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Found 8 papers, 3 papers with code
Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
Also, we show that compared to natural language, pseudocode can better guide the reasoning of LMs, even though they are trained to follow natural language instructions.
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering
Recent studies have investigated utilizing Knowledge Graphs (KGs) to enhance Quesetion Answering (QA) performance of Large Language Models (LLMs), yet structured KG verbalization remains challengin.
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models
The results suggest that VerifiNER can successfully verify errors from existing models as a model-agnostic approach.
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Hence, the focus of our work is to leverage open-source code LLMs to generate helpful feedback with correct guidance for code editing.
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning.
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.
TUTORING: Instruction-Grounded Conversational Agent for Language Learners
In this paper, we propose Tutoring bot, a generative chatbot trained on a large scale of tutor-student conversations for English-language learning.
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
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