Papers with BART-large
Generative Interpretation: Toward Human-Like Evaluation for Educational Question-Answer Pair Generation (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing evaluation methods often fail to produce objective results and favor high similarity to the ground-truth question-answer pairs. |
| Approach: | They propose an alternative approach to evaluate question-answer generation using Generative Interpretation (GI) GI outperforms existing evaluation methods in terms of human alignment . |
| Outcome: | The proposed approach outperforms existing evaluation methods in human alignment and shows comparable performance with GPT3.5, only with BART-large. |
TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing auto-regressive pre-trained language models are challenged by recent emerging numerical reasoning datasets due to the error-prone implicit calculation. |
| Approach: | They propose a pre-computation tool to pre-compute aggregation/arithmetic results for the table in advance, so they are handy and readily available for PLMs to answer numerical reasoning questions. |
| Outcome: | The proposed model improves on TAT-QA and T5 and BART-large on multiple benchmarks. |
Inverse Reinforcement Learning for Text Summarization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies show that inverse reinforcement learning (RL) training has certain disadvantages such as object mismatch and exposure bias. |
| Approach: | They propose inverse reinforcement learning (IRL) as an effective paradigm for training abstractive summarization models. |
| Outcome: | The proposed model outperforms MLE and RL baselines on ROUGE, coverage, novelty, compression ratio, factuality, and human evaluations. |
Small Language Models can Outperform Humans in Short Creative Writing: A Study Comparing SLMs with Humans and LLMs (2025.coling-main)
Copied to clipboard
| Challenge: | a fine-tuned small language model (SLM) can generate human-like text, but it requires immense computational resources and large datasets. |
| Approach: | They evaluate the creative writing abilities of a fine-tuned small language model, BART-large . they compare it to human writers and two large language models: GPT-3.5 and GPT-4o . |
| Outcome: | The proposed model outperforms human writers and two large language models in two experiments . the results highlight how model size and fine-tuning influence creativity, fluency, and coherence . |
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing pre-trained models need fine-tuning on tens of thousands of examples to achieve good results. |
| Approach: | They propose a framework that leverages pre-trained text-to-text models and aligns them with their pre-training framework. |
| Outcome: | The proposed framework outperforms the XLM-Roberta-large on multiple QA benchmarks and is applicable to multilingual situations. |