activity
20192025
most citedSmarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

28 citations · 129 across the 32 of their papers we have counts for

collaborators
Showing 2025Show all

11 papers · 1 filter

cs.IR2025

HLTCOE at TREC 2024 NeuCLIR Track

Eugene Yang, Dawn Lawrie, Orion Weller +1

The HLTCOE team applied PLAID, an mT5 reranker, GPT-4 reranker, score fusion, and document translation to the TREC 2024 NeuCLIR track. For PLAID we included a variety of models and…

cs.CL2025★ 4 cited

mmBERT: A Modern Multilingual Encoder with Annealed Language Learning

Marc Marone, Orion Weller, William Fleshman +3

Encoder-only languages models are frequently used for a variety of standard machine learning tasks, including classification and retrieval. However, there has been a lack of recent…

cs.IR2025

Auto-ARGUE: LLM-Based Report Generation Evaluation

William Walden, Marc Mason, Orion Weller +10

Generation of citation-backed reports is a primary use case for retrieval-augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, tools…

cs.IR2025★ 6 cited

On the Theoretical Limitations of Embedding-Based Retrieval

Orion Weller, Michael Boratko, Iftekhar Naim +1

Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, an…

cs.CL2025★ 1 cited

Seq vs Seq: An Open Suite of Paired Encoders and Decoders

Orion Weller, Kathryn Ricci, Marc Marone +3

The large language model (LLM) community focuses almost exclusively on decoder-only language models, since they are easier to use for text generation. However, a large subset of th…

cs.IR2025

HLTCOE at LiveRAG: GPT-Researcher using ColBERT retrieval

Kevin Duh, Eugene Yang, Orion Weller +2

The HLTCOE LiveRAG submission utilized the GPT-researcher framework for researching the context of the question, filtering the returned results, and generating the final answer. Th…

cs.IR2025★ 1 cited

Rank-K: Test-Time Reasoning for Listwise Reranking

Eugene Yang, Andrew Yates, Kathryn Ricci +4

Retrieve-and-rerank is a popular retrieval pipeline because of its ability to make slow but effective rerankers efficient enough at query time by reducing the number of comparisons…

cs.CL2025

Beyond RAG: Task-Aware KV Cache Compression for Comprehensive Knowledge Reasoning

Giulio Corallo, Orion Weller, Fabio Petroni +1

Incorporating external knowledge in large language models (LLMs) enhances their utility across diverse applications, but existing methods have trade-offs. Retrieval-Augmented Gener…

cs.IR2025★ 1 cited

Rank1: Test-Time Compute for Reranking in Information Retrieval

Orion Weller, Kathryn Ricci, Eugene Yang +3

We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language mo…

cs.IR2025★ 1 cited

mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval

Orion Weller, Benjamin Chang, Eugene Yang +7

Retrieval systems generally focus on web-style queries that are short and underspecified. However, advances in language models have facilitated the nascent rise of retrieval models…

cs.LG2025★ 23 cited

Humanity's Last Exam

Long Phan, Alice Gatti, Ziwen Han +1168

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…