Papers by Alexander Clark
ADaPT: As-Needed Decomposition and Planning with Language Models (2024.findings-naacl)
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Archiki Prasad, Alexander Koller, Mareike Hartmann, Peter Clark, Ashish Sabharwal, Mohit Bansal, Tushar Khot
| Challenge: | Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. |
| Approach: | They propose an approach that explicitly plans and decomposes complex sub-tasks when the LLM is unable to execute them. |
| Outcome: | The proposed approach significantly outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft. |
Consistent Unsupervised Estimators for Anchored PCFGs (2020.tacl-1)
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| Challenge: | a novel approach for learning probabilistic context-free grammars from strings is proposed . strong learning means that there can be many structurally different PCFGs that define the same distribution over strings. |
| Approach: | They propose an algorithm that is a consistent estimator for a class of PCFGs that are anchored . they show that if the grammar is anchored, the parameters can be directly related to distributional properties of the anchoring strings. |
| Outcome: | The proposed algorithm is consistent for a large class of probabilistic context-free grammars . it shows that the proposed algorithm has good finite sample behavior . |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |