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https://arxiv.org/abs/2502.00290 Estimating LLM Uncertainty with LogitsIn recent years, Large Language Models (LLMs) have seen remarkable advancements and have been extensively integrated across various fields. Despite their progress, LLMs are prone to hallucinations, producing responses that may not be dependable if the modearxiv.org 지금 진행 중인 연구에 관련이 있는 논문입니다.Uncertainty를 측정하기 위해 우린 이렇게 했다! 라는 ..

https://arxiv.org/abs/2501.09749 Enhancing Lexicon-Based Text Embeddings with Large Language ModelsRecent large language models (LLMs) have demonstrated exceptional performance on general-purpose text embedding tasks. While dense embeddings have dominated related research, we introduce the first Lexicon-based EmbeddiNgS (LENS) leveraging LLMs that achiearxiv.org기존 Dense embedding의 문제점을 말합니다.그리고 ..

2025.03.17 - [인공지능/논문 리뷰 or 진행] - Embedding + Generation Model 사전 논문 조사5 - 데이터 셋 및 평가 데이터 정리 Embedding + Generation Model 사전 논문 조사5 - 데이터 셋 및 평가 데이터 정리2024.12.23 - [인공지능/논문 리뷰 or 진행] - ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰 ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰https://arxiv.org/abs/2401.10225 ChatQA: Surpassing GPT-4 onyoonschallenge.tistory.com여기서 ..

2024.12.23 - [인공지능/논문 리뷰 or 진행] - ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰 ChatQA: Surpassing GPT-4 on Conversational QA and RAG - 논문 리뷰https://arxiv.org/abs/2401.10225 ChatQA: Surpassing GPT-4 on Conversational QA and RAGIn this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (Q..

https://arxiv.org/abs/2411.16724 Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via AttentHallucinations in Large Vision-Language Models (LVLMs) significantly undermine their reliability, motivating researchers to explore the causes of hallucination. However, most studies primarily focus on the language aspect rather than the..

https://arxiv.org/abs/2503.09573 Block Diffusion: Interpolating Between Autoregressive and Diffusion Language ModelsDiffusion language models offer unique benefits over autoregressive models due to their potential for parallelized generation and controllability, yet they lag in likelihood modeling and are limited to fixed-length generation. In this work, we introduce aarxiv.org시간 나는대로 천천히 리뷰 작성 ..

https://arxiv.org/abs/2405.19333 Multi-Modal Generative Embedding ModelMost multi-modal tasks can be formulated into problems of either generation or embedding. Existing models usually tackle these two types of problems by decoupling language modules into a text decoder for generation, and a text encoder for embedding. To exparxiv.org이 논문은 Multi-Modal이기도 하고, 이미지는 일단 나중에 생각할 거기 때문에 적당히 보고 넘어가겠습니다..

https://aclanthology.org/2025.coling-main.94/ Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language ModelsChengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao. Proceedings of the 31st International Conference on Computational Linguistics. 2025.aclanthology.org그런데 이 논문은 Embedding + Gen은 아닌 잘 못 찾은 논문이라 ㅎㅎ... 그래도 재밌어서 쭉 읽어 봤습니다. 이 논문은 검색을 언제 진행할까가 주요..

https://aclanthology.org/2025.coling-main.549/ GEAR: A Simple GENERATE, EMBED, AVERAGE AND RANK Approach for Unsupervised Reverse DictionaryFatemah Yousef Almeman, Luis Espinosa Anke. Proceedings of the 31st International Conference on Computational Linguistics. 2025.aclanthology.org저는 RD라는 Task 자체를 처음 봤습니다.그래서 뭔가 했더니 설명을 주면 반대로 단어를 맞추는 것이네요신서유기가 생각나는 Task...제가 크게 관심있는 분야는 아니라 이런게 있다 정도만 보고 넘어갔습..

https://arxiv.org/abs/2010.03768 ALFWorld: Aligning Text and Embodied Environments for Interactive LearningGiven a simple request like Put a washed apple in the kitchen fridge, humans can reason in purely abstract terms by imagining action sequences and scoring their likelihood of success, prototypicality, and efficiency, all without moving a muscle. Once we searxiv.org     사람은 단순한 요청이 주어지면 Acti..