Papers by Damien Teney

2 papers
Vision-Language Pretraining: Current Trends and the Future (2022.acl-tutorials)

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Challenge: Recent vision-language models are being used for downstream tasks that require large datasets and supervised datasets.
Approach: They focus on recent vision-language pretraining paradigms and their strengths and shortcomings . they compare the different family of models used for vision- language pretraining .
Outcome: This paper provides the background on image–language datasets, benchmarks, and modeling innovations before the multimodal pretraining area.
A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers (2024.naacl-long)

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Challenge: evaluating the generalisability of Transformers to out-of-distribution mathematical reasoning problems is a challenge for many open-source models.
Approach: They propose a method for generating and perturbing detailed derivations of equations at scale, aided by a symbolic engine, and compare their results to sequence classification tasks.
Outcome: The proposed framework outperforms GPT-4, GPT-3.5 and a canon of fine-tuned BERT models in classification tasks . perturbations to input reasoning can reduce their performance by up to 80 F1 points .

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