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Found 29 papers, 21 papers with code
Drowning in Documents: Consequences of Scaling Reranker Inference
Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems.
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
The hallucinations of large language models (LLMs) are increasingly mitigated by allowing LLMs to search for information and to ground their answers in real sources.
PAPILLON: PrivAcy Preservation from Internet-based and Local Language MOdel ENsembles
While open-source models, hosted locally on the user's machine, alleviate some concerns, models that users can host locally are often less capable than proprietary frontier models.
Fine-Tuning and Prompt Optimization: Two Great Steps that Work Better Together
Natural Language Processing (NLP) systems are increasingly taking the form of sophisticated modular pipelines, e. g., Retrieval Augmented Generation (RAG), where each module may involve a distinct Language Model (LM) and an associated prompt template.
Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
To make this tractable, we factorize our problem into optimizing the free-form instructions and few-shot demonstrations of every module and introduce several strategies to craft task-grounded instructions and navigate credit assignment across modules.
Prompts as Auto-Optimized Training Hyperparameters: Training Best-in-Class IR Models from Scratch with 10 Gold Labels
We develop a method for training small-scale (under 100M parameter) neural information retrieval models with as few as 10 gold relevance labels.
Backtracing: Retrieving the Cause of the Query
While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators -- such as lecturers who want to improve their content -- identify segments that _caused_ a user to ask those questions.
Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models
We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages.
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
Building Efficient and Effective OpenQA Systems for Low-Resource Languages
Our results show that SQuAD-TR makes OpenQA feasible for Turkish, which we hope encourages researchers to build OpenQA systems in other low-resource languages.
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
We integrate our constructs into the recent DSPy programming model for LMs, and present new strategies that allow DSPy to compile programs with LM Assertions into more reliable and accurate systems.
Image and Data Mining in Reticular Chemistry Using GPT-4V
The integration of artificial intelligence into scientific research has reached a new pinnacle with GPT-4V, a large language model featuring enhanced vision capabilities, accessible through ChatGPT or an API.
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems
Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand annotations for input queries, passages to retrieve, and responses to generate.
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
The ML community is rapidly exploring techniques for prompting language models (LMs) and for stacking them into pipelines that solve complex tasks.
Resources and Evaluations for Multi-Distribution Dense Information Retrieval
We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution.
UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers
Many information retrieval tasks require large labeled datasets for fine-tuning.
Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP
Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).
Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
PLAID: An Efficient Engine for Late Interaction Retrieval
PLAID uses centroid interaction as well as centroid pruning, a mechanism for sparsifying the bag of centroids, within a highly-optimized engine to reduce late interaction search latency by up to 7$\times$ on a GPU and 45$\times$ on a CPU against vanilla ColBERTv2, while continuing to deliver state-of-the-art retrieval quality.
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches.
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks.
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Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Many text generation systems benefit from using a retriever to retrieve passages from a textual knowledge corpus (e. g., Wikipedia) which are then provided as additional context to the generator.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Learning Passage Impacts for Inverted Indexes
Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT.
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval
Multi-hop reasoning (i. e., reasoning across two or more documents) is a key ingredient for NLP models that leverage large corpora to exhibit broad knowledge.
Relevance-guided Supervision for OpenQA with ColBERT
In much recent work, the retriever is a learned component that uses coarse-grained vector representations of questions and passages.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
