28 citations · 129 across the 32 of their papers we have counts for
35 papers
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question Answering
Nathaniel Weir, Bhavana Dalvi Mishra, Orion Weller +6
Recent reasoning methods (e.g., chain-of-thought, entailment reasoning) help users understand how language models (LMs) answer a single question, but they do little to reveal the L…
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Benjamin Warner, Antoine Chaffin, Benjamin Clavié +11
Encoder-only transformer models such as BERT offer a great performance-size tradeoff for retrieval and classification tasks with respect to larger decoder-only models. Despite bein…
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models
Orion Weller, Benjamin Van Durme, Dawn Lawrie +3
Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we pre…
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
Abe Bohan Hou, Orion Weller, Guanghui Qin +5
Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writin…
On the Evaluation of Machine-Generated Reports
James Mayfield, Eugene Yang, Dawn Lawrie +10
Large Language Models (LLMs) have enabled new ways to satisfy information needs. Although great strides have been made in applying them to settings like document ranking and short-…
Learning to Reason via Program Generation, Emulation, and Search
Nathaniel Weir, Muhammad Khalifa, Linlu Qiu +2
Program synthesis with language models (LMs) has unlocked a large set of reasoning abilities; code-tuned LMs have proven adept at generating programs that solve a wide variety of a…
SELF-[IN]CORRECT: LLMs Struggle with Discriminating Self-Generated Responses
Dongwei Jiang, Jingyu Zhang, Orion Weller +3
Can LLMs consistently improve their previous outputs for better results? For this to be true, LLMs would need to be better at discriminating among previously-generated alternatives…
FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
Orion Weller, Benjamin Chang, Sean MacAvaney +5
Modern Language Models (LMs) are capable of following long and complex instructions that enable a large and diverse set of user requests. While Information Retrieval (IR) models us…
Dated Data: Tracing Knowledge Cutoffs in Large Language Models
Jeffrey Cheng, Marc Marone, Orion Weller +3
Released Large Language Models (LLMs) are often paired with a claimed knowledge cutoff date, or the dates at which training data was gathered. Such information is crucial for appli…
Enhancing Systematic Decompositional Natural Language Inference Using Informal Logic
Nathaniel Weir, Kate Sanders, Orion Weller +8
Recent language models enable new opportunities for structured reasoning with text, such as the construction of intuitive, proof-like textual entailment trees without relying on br…
When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
Orion Weller, Kyle Lo, David Wadden +4
Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universal…
MegaWika: Millions of reports and their sources across 50 diverse languages
Samuel Barham, Orion Weller, Michelle Yuan +9
To foster the development of new models for collaborative AI-assisted report generation, we introduce MegaWika, consisting of 13 million Wikipedia articles in 50 diverse languages,…
"According to ...": Prompting Language Models Improves Quoting from Pre-Training Data
Orion Weller, Marc Marone, Nathaniel Weir +3
Large Language Models (LLMs) may hallucinate and generate fake information, despite pre-training on factual data. Inspired by the journalistic device of "according to sources", we…