research-papers

Understanding prompt engineering may not require rethinking generalization

Original Paper: https://arxiv.org/abs/2310.03957 By: Victor Akinwande, Yiding Jiang, Dylan Sam, J. Zico Kolter Abstract: Zero-shot learning in prompted vision-language models, the practice of crafting prompts to build classifiers without an explicit training process, has achieved impressive performance in many settings. This success presents a seemingly

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Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models

Original Paper: https://arxiv.org/abs/2310.03123 By: Zihao Lin, Yan Sun, Yifan Shi, Xueqian Wang, Lifu Huang, Li Shen, Dacheng Tao Abstract: With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Although

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Large Language Models as Analogical Reasoners

Original Paper: https://arxiv.org/abs/2310.01714 By: Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H. Chi, Denny Zhou Abstract: Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process. In this

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Prompt-tuning latent diffusion models for inverse problems

Original Paper: https://arxiv.org/abs/2310.01110 By: Hyungjin Chung, Jong Chul Ye, Peyman Milanfar, Mauricio Delbracio Abstract: We propose a new method for solving imaging inverse problems using text-to-image latent diffusion models as general priors. Existing methods using latent diffusion models for inverse problems typically rely on simple

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SPELL: Semantic Prompt Evolution based on a LLM

Original Paper: https://arxiv.org/abs/2310.01260 By: Yujian Betterest Li, Kai Wu Abstract: Prompt engineering is a new paradigm for enhancing the performance of trained neural network models. For optimizing text-style prompts, existing methods usually individually operate small portions of a text step by step, which either breaks

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AutoHall: Automated Hallucination Dataset Generation for Large Language Models

Original Paper: https://arxiv.org/abs/2310.00259 By: Zouying Cao, Yifei Yang, Hai Zhao Abstract: While LLMs have garnered widespread applications across various domains due to their powerful language understanding and generation capabilities, the detection of non-factual or hallucinatory content generated by LLMs remains scarce. Currently, one significant challenge

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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Original Paper: https://arxiv.org/abs/2309.16797 By: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel Abstract: Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper,

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Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

Original Paper: https://arxiv.org/abs/2308.10379 By: Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin Abstract: Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to an external modus operandi involving halting, modifying, and then resuming the generation process to boost Large Language

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Chain-of-Verification Reduces Hallucination in Large Language Models

Original Paper: https://arxiv.org/abs/2309.11495 By: Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston Abstract: Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate

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Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

Original Paper: https://arxiv.org/abs/2309.11064 By: Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Amit Sheth, Amitava Das Abstract: As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While

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Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers

Original Paper: https://arxiv.org/abs/2309.08532 By: Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang Abstract: Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To

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Temporal evolution of depolarization and magnetic field of FRB 20201124A

Original Paper: https://arxiv.org/abs/2309.06653 By: Wan-Jin Lu, Zhen-Yin Zhao, F. Y. Wang, Z. G. Dai Abstract: Fast radio bursts (FRBs) are energetic millisecond phenomena in radio band. Polarimetric studies of repeating FRBs indicate that many of these sources occupy extreme and complex magneto-ionized environments. Recently, a