Language Models

By Jordan Boyd-Graber, Philipp Koehn. 2009

1,167 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Large Language Models

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohit Bansal
2. Luke S. Zettlemoyer
3. Hannaneh Hajishirzi
4. Hinrich Schütze
5. Noah A. Smith
6. Yejin Choi
7. Percy Liang
8. Timo Schick

In Information Systems

1. Hannaneh Hajishirzi
2. Mohit Bansal
3. Sewon Min
4. Luke S. Zettlemoyer
5. Timo Schick
6. Hinrich Schütze
7. Eric Wallace
8. Noah A. Smith

In Large Language Models

1. Hinrich Schütze
2. Luke S. Zettlemoyer
3. Jie Tang
4. Timo Schick
5. Mohit Bansal
6. Xiao Liu
7. Hannaneh Hajishirzi
8. Ming Ding

Citations in the corpus

• Book Review: Statistical Language Models for Information Retrieval by ChengXiang Zhai (2008, neutral): “06] : improves over the basic LM more significantly • Cluster-based query likelihood: Similar to the translation model, but “translate” the whole document to the query through a set of clusters [Kurland & Lee 04] ( | , ) ( | ) ( | ) C Clusters p Q D R p Q C p C D ∈ = ∑ How likely doc D belongs to cl”
• VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (2021, background): “Several papers accordingly propose new architectures and objectives to model language, such as ELMo [35], BERT [8], RoBERTa [31], GPT series [4,38,39], and encoder-decoder versions such as BART [25] and T5 [40].”
• Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning (2020, background): “More recently, analysis of relational knowledge contained in pre-trained BERT models has been the subject of different studies (Petroni et al., 2019; Poerner et al., 2019).”
• Benchmarking Cognitive Biases in Large Language Models as Evaluators (2023, background): “Large language models (LLMs) (Brown et al., 2020; Ouyang et al., 2022) adapted to follow various kinds of instructions have been popularly utilized for several natural language tasks.”
• RLTF: Reinforcement Learning from Unit Test Feedback (2023, background): “An AI model that can generate programs based on human requirements could significantly transform programming tools and workflows (Zan et al., 2023).”
• SILO Language Models: Isolating Legal Risk In a Nonparametric Datastore (2023, positive): “Our work is closely related to recent studies on modular LMs, which have specialized parameters (or experts ) trained on different domains (Gururangan et al., 2022; Li et al., 2022; Gururangan et al., 2023), languages (Pfeiffer et al., 2020; 2022), or tasks (Chen et al., 2022b; Jang et al., 2023a).”