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Articles by Jwala Dhamala
Computer Science > Computation and Language
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Tree-of-traversals: A zero-shot reasoning algorithm for augmenting black-box language models with knowledge graphs
Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. However, KGs and LLMs are often developed separately and must be integrated after training.
Tokenization matters: Navigating data-scarce tokenization for gender inclusive language technologies
Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse English neopronouns (e.g., xe, zir, fae). While data scarcity is a known culprit, the precise mechanisms through which scarcity affects this behavior remain under-explored.
Are you talking to [‘xem’] or [‘x’, ‘em’]? On tokenization and addressing misgendering in LLMs with pronoun tokenization parity
A large body of NLP research has documented the ways gender biases manifest and amplify within large language models (LLMs), though this research has pre- dominantly operated within a gender binary-centric context. A growing body of work has identified the harmful limitations of this gender-exclusive framing; many LLMs cannot correctly and consistently refer to persons outside the gender binary, especially if they use neopronouns.
JAB: Joint adversarial prompting and belief augmentation
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance. Here we introduce a joint framework in which we simultaneously probe and improve the robustness of a black-box target model via adversarial prompting and belief augmentation using iterative feedback loops.
Amazon Bedrock offers access to multiple generative AI models
The drive to harness the transformative power of high-end machine learning models has meant some businesses are facing new challenges. Teams want assistance in crafting compelling documents, summarizing complex documents, building conversational-AI agents, or generating striking, customized visuals. In April, Amazon stepped in to assist customers contending with the need to build and scale generative AI applications with a new service: Bedrock.
Multi-VALUE: A framework for cross-dialectal English NLP
Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language technology must critically be dialect invariant, meaning that performance remains constant over dialectal shifts. Current systems often fall short of this ideal since they are designed and tested on a single dialect: Standard American English (SAE).
Resolving ambiguities in text-to-image generative models
Natural language often contains ambiguities that can lead to misinterpretation and miscommunication. While humans can handle ambiguities effectively by asking clarifying questions and/or relying on contextual cues and commonsense knowledge, resolving ambiguities can be notoriously hard for machines. In this work, we study ambiguities that arise in text-to-image generative models.
“I’m fully who I am”: Towards centering transgender and non-binary voices to measure biases in open language generation
The role can be based in any of our EU offices. Amazon Supply Chain forms the backbone of the fastest growing e-commerce business in the world. The sheer growth of the business and the company's mission "to be Earth’s most customer-centric company” makes the customer fulfillment business bigger and more complex with each passing year.
Federated learning with weak supervision for speech recognition
Automatic-speech-recognition (ASR) models, which transcribe spoken utterances, are a key component of voice assistants. They are increasingly being deployed on devices at the edge of the Internet, where they enable faster responses (since they don’t require cloud processing) and continued service even during connectivity interruptions. But ASR models need regular updating, as new words and names enter the public conversation.
Computing on private data
Many of today’s most innovative computation-based products and solutions are fueled by data. Where those data are private, it is essential to protect them and to prevent the release of information about data subjects, owners, or users to the wrong parties. How can we perform useful computations on sensitive data while preserving privacy? We will revisit two well-studied approaches to this challenge: secure multiparty computation (MPC) and differential privacy (DP).
Making machine translation more robust, consistent, and stable
Like many other machine learning applications, neural machine translation (NMT) benefits from overparameterized deep neural models — models so large that they would seem to risk overfitting but whose performance, for some reason, keeps scaling with the number of parameters.
BOLD: Dataset and metrics for measuring biases in open-ended language generation
Recent advances in deep learning techniques have enabled machines to generate cohesive open-ended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to automatic storytelling, they have been shown to generate texts that exhibit social biases.
Measuring fairness of text classifiers via prediction sensitivity
With the rapid growth in language processing applications, fairness has emerged as an important consideration in data-driven solutions. Although various fairness definitions have been explored in the recent literature, there is lack of consensus on which metrics most accurately reflect the fairness of a system.
Evaluating the effectiveness of efficient neural architecture search for sentence-pair tasks
Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on a variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.
Does robustness improve fairness? Approaching fairness with word substitution robustness methods for text classification
How do you design and provide right incentives for millions of sellers that inbound and ship billions of customer orders? How do you measure sellers' response to /causal impacts of capacity control policies we implemented at Amazon using the state-of-the-art econometric techniques? How do you optimize Amazon’s third-party supply chain using new ideas never implemented at this scale to benefit millions of customers worldwide? How do you design and evaluate seller assistance to drive their success?
Mitigating gender bias in distilled language models via counterfactual role reversal
Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model’s biases onto the distilled model.
An analysis of the effects of decoding algorithms on fairness in open-ended language generation
Several prior works have shown that language models (LMs) can generate text containing harmful social biases and stereotypes. While decoding algorithms play a central role in determining properties of LM generated text, their impact on the fairness of the generations has not been studied. We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality.
Advances in trustworthy machine learning at Alexa AI
At Amazon, we take the protection of customer data very seriously. We are also committed to eliminating the biases that can exist in off-the-shelf language models — such as GPT-3 and RoBERTa — that are the basis of most modern natural-language processing. Trained on public texts, these language models are known to reflect the biases implicit in those texts.
Amazon scientists help SK telecom create Korean-based natural language processor
Korean is a major world language, spoken by some 80 million people. Although it has a long history, dating back to what is believed to be its start in Manchuria, Korean is what linguists call an “isolate,” with no apparent link to other languages, such as English has with French and Latin. But now Korean is part of the revolution in natural language processing, a branch of artificial intelligence that helps computers recognize and interpret human language.
New dataset, metrics enable evaluation of bias in language models
Language models, which encode the probabilities of particular sequences of words, have been much in the news lately for their almost uncanny ability to produce long, largely coherent texts from just a few seed words, or “prompts”. Language models are also crucial to commercial AI systems that perform tasks like automatic speech recognition, machine translation, and question answering, among other things.
20B-parameter Alexa model sets new marks in few-shot learning
Most major advances in AI have come from supervised learning, in which machine learning models are trained on annotated data. But as commercial AI models continue to increase in scale, relying on data annotation is becoming unsustainable. At Alexa AI, we are moving to the new paradigm of generalizable intelligence, in which models can learn new concepts and transfer knowledge from one language or task to another with minimal human input.
ACL: What comes next for natural-language processing?
Kathleen McKeown, the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and an Amazon Scholar, has a storied history with the Association for Computational Linguistics, whose annual meeting is taking place this week. From 1990 to 1992, she served one-year terms as a member of the association’s executive committee, its vice president, and finally its president, and from 1995 to 1997, she was secretary treasurer.
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