Athina AI Agent

research-papers

LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression

Original Paper: https://arxiv.org/abs/2310.06839 By: Huiqiang Jiang, Qianhui Wu, Xufang Luo, Dongsheng Li, Chin-Yew Lin, Yuqing Yang, Lili Qiu Abstract: In long context scenarios, large language models (LLMs) face three main challenges: higher computational/financial cost, longer latency, and inferior performance. Some studies reveal that the

research-papers

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

research-papers

To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Original Paper: https://arxiv.org/abs/2409.12183 By: Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, Greg Durrett Abstract: Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But

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A Comprehensive Evaluation of Quantized Instruction-Tuned Large Language Models: An Experimental Analysis up to 405B

Original Paper: https://arxiv.org/abs/2409.11055 By: Jemin Lee, Sihyeong Park, Jinse Kwon, Jihun Oh, Yongin Kwon Abstract: Prior research works have evaluated quantized LLMs using limited metrics such as perplexity or a few basic knowledge tasks and old datasets. Additionally, recent large-scale models such as Llama 3.

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Large Language Monkeys: Scaling Inference Compute with Repeated Sampling

Original Paper: https://arxiv.org/abs/2407.21787 By: Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini Abstract: Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the

research-papers

What is the Role of Small Models in the LLM Era: A Survey

Original Paper: https://arxiv.org/abs/2409.06857 By: Lihu Chen, Gaël Varoquaux Abstract: Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponentially higher

research-papers

Agent Workflow Memory

Original Paper: https://arxiv.org/abs/2409.07429 By: Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, Graham Neubig Abstract: Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contrast, humans can flexibly

research-papers

Achieving Peak Performance for Large Language Models: A Systematic Review

Published Date: 7 Sep 2024 Original Paper: https://arxiv.org/abs/2409.04833 By: Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész Abstract: In recent years, large language models (LLMs) have achieved remarkable success in natural language processing (NLP). LLMs require an extreme amount of parameters to attain high performance.

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Strategic Chain-of-Thought: Guiding Accurate Reasoning in LLMs through Strategy Elicitation

Original Paper: https://arxiv.org/abs/2409.03271v1 By: Yu Wang, Shiwan Zhao, Zhihu Wang, Heyuan Huang, Ming Fan, Yubo Zhang, Zhixing Wang, Haijun Wang, Ting Liu Abstract: The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs). However, despite

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Large Language Model-Based Agents for Software Engineering: A Survey

Original Paper: https://arxiv.org/abs/2409.02977 By: Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, Yiling Lou Abstract: The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents

research-papers

LLM Pruning and Distillation in Practice: The Minitron Approach

Original Paper: https://arxiv.org/pdf/2408.11796 By: Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov Abstract We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B

research-papers

Controllable Text Generation for Large Language Models: A Survey

Original Paper: https://arxiv.org/abs/2408.12599 By: Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, Zhiyu Li Abstract: In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world