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20212026
most citedFew-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

294 citations · 307 across the 17 of their papers we have counts for

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Showing 2024 · cs.CLShow all

5 papers · 2 filters

cs.CL2024

FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists

Tenghao Huang, Donghee Lee, John Sweeney +5

Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typical…

cs.CL2024

Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation

Dongwon Jung, Qin Liu, Tenghao Huang +2

Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it of…

cs.CL2024★ 5 cited

Are Large Language Models Capable of Generating Human-Level Narratives?

Yufei Tian, Tenghao Huang, Miri Liu +5

This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narr…

cs.CL2024

Red Teaming Language Models for Processing Contradictory Dialogues

Xiaofei Wen, Bangzheng Li, Tenghao Huang +1

Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing tas…

cs.CL2024

Planning and Editing What You Retrieve for Enhanced Tool Learning

Tenghao Huang, Dongwon Jung, Muhao Chen

Recent advancements in integrating external tools with Large Language Models (LLMs) have opened new frontiers, with applications in mathematical reasoning, code generators, and sma…