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Found 10 papers, 7 papers with code
Prompt Optimization via Adversarial In-Context Learning
We propose a new method, Adversarial In-Context Learning (adv-ICL), to optimize prompt for in-context learning (ICL) by employing one LLM as a generator, another as a discriminator, and a third as a prompt modifier.
InstructCoder: Instruction Tuning Large Language Models for Code Editing
In this work, we explore the use of Large Language Models (LLMs) to edit code based on user instructions.
ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning
We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios.
Automatic Model Selection with Large Language Models for Reasoning
Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two distinct reasoning methods, each with its own strengths.
Ranked #1 on
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CodeInstruct: Empowering Language Models to Edit Code
In this work, we explore the use of large language models (LLMs) to edit code based on user instructions, covering a broad range of implicit tasks such as comment insertion, code optimization, and code refactoring.
Self-Evaluation Guided Beam Search for Reasoning
Stochastic beam search balances exploitation and exploration of the search space with temperature-controlled randomness.
CANVASEMB: Learning Layout Representation with Large-scale Pre-training for Graphic Design
Layout representation, which models visual elements in a canvas and their inter-relations, plays a crucial role in graphic design intelligence.
Exploring Question-Specific Rewards for Generating Deep Questions
Recent question generation (QG) approaches often utilize the sequence-to-sequence framework (Seq2Seq) to optimize the log-likelihood of ground-truth questions using teacher forcing.
Semantic Graphs for Generating Deep Questions
This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage.
A Sketch-Based System for Semantic Parsing
The sketch is a high-level structure of the logical form exclusive of low-level details such as entities and predicates.
