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Found 19 papers, 6 papers with code
Prompt Engineering a Prompt Engineer
In addition, inspired by common optimization concepts such as batch size, step size and momentum, we introduce their verbalized counterparts to the meta-prompt and investigate their effects.
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions
Recent progress in Large Language Models (LLMs) has produced models that exhibit remarkable performance across a variety of NLP tasks.
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization
SCQ works like a differentiable convex optimization (DCO) layer: in the forward pass, we solve for the optimal convex combination of codebook vectors that quantize the inputs.
In-Context Demonstration Selection with Cross Entropy Difference
Our method is based on the observation that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example by a language model that was finetuned on that demonstration.
i-Code Studio: A Configurable and Composable Framework for Integrative AI
Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
Automatic Prompt Optimization with "Gradient Descent" and Beam Search
Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort.
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning
Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions.
FAST: Improving Controllability for Text Generation with Feedback Aware Self-Training
Controllable text generation systems often leverage control codes to direct various properties of the output like style and length.
Automatic Rule Induction for Interpretable Semi-Supervised Learning
Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
A fundamental goal of scientific research is to learn about causal relationships.
Repairing Pronouns in Translation with BERT-Based Post-Editing
We investigate the severity of this pronoun issue, showing that (1) in some domains, pronoun choice can account for more than half of a NMT systems' errors, and (2) pronouns have a disproportionately large impact on perceived translation quality.
Causal Effects of Linguistic Properties
Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.
Automatically Neutralizing Subjective Bias in Text
To address this issue, we introduce a novel testbed for natural language generation: automatically bringing inappropriately subjective text into a neutral point of view ("neutralizing" biased text).
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style
How much does {``}free shipping!
Deconfounded Lexicon Induction for Interpretable Social Science
The first uses a bifurcated architecture to separate the explanatory power of the text and confounds.
JESC: Japanese-English Subtitle Corpus
In this paper we describe the Japanese-English Subtitle Corpus (JESC).
