Papers by Ari Kobren
Optimal Transport-based Alignment of Learned Character Representations for String Similarity (P19-1)
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| Challenge: | String similarity models are crucial for record linkage, data integration, search and entity resolution systems. |
| Approach: | They propose a model that encodes the characters of each string, aligns the encodings using Sinkhorn Iteration and scores the alignment with a convolutional neural network. |
| Outcome: | The proposed model outperforms state-of-the-art and classical similarity models on four of the five datasets and improves performance by applying it to cross-document coreference. |
Upstream Mitigation Is Not All You Need: Testing the Bias Transfer Hypothesis in Pre-Trained Language Models (2022.acl-long)
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| Challenge: | Large language models and other massively pre-trained "foundation" models can easily adapt to a wide variety of downstream tasks in a process called finetuning. |
| Approach: | They propose to use the bias transfer hypothesis to reduce social biases internalized by large language models during pre-training into harmful task-specific behavior after fine-tuning. |
| Outcome: | The bias transfer hypothesis is the theory that social biases internalized by large language models during pre-training transfer into harmful task-specific behavior after fine-tuning. |
Don’t Just Clean It, Proxy Clean It: Mitigating Bias by Proxy in Pre-Trained Models (2022.findings-emnlp)
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| Challenge: | Transformer-based pre-trained models can encode societal biases in their contextual representations and in downstream predictions when fine-tuned on task-specific data. |
| Approach: | They propose an approach that selectively eliminates stereotypical associations at fine-tuning, so that the model doesn't learn to excessively rely on those signals. |
| Outcome: | The proposed approach reduces biases from identity words and frequently co-occurring proxies by > 60% in toxicity classification, and also extends to multiple identities. |
Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations (2024.emnlp-main)
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| Challenge: | Existing work on citation generation has focused on unambiguous settings with single answers, failing to address the complexity of real-world scenarios. |
| Approach: | They propose a task of QA with source citation in ambiguous settings where multiple valid answers exist, where multiple sources exist. |
| Outcome: | The proposed framework generates multiple answers and cites their sources, allowing users to verify the factuality of each answer and make informed decisions. |