Papers by Lisa Bauer
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. |
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021.emnlp-main)
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| Challenge: | Current commonsense-reasoning tasks are discriminative in nature, where a model answers a multiple-choice question for a certain context. |
| Approach: | They propose a generative task that generates a commonsense-augmented graph for stance prediction by using a create-verify-and-refine graph collection framework. |
| Outcome: | The proposed model is able to generate a graph that serves as non-trivial, complete, and unambiguous explanation for the predicted stance. |
Analyzing the Limits of Self-Supervision in Handling Bias in Language (2022.findings-emnlp)
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| Challenge: | a recent study shows that natural language models can perform tasks with little to no in-context supervision . a number of tasks are performed using self-supervised pre-training . |
| Approach: | They define and comprehensively evaluate how well natural language taskprompting captures the semantics of four tasks for bias: diagnosis, identification, extraction and rephrasing. |
| Outcome: | The proposed model performs to wide varying degrees across bias dimensions . the model is largely challenged when prompted to perform these tasks . |
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains (2024.findings-emnlp)
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| Challenge: | a lack of anonymization of sensitive text data hinders development of NLP tools . poorly anonymized sensitive data cannot be easily shared with annotators or external researchers . |
| Approach: | They propose to use synthetic data to generate differentially private language models in place of real data to facilitate NLP development without compromising privacy. |
| Outcome: | The proposed model can be used to train public models without compromising privacy. |
Multi-Token Completion for Text Anonymization (2026.eacl-long)
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| Challenge: | Text anonymization is a critical task for enabling research and development in high-stakes domains containing private data. |
| Approach: | They propose a method for predicting replacements for sensitive spans with principled use-inspired evaluation criteria. |
| Outcome: | The proposed method produces more realistic text and preserves utility than alternative infilling methods and differentially private mechanisms across multiple domains without retraining. |
Disentangling Online Chats with DAG-structured LSTMs (2021.starsem-1)
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| Challenge: | a number of messaging systems allow fast and synchronous textual communication but they often have a more complicated structure in which independent sub-conversations are interwoven with one another. |
| Approach: | They propose a model that can handle directed acyclic dependencies and integrates structured information into the conversation. |
| Outcome: | The proposed model achieves state-of-the-art status on the task of recovering reply-to relations and is competitive on other disentanglement metrics. |
Identify, Align, and Integrate: Matching Knowledge Graphs to Commonsense Reasoning Tasks (2021.eacl-main)
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| Challenge: | Empirically, we investigate KG matches for the SocialIQA, Physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC (SIQA), ConceptNet (Speer et al., 2017), and an automatically constructed instructional KG based on WikiHow (Ostermann e., 2019b). |
| Approach: | They propose a method to assess how well a candidate KG can fill in knowledge gaps for a given task by using commonsense probes. |
| Outcome: | Empirically, we show that the proposed KG-to-task match is a good match for socialIQA, physical IQA, and MCScript2.0 datasets with 3 diverse KGs: ATOMIC, ConceptNet, and an instructional KG based on WikiHow. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |
Social Commonsense for Explanation and Cultural Bias Discovery (2023.eacl-main)
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| Challenge: | Social commonsense contains many human biases due to social and cultural influence. |
| Approach: | They aim to identify cultural biases in data that strongly influence model decisions . they use social commonsense knowledge to augment large-scale language models . |
| Outcome: | The proposed method shows that social commonsense knowledge can explain model behavior on two social tasks. |