| Challenge: | Existing approaches to generate graphs using pre-trained language models hinder their ability to generate them correctly. |
| Approach: | They propose to frame structured commonsense reasoning tasks as code generation tasks instead of serializing the output graph as a flat list of nodes and edges. |
| Outcome: | The proposed approach outperforms natural-language LMs in three natural language tasks even when the downstream task does not involve source code at all. |
Similar Papers
A Systematic Investigation of Commonsense Knowledge in Large Language Models (2022.emnlp-main)
Copied to clipboard
Xiang Lorraine Li, Adhiguna Kuncoro, Jordan Hoffmann, Cyprien de Masson d’Autume, Phil Blunsom, Aida Nematzadeh
| Challenge: | Recent large language models (LMs) have shown impressive performance on many NLP tasks under the zero-shot and few-shot setup. |
| Approach: | They conduct a systematic and rigorous zero-shot and few-shot commonsense evaluation of large pre-trained language models to better understand their ability to capture commonsensical knowledge. |
| Outcome: | The proposed model can exploit surface cues and annotation artefacts without task-specific supervision and is insufficient to achieve human-level commonsense performance. |
Few-Shot Semantic Parsing with Language Models Trained on Code (2022.naacl-main)
Copied to clipboard
| Challenge: | Large language models can perform semantic parsing with little training data, when prompted with in-context examples. |
| Approach: | They propose to map natural language to a controlled natural language-like representation . they find that OpenAI Codex performs better on such tasks than equivalent GPT-3 models . |
| Outcome: | The proposed model performs better on large parsing tasks than GPT-3 models on Overnight and SMCalFlow. |
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models pre-trained on massive corpora have shown impressive few-shot learning ability on many NLP tasks. |
| Approach: | They propose to recast structured output in the form of code instead of natural language and use generative LLMs of code to perform IE tasks. |
| Outcome: | The proposed method outperforms fine-tuning moderate-size pre-trained models and prompting NL-LLMs under few-shot settings. |
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2021.naacl-main)
Copied to clipboard
| Challenge: | Pretraining ever-larger language models on massive corpora requires enormous amounts of compute. |
| Approach: | They propose to convert textual inputs into cloze questions that contain a task description . they also exploit unlabeled data to improve their performance . |
| Outcome: | The proposed model outperforms GPT-3 with PET/iPET with cloze questions and unlabeled data. |
Do Language Models Perform Generalizable Commonsense Inference? (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent work has applied pretrained language models to populate commonsense knowledge graphs (CKGs) but there is a lack of understanding on their generalization to multiple CKGs, unseen relations, and novel entities. |
| Approach: | They analyze the ability of pretrained language models to perform generalizable commonsense inference in terms of knowledge capacity, transferability and induction. |
| Outcome: | The proposed models can adapt to different schemas defined by multiple CKGs but fail to generalize to new relations. |
Constrained Language Models Yield Few-Shot Semantic Parsers (2021.emnlp-main)
Copied to clipboard
Richard Shin, Christopher Lin, Sam Thomson, Charles Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jason Eisner, Benjamin Van Durme
| Challenge: | Large pretrained language models excel at generating natural language, but they are not efficient for task specific semantic parsing. |
| Approach: | They propose to use large pretrained language models as few-shot semantic parsers . they paraphrase inputs into a controlled sublanguage resembling English . |
| Outcome: | The proposed model can generate surprisingly accurate models on multiple tasks with minimal code and data. |
Making Pre-trained Language Models Better Few-shot Learners (2021.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that the GPT-3 model can perform few-shots on language understanding tasks with a natural-language prompt and a few task demonstrations. |
| Approach: | They propose a technique for fine-tuning language models using a few examples . they propose LM-BFF, which uses prompt-based fine-uning and a pipeline for automating prompt generation . |
| Outcome: | The proposed approach outperforms standard fine-tuning procedures on a range of NLP tasks. |
Few-Shot NLG with Pre-Trained Language Model (2020.acl-main)
Copied to clipboard
| Challenge: | Neural-based approaches to natural language generation are data-hungry and difficult to adopt in real-world applications. |
| Approach: | They propose a task of few-shot natural language generation from structured data or knowledge to generate coherent sentences from input data and language modeling to compose coherent sentences. |
| Outcome: | The proposed approach outperforms the strongest baseline approach by over 8.0 BLEU points improvement. |
Large Language Models are few(1)-shot Table Reasoners (2023.findings-eacl)
Copied to clipboard
| Challenge: | Recent literature has shown that large language models are excellent few-shot reasoners to solve text reasoning tasks. |
| Approach: | They evaluated LLMs on popular table QA and fact verification datasets like WikiTableQuestion, FetaQA, TabFact, and FEVEROUS and found they are competent at complex reasoning over table structures. |
| Outcome: | The proposed models are more competent at complex reasoning over table structures than tuned T5-large models. |
Few-shot Learning with Multilingual Generative Language Models (2022.emnlp-main)
Copied to clipboard
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li
| Challenge: | Large-scale generative language models such as GPT-3 are competitive few-shot learners. |
| Approach: | They train multilingual generative language models on a corpus covering a diverse set of languages and study their few- and zero-shot learning capabilities. |
| Outcome: | The proposed model outperforms GPT-3 on 171 out of 182 directions with 32 training examples and surpasses the official supervised baseline in 45 directions. |