Papers with GPS
Explainable Multi-hop Verbal Reasoning Through Internal Monologue (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing state-of-the-art language models do not make intermediate reasoning steps explicit . large pretrained language models such as BERT and RoBERTa have been successfully used in multi-hop reasoning problems . |
| Approach: | They propose to decompose multi-hop reasoning problems into several simple ones and use natural language to guide intermediate reasoning hops. |
| Outcome: | The proposed model can generate subgoals and perform inference in natural language at each reasoning step. |
LANS: A Layout-Aware Neural Solver for Plane Geometry Problem (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing neural solvers take GPS as vision-language task but lack layout awareness . Existing models are criticized for complex rules and poor adaptability . |
| Approach: | They propose a layout-aware neural solver called LANS that integrates two modules to solve GPS. |
| Outcome: | The proposed solver outperforms existing neural and symbolic solvers on two datasets. |
Recognition of Implicit Geographic Movement in Text (2020.lrec-1)
Copied to clipboard
| Challenge: | a growing field of research is analyzing the geographic movement of humans, animals, and other entities. |
| Approach: | They created a corpus of sentences labeled as describing geographic movement or not . they used hand labeling, crowd voting and machine learning to predict more labels . |
| Outcome: | a new method uses hand labeling, crowd voting and machine learning to predict more labels. |
GPS: Genetic Prompt Search for Efficient Few-Shot Learning (2022.emnlp-main)
Copied to clipboard
| Challenge: | Pretrained language models are often finetuned for downstream tasks, which has been shown to improve performance over non-pretrained models. |
| Approach: | They propose a genetic algorithm to automatically search for the best prompt for few-shot learning with pretrained language models by gradient-free algorithm. |
| Outcome: | Experiments on diverse datasets show that the proposed method outperforms manual prompts by 2.6 points. |
GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction. |
| Approach: | They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs. |
| Outcome: | The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%. |