Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation (P19-1)
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| Challenge: | Existing benchmarks measure common sense knowledge indirectly or without reasoning. |
| Approach: | They propose a benchmark to test whether a system can differentiate natural language statements that make sense from those that do not make sense. |
| Outcome: | The proposed benchmarks show that models trained on large corpora perform better than humans on some benchmarks. |
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| Challenge: | In this tutorial, we will outline the various types of commonsense knowledge and discuss techniques to gather and represent commonsence knowledge. |
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| Challenge: | Evaluation for many natural language understanding (NLU) tasks is broken due to unreliable and biased systems scoring so high on standard benchmarks. |
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| Challenge: | a recent paper suggests that the evidence underspecifies the understanding of large language models. |
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Mihir Parmar, Nisarg Patel, Neeraj Varshney, Mutsumi Nakamura, Man Luo, Santosh Mashetty, Arindam Mitra, Chitta Baral
| Challenge: | Existing work investigating the logical reasoning ability of large language models has focused only on a couple of inference rules of propositional and first-order logics. |
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| Challenge: | Reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). |
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Which Evaluations Uncover Sense Representations that Actually Make Sense? (2020.lrec-1)
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| Challenge: | Existing sense representations fail for human-centric tasks like inspecting a language’s sense inventory. |
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| Challenge: | Pre-trained word representations capture common sense on physical properties such as size and weight. |
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A Pragmatics-Centered Evaluation Framework for Natural Language Understanding (2022.lrec-1)
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| Challenge: | a number of studies have suggested that models induce universal text representations . current benchmarks focus on semantic phenomena, so pragmatics needs to be the focus . |
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