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Found 15 papers, 10 papers with code
Do ``Undocumented Workers'' == ``Illegal Aliens''? Differentiating Denotation and Connotation in Vector Spaces
In politics, neologisms are frequently invented for partisan objectives.
In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax
In-context learning (ICL) is now a common method for supervising large language models (LLMs): given labeled examples in the input context, the LLM learns to perform the task without weight updates.
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.
Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs
To study such interactions, we propose Simfluence, a new paradigm for TDA where the goal is not to produce a single influence score per example, but instead a training run simulator: the user asks, ``If my model had trained on example $z_1$, then $z_2$, ..., then $z_n$, how would it behave on $z_{test}$?
Larger language models do in-context learning differently
We next study semantically-unrelated label ICL (SUL-ICL), in which labels are semantically unrelated to their inputs (e. g., foo/bar instead of negative/positive), thereby forcing language models to learn the input-label mappings shown in in-context exemplars in order to perform the task.
The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
We study the design decisions of publicly available instruction tuning methods, and break down the development of Flan 2022 (Chung et al., 2022).
Are Language Models Worse than Humans at Following Prompts? It's Complicated
However, recent work finds that models can perform surprisingly well when given intentionally irrelevant or misleading prompts.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Crosslingual Generalization through Multitask Finetuning
We find finetuning large multilingual language models on English tasks with English prompts allows for task generalization to non-English languages that appear only in the pretraining corpus.
Ranked #1 on
Zero-Shot Learning
on Story Cloze
Scaling Instruction-Finetuned Language Models
We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation).
Ranked #1 on
Coreference Resolution
on WSC
Cross-Lingual Question Answering
Multi-task Language Understanding
+1
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models
State-of-the-art neural language models can now be used to solve ad-hoc language tasks through zero-shot prompting without the need for supervised training.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
PromptSource is a system for creating, sharing, and using natural language prompts.
Multitask Prompted Training Enables Zero-Shot Task Generalization
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020).
Do Prompt-Based Models Really Understand the Meaning of their Prompts?
We find that models learn just as fast with many prompts that are intentionally irrelevant or even pathologically misleading as they do with instructively "good" prompts.
Are "Undocumented Workers" the Same as "Illegal Aliens"? Disentangling Denotation and Connotation in Vector Spaces
In politics, neologisms are frequently invented for partisan objectives.
