Papers by Jwala Dhamala
MICo: Preventative Detoxification of Large Language Models through Inhibition Control (2024.findings-naacl)
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Roy Siegelmann, Ninareh Mehrabi, Palash Goyal, Prasoon Goyal, Lisa Bauer, Jwala Dhamala, Aram Galstyan, Rahul Gupta, Reza Ghanadan
| Challenge: | Large Language Models (LLMs) have a tendency to devolve into toxic degeneration . model may classify prompts as toxic or non-toxic and categorically refuse to respond to those deemed toxic. |
| Approach: | They propose a mechanism for LLM detoxification by labeling acceptable and unacceptable examples and including a corresponding acceptable rewrite with every unacceptable example. |
| Outcome: | The proposed model improves on the baseline model and shows that it detects and rewrites toxic and harmful examples. |
Multi-VALUE: A Framework for Cross-Dialectal English NLP (2023.acl-long)
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| Challenge: | Current systems that focus on standard American English are not dialect invariant . current systems focus on a single dialect, which results in performance discrepancies . |
| Approach: | They propose a resource for evaluating and achieving English dialect invariance . they stress test question answering, machine translation, and semantic parsing . |
| Outcome: | The proposed system is based on a rule-based translation system spanning 50 English dialects and 189 unique linguistic features. |
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2022.acl-short)
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Yang Trista Cao, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta, Varun Kumar, Jwala Dhamala, Aram Galstyan
| Challenge: | Recent natural language processing systems use large language models as the backbone . however, societal biases are encoded in these models and transferred to downstream applications . |
| Approach: | They propose to use two categories to measure fairness in natural language processing tasks . they find intrinsic and extrinsic metrics do not correlate in their original setting . |
| Outcome: | The proposed metrics do not correlate in their original setting, the authors show . they find that they are not accurate when correcting for metric misalignments and noise . |
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies (2024.findings-naacl)
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Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal, Jwala Dhamala, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Yuval Pinter, Rahul Gupta
| Challenge: | a recent study documented the harmful limitations of gender binary-centric large language models . data scarcity is a known culprit, but the precise mechanisms through which scarcity affects this behavior remain underexplored. |
| Approach: | They propose to use BPE tokenization to enforce consistent tokenization across gendered pronouns to improve neopronoun proficiency. |
| Outcome: | The proposed methods outperform finetuning with standard BPE, and improve neopronoun proficiency. |
Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification (2021.findings-acl)
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| Challenge: | Existing methods to reduce disparities in model outcomes have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training. |
| Approach: | They propose to use certified word substitution robustness methods to improve equality of odds and equality of opportunity on multiple text classification tasks. |
| Outcome: | The proposed methods improve equality of odds and equality of opportunity on multiple text classification tasks. |
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal (2022.findings-acl)
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Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan
| Challenge: | Language models excel at generating coherent text, but can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. |
| Approach: | They propose to modify teacher probabilities and augment the training set to learn a fair model during knowledge distillation by modifying teacher probability and augmenting the training sets. |
| Outcome: | The proposed approach reduces gender disparity in open-ended text generated from the distilled and finetuned models with only a minor compromise in utility. |
Measuring Fairness of Text Classifiers via Prediction Sensitivity (2022.acl-long)
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| Challenge: | Existing fairness metrics are not yet available to measure the fairness of language processing systems. |
| Approach: | They propose a new metric which measures fairness in machine learning models based on the model’s prediction sensitivity to perturbations in input features. |
| Outcome: | The proposed metric can be linked with a specific notion of group fairness and individual fairness, and correlates well with humans’ perception of fairness. |
Resolving Ambiguities in Text-to-Image Generative Models (2023.acl-long)
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Ninareh Mehrabi, Palash Goyal, Apurv Verma, Jwala Dhamala, Varun Kumar, Qian Hu, Kai-Wei Chang, Richard Zemel, Aram Galstyan, Rahul Gupta
| Challenge: | ambiguities can lead to misinterpretation and miscommunication in natural language . resolving ambiguity is notoriously hard for machines . |
| Approach: | They propose a framework to disambiguate prompts given to generative models by soliciting clarifications from the end user. |
| Outcome: | The proposed framework generates more faithful images better aligned with user intention in the presence of ambiguities. |
Tree-of-Traversals: A Zero-Shot Reasoning Algorithm for Augmenting Black-box Language Models with Knowledge Graphs (2024.acl-long)
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Elan Markowitz, Anil Ramakrishna, Jwala Dhamala, Ninareh Mehrabi, Charith Peris, Rahul Gupta, Kai-Wei Chang, Aram Galstyan
| Challenge: | Knowledge graphs (KGs) complement Large Language Models (LLMs) by providing reliable, structured, domain-specific, and up-to-date external knowledge. |
| Approach: | They propose a zero-shot reasoning algorithm that augments black-box LLMs with one or more KGs. |
| Outcome: | The proposed algorithm significantly improves performance on question answering and KG question answering tasks. |