits based on new information provided in-context. We train
3.8, 7, and 22 Billion parameter quantized LLMs on a dataset
of less than 50 unique circuits, and text documents providing
layout knowledge. The 22B parameter model is tuned in 2 hours
on a single NVIDIA A100 GPU. The open-source evaluation
set is proposed as an automation benchmark for LLM layout
automation tasks, and ranges from 2-transistor circuits to a
∆Σ ADC. The 22B model completes 70% of the tasks in the
evaluation set, and is able to pass DRC and LVS verification on
unseen 4 transistor blocks.
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Empowering Teams through Data: An In-Depth Study of Data Engineering, Cloud Storage, and Business Intelligence in Collaborative Workspaces
Jayasekhar Konduru
SSRG International Journal of Computer Science and Engineering (2024)
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In the present computerized period, information driven navigation is essential for the progress of cooperative work areas. This paper gives an extensive examination of how information designing, distributed storage, and business insight synergistically engage groups. We look at the basic standards of information designing, zeroing in on the plan, development, and the management of adaptable information pipelines. The job of distributed storage is investigated, featuring its ability to give adaptable, secure, and open information arrangements. Besides, we dive into business knowledge instruments and their capacity to change crude information into significant experiences. Through contextual analyses and exact information, we delineate the groundbreaking effect of these advances in group efficiency, coordinated effort, and dynamic cycles. This examination highlights the significance of incorporating hearty information designing works on, utilizing distributed storage arrangements, and utilizing complex business knowledge apparatuses to establish information engaged cooperative conditions.
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Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Cheng-Yu Hsieh
Yung-Sung Chuang
Chun-Liang Li
Abhishek Kumar
James Glass
Alexander Ratner
Ranjay Krishna
Preview abstract
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work, we make three contributions. First, we set out to understand the factors that cause this phenomenon. In doing so, we establish a connection between lost-in-the-middle to LLMs' intrinsic attention bias: LLMs exhibit a U-shaped attention bias where the tokens at the beginning and at the end of its input receive higher attention, regardless of their relevance. Second, we mitigate this positional bias through a calibration mechanism, found-in-the-middle, that allows the model to attend to contexts faithfully according to their relevance, even though when they are in the middle. Third, we show found-in-the-middle not only achieves better performance in locating relevant information within a long context, but also eventually leads to improved retrieval-augmented generation (RAG) performance across various tasks, outperforming existing methods by up to 15 percentage points. These findings open up future directions in understanding LLM attention bias and its potential consequences.
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Take it, Leave it, or Fix it: Measuring Productivity and Trust in Human-AI Collaboration
29th International Conference on Intelligent User Interfaces (IUI ’24), ACM, New York, NY, USA (2024)
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Although recent developments in generative AI have greatly enhanced the capabilities of conversational agents such as Google's Bard or OpenAI's ChatGPT, it's unclear whether the usage of these agents aids users across various contexts. To better understand how access to conversational AI affects productivity and trust, we conducted a mixed-methods, task-based user study, observing 76 software engineers (N=76) as they completed a programming exam with and without access to Bard. Effects on performance, efficiency, satisfaction, and trust vary depending on user expertise, question type (open-ended "solve" questions vs. definitive "search" questions), and measurement type (demonstrated vs. self-reported). Our findings include evidence of automation complacency, increased reliance on the AI over the course of the task, and increased performance for novices on “solve”-type questions when using the AI. We discuss common behaviors, design recommendations, and impact considerations to improve collaborations with conversational AI.
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CodecLM: Aligning Language Models with Tailored Synthetic Data
Chun-Liang Li
Jin Miao
NAACL 2024
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Instruction tuning has emerged as the key in aligning large language models (LLMs) with specific task instructions, thereby mitigating the discrepancy between the next-token prediction objective and users' actual goals. To reduce the labor and time cost to collect or annotate data by humans, researchers start to explore the use of LLMs to generate instruction-aligned synthetic data. Recent works focus on generating diverse instructions and applying LLM to increase instruction complexity, often neglecting downstream use cases. It remains unclear how to tailor high-quality data to elicit better instruction-following abilities in different target instruction distributions and LLMs. To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. Drawing on the Encode-Decode principles, we use LLMs as codecs to guide the data generation process. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution, and then decode metadata to create tailored instructions. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples. Extensive experiments on four open-domain instruction following benchmarks validate the effectiveness of CodecLM over the current state-of-the-arts.
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In this talk, we will introduce the development and evolution of speaker diarization technologies at Google in the past decade, and how they landed as impactful products such as Cloud Speech-to-Text and the Pixel Recorder app. The talk will cover four critical milestones of the speaker diarization technologies at Google: (1) leveraging deep speaker embeddings; (2) leveraging supervised clustering; (3) leveraging sequence transducers; and (4) leveraging large language models. The talk will also discuss how speaker diarization will evolve in the new era of multimodal large language models.
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Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation
Susan Lin
Jeremy Warner
J.D. Zamfirescu-Pereira
Matthew G Lee
Sauhard Jain
Michael Xuelin Huang
Bjoern Hartmann
Can Liu
Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA
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Dictation enables efficient text input on mobile devices. However, writing with speech can produce disfluent, wordy, and incoherent text and thus requires heavy post-processing. This paper presents Rambler, an LLM-powered graphical user interface that supports gist-level manipulation of dictated text with two main sets of functions: gist extraction and macro revision. Gist extraction generates keywords and summaries as anchors to support the review and interaction with spoken text. LLM-assisted macro revisions allow users to respeak, split, merge, and transform dictated text without specifying precise editing locations. Together they pave the way for interactive dictation and revision that help close gaps between spontaneously spoken words and well-structured writing. In a comparative study with 12 participants performing verbal composition tasks, Rambler outperformed the baseline of a speech-to-text editor + ChatGPT, as it better facilitates iterative revisions with enhanced user control over the content while supporting surprisingly diverse user strategies.
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The effect of regularizers such as weight decay when training deep neural networks is not well understood. We study the influence of weight decay as well as $L2$-regularization when training neural network models in which parameter matrices interact multiplicatively. This combination is of particular interest as this parametrization is common in attention layers, the workhorse of transformers. Here, key-query, as well as value-projection parameter matrices, are multiplied directly with each other: $W_K^TW_Q$ and $PW_V$.
We extend previous results and show on one hand that any local minimum of a $L2$-regularized loss of the form $L(AB^\top) + \lambda (\|A\|^2 + \|B\|^2)$ coincides with a minimum of the nuclear norm-regularized loss $L(AB^\top) + \lambda\|AB^\top\|_*$, and on the other hand that the 2 losses become identical exponentially quickly during training. We thus complement existing works linking $L2$-regularization with low-rank regularization, and in particular, explain why such regularization on the matrix product affects early stages of training.
Based on these theoretical insights, we verify empirically that the key-query and value-projection matrix products $W_K^TW_Q, PW_V$ within attention layers, when optimized with weight decay, as usually done in vision tasks and language modelling, indeed induce a significant reduction in the rank of $W_K^TW_Q$ and $PW_V$, even in fully online training.
We find that, in accordance with existing work, inducing low rank in attention matrix products can damage language model performance, and observe advantages when decoupling weight decay in attention layers from the rest of the parameters.
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