Papers by Jack Lanchantin
Reevaluating Adversarial Examples in Natural Language (2020.findings-emnlp)
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| Challenge: | State-of-the-art adversarial examples lack a common definition of what constitutes success . human surveys show that to preserve semantics, we need to increase the minimum cosine similarities between the embeddings of swapped words and between the sentence encodings of original and perturbed sentences. |
| Approach: | They propose a unified definition of what constitutes a successful adversarial example . they propose four categories of constraints that are used to define adversarials . |
| Outcome: | The proposed framework is based on the outputs of two state-of-the-art synonym substitution attacks. |
TOOLVERIFIER: Generalization to New Tools via Self-Verification (2024.findings-emnlp)
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Dheeraj Mekala, Jason Weston, Jack Lanchantin, Roberta Raileanu, Maria Lomeli, Jingbo Shang, Jane Dwivedi-Yu
| Challenge: | Existing tools and APIs present a challenge for generalization, despite frequent parameter updates and the daily introduction of new tools. |
| Approach: | They propose a method which distinguishes between close candidates by self-asking contrastive questions during tool selection and parameter generation. |
| Outcome: | Experiments on 4 tasks from the ToolBench benchmark show an improvement of 22% over few-shot baselines. |
Robustness of Named-Entity Replacements for In-Context Learning (2023.findings-emnlp)
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Saeed Goodarzi, Nikhil Kagita, Dennis Minn, Shufan Wang, Roberto Dessi, Shubham Toshniwal, Adina Williams, Jack Lanchantin, Koustuv Sinha
| Challenge: | Modern large language models perform in-context learning, where query- answer demonstrations are shown before the final query. |
| Approach: | They propose to use in-context learning to prompt queries before they are answered . they find that the choice of demonstrations can affect model performance . |
| Outcome: | The proposed model performance improves on named entity replacements across three reasoning tasks and two popular LLMs. |