Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Sil, Avirup"'
Autor:
Reddy, Revanth Gangi, Doo, JaeHyeok, Xu, Yifei, Sultan, Md Arafat, Swain, Deevya, Sil, Avirup, Ji, Heng
Large Language Models (LLMs) have significantly advanced the field of information retrieval, particularly for reranking. Listwise LLM rerankers have showcased superior performance and generalizability compared to existing supervised approaches. Howev
Externí odkaz:
http://arxiv.org/abs/2406.15657
Autor:
Xian, Jasper, Samuel, Saron, Khoubsirat, Faraz, Pradeep, Ronak, Sultan, Md Arafat, Florian, Radu, Roukos, Salim, Sil, Avirup, Potts, Christopher, Khattab, Omar
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels. The method depends on generating synthetic queries for documents using a language model (LM), and the key
Externí odkaz:
http://arxiv.org/abs/2406.11706
Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While
Externí odkaz:
http://arxiv.org/abs/2404.02103
Publikováno v:
Data Intelligence, Vol 1, Iss 2, Pp 99-120 (2019)
We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to variou
Externí odkaz:
https://doaj.org/article/3c09cf3d056e4f3796f50a523fc3bce5
We present a large-scale empirical study of how choices of configuration parameters affect performance in knowledge distillation (KD). An example of such a KD parameter is the measure of distance between the predictions of the teacher and the student
Externí odkaz:
http://arxiv.org/abs/2401.06356
Autor:
Tillmann, Christoph, Trivedi, Aashka, Rosenthal, Sara, Borse, Santosh, Zhang, Rong, Sil, Avirup, Bhattacharjee, Bishwaranjan
Publikováno v:
EMNLP 2023 Demo Track
Offensive language such as hate, abuse, and profanity (HAP) occurs in various content on the web. While previous work has mostly dealt with sentence level annotations, there have been a few recent attempts to identify offensive spans as well. We buil
Externí odkaz:
http://arxiv.org/abs/2312.11344
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Retrieval-Augmented Generation (RAG), an ad hoc appro
Externí odkaz:
http://arxiv.org/abs/2310.11511
Autor:
Reddy, Revanth Gangi, Dasigi, Pradeep, Sultan, Md Arafat, Cohan, Arman, Sil, Avirup, Ji, Heng, Hajishirzi, Hannaneh
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-encoder network initially retrieves a pre-defined number of candidates (e.g., K=100), which are then reranked by a more powerful cross-encoder model. While the
Externí odkaz:
http://arxiv.org/abs/2305.11744
Autor:
Saad-Falcon, Jon, Khattab, Omar, Santhanam, Keshav, Florian, Radu, Franz, Martin, Roukos, Salim, Sil, Avirup, Sultan, Md Arafat, Potts, Christopher
Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we deve
Externí odkaz:
http://arxiv.org/abs/2303.00807
Autor:
Sil, Avirup, Sen, Jaydeep, Iyer, Bhavani, Franz, Martin, Fadnis, Kshitij, Bornea, Mihaela, Rosenthal, Sara, McCarley, Scott, Zhang, Rong, Kumar, Vishwajeet, Li, Yulong, Sultan, Md Arafat, Bhat, Riyaz, Florian, Radu, Roukos, Salim
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrie
Externí odkaz:
http://arxiv.org/abs/2301.09715