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Found 7 papers, 4 papers with code
Tailoring Self-Rationalizers with Multi-Reward Distillation
Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline.
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure prompting can often be limited.
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
Existing metrics like task performance of the LM generating the rationales, or similarity between generated and gold rationales are not good indicators of their human utility.
A Framework for Rationale Extraction for Deep QA models
As neural-network-based QA models become deeper and more complex, there is a demand for robust frameworks which can access a model's rationale for its prediction.
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints
For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration).
Towards Interpreting BERT for Reading Comprehension Based QA
BERT and its variants have achieved state-of-the-art performance in various NLP tasks.
Scene Graph based Image Retrieval -- A case study on the CLEVR Dataset
With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community.
