Athina AI

research-papers

EfficientRAG: Efficient Retriever for Multi-Hop Question Answering

Original Paper: https://arxiv.org/abs/2408.04259 By: Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang Abstract Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries. While iterative retrieval methods improve performance by

research-papers

Know Your Limits: A Survey of Abstention in Large Language Models

Original Paper: https://arxiv.org/abs/2407.18418 By: Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, Lucy Lu Wang Abstract: Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in

research-papers

Transformer Explainer: Interactive Learning of Text-Generative Models

Original Paper: https://arxiv.org/abs/2408.04619 By: Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Zijie J. Wang, Seongmin Lee, Benjamin Hoover, Duen Horng Chau Abstract: Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool

research-papers

Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

Original Paper: https://arxiv.org/abs/2408.04187 By: Junde Wu, Jiayuan Zhu, Yunli Qi Abstract: We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities and generating evidence-based results, thereby improving safety and

research-papers

Conversational Prompt Engineering

Original Paper: https://arxiv.org/abs/2408.04560 By: Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim Abstract: Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often

research-papers

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Original Paper: https://arxiv.org/abs/2408.03314 By: Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar Abstract: Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study

research-papers

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

Original Paper: https://arxiv.org/abs/2408.02545 By: Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat, Peter Izsak Abstract: Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, evaluating these systems presents significant challenges, necessitating assessment of both retrieval accuracy

research-papers

From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future

Original Paper: https://arxiv.org/abs/2408.02479 By: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Abstract: With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success

research-papers

From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future

Original Paper: https://arxiv.org/abs/2408.02479 By: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Abstract: With the rise of large language models (LLMs), researchers are increasingly exploring their applications in var ious vertical domains, such as software engineering. LLMs have achieved remarkable success

research-papers

Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Original Paper: https://arxiv.org/abs/2408.02442 By: Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen Abstract: Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large

research-papers

ShieldGemma: Generative AI Content Moderation Based on Gemma

Original Paper: https://arxiv.org/abs/2407.21772 By: Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, Oscar Wahltinez Abstract: We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2. These

research-papers

Improving Retrieval Augmented Language Model with Self-Reasoning

Original Paper: https://arxiv.org/abs/2407.19813 By: Yuan Xia, Jingbo Zhou, Zhenhui Shi, Jun Chen, Haifeng Huang Abstract: The Retrieval-Augmented Language Model (RALM) has shown remarkable performance on knowledge-intensive tasks by incorporating external knowledge during inference, which mitigates the factual hallucinations inherited in large language models (LLMs). Despite