Papers with language task
Cross-Task Generalization Abilities of Large Language Models (2024.naacl-srw)
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| Challenge: | a thesis proposal advocates for the crucial role of cross-task generalization in NLP systems. |
| Approach: | They propose to benchmark cross-task generalization abilities with diverse NLP tasks . they also propose to develop model architectures for improving cross- task generalization . |
| Outcome: | This paper compares cross-task generalization abilities with diverse NLP tasks . it also analyzes and predicts the generalization landscape of current state-of-the-art large language models . |
Targeting the Benchmark: On Methodology in Current Natural Language Processing Research (2021.acl-short)
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| Challenge: | a language benchmark is a task devised that is restricted enough to be managable with current methods, but is deemed challenging enough to serve as a benchmark. |
| Approach: | They propose to use a language task as a benchmark and a baseline model to argue it is challenging enough to be a good one. |
| Outcome: | The proposed language benchmarks are based on a dataset and a language task . the proposed benchmarks can be used to measure progress towards the goal of the research . |
Adaptive Gating in Mixture-of-Experts based Language Models (2023.emnlp-main)
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| Challenge: | Existing models employ a fixed gating network where each token is computed by the same number of experts. |
| Approach: | They propose a flexible training strategy that allows tokens to be processed by a variable number of experts based on expert probability distribution. |
| Outcome: | The proposed model reduces training time and inference quality while maintaining sparsity while maintaining inference accuracy. |
Learning to Rank Visual Stories From Human Ranking Data (2022.acl-long)
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| Challenge: | Existing studies on visual storytelling (VIST) use automated evaluation metrics for text generation. |
| Approach: | They develop a Vrank metric that repurposes human evaluation results for automatic evaluation. |
| Outcome: | The proposed model is more accurate than existing metrics and is generalizable to textual stories. |
Blackbird language matrices (BLM), a new task for rule-like generalization in neural networks: Can Large Language Models pass the test? (2023.findings-emnlp)
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| Challenge: | Existing methods to evaluate large language models for generalization lack generalization ability . current methods for evaluating LLMs are based on tests of human intelligence . |
| Approach: | They propose to use a language task to evaluate large language models' generalisation ability . they propose to ask LLMs to solve simple variants of the RAVEN IQ test . |
| Outcome: | The proposed task can be used to evaluate the generalisation ability of large language models . it shows that current generative models can handle the task in the sense that they understand instructions . |
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021.emnlp-main)
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| Challenge: | We study whether and how cross-task generalization ability can be acquired . we use CrossFit to standardize seen/unseen task partitions and evaluation protocols . |
| Approach: | They propose a problem setup for studying cross-task generalization ability which standardizes seen/unseen task partitions and data access during different learning stages. |
| Outcome: | The proposed model can be used to build few-shot learners across diverse tasks. |
Find-the-Common: A Benchmark for Explaining Visual Patterns from Images (2024.lrec-main)
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| Challenge: | Recent advances in Instruction-fine-tuned Vision and Language Models (IVLMs) have prompted some studies to analyze the reasoning capabilities of IVLMs. |
| Approach: | They introduce a vision and language task for Inductive Visual Reasoning that uses common attributes across visual scenes to find common answers. |
| Outcome: | The proposed model can archive with 48% accuracy on the FTC, compared with state-of-the-art models. |