| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
| Outcome: | The proposed approach undermins arguments by attacking weak premises . human annotators favor the proposed approach over state-of-the-art approaches . |
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| Challenge: | Recent advances in Large Language Models (LLMs) show promise in automating counter-argument generation. |
| Approach: | They compare multi-step and one-step generation methods for counter-arguments across 100 debate topics. |
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Conclusion-based Counter-Argument Generation (2023.eacl-main)
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| Challenge: | Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. |
| Approach: | They propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. |
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A Closer Look into the Robustness of Neural Dependency Parsers Using Better Adversarial Examples (2021.findings-acl)
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| Challenge: | Neural network-based models have been successful in a wide range of NLP tasks, but their performance is undermined by adversarial examples that would pose no confusion for humans. |
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Detecting Attackable Sentences in Arguments (2020.emnlp-main)
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| Challenge: | Prior work in NLP studies focus on argument quality and making counterarguments toward the main claim, without investigating what parts of an argument are attackable for successful persuasion. |
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A Dataset of General-Purpose Rebuttal (D19-1)
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Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim
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KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations (2023.acl-short)
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| Challenge: | eIA is an adversarial attack that generates inconsistent natural language explanations (NLEs) a model that generate In-NLE is undesirable, as it has a faulty decision-making process or is prone to inconsistencies. |
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Towards an argumentative content search engine using weak supervision (C18-1)
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| Challenge: | Existing work focused on detecting claims within a small set of documents . however, pinpointing relevant claims within massive unstructured corpora, received little attention. |
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A Simple, Yet Effective Approach to Finding Biases in Code Generation (2023.findings-acl)
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
| Approach: | They propose a framework that automatically identifies subtle cues a code generation model might exploit . they propose an automated intervention mechanism reminiscent of adversarial testing . |
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High-quality argumentative information in low resources approaches improve counter-narrative generation (2023.findings-emnlp)
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| Challenge: | a recent study shows that fine-tuning improves the performance of language models . large language models generate acceptable texts in a number of scenarios, a study shows . |
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Towards Controllable Biases in Language Generation (2020.findings-emnlp)
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| Challenge: | a new method to induce societal biases in natural language generation is being developed . a method to equalize the amount of biased text across demographics is effective . |
| Approach: | They propose a method to induce societal biases in natural language generation by using demographic inequalities. |
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